Multiple Loss Ratio Search
draft-ietf-bmwg-mlrsearch-15
| Document | Type | Active Internet-Draft (bmwg WG) | |
|---|---|---|---|
| Authors | Maciek Konstantynowicz , Vratko Polák | ||
| Last updated | 2025-11-18 (Latest revision 2025-11-04) | ||
| Replaces | draft-vpolak-mkonstan-bmwg-mlrsearch | ||
| RFC stream | Internet Engineering Task Force (IETF) | ||
| Intended RFC status | Informational | ||
| Formats | |||
| Reviews |
INTDIR Telechat review
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Ready w/nits
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| Additional resources |
GitHub Repository
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| Stream | WG state | Submitted to IESG for Publication | |
| Associated WG milestone |
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| Document shepherd | Giuseppe Fioccola | ||
| Shepherd write-up | Show Last changed 2025-04-08 | ||
| IESG | IESG state | RFC Ed Queue | |
| Action Holders |
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| Consensus boilerplate | Yes | ||
| Telechat date | (None) | ||
| Responsible AD | Mohamed Boucadair | ||
| Send notices to | giuseppe.fioccola@huawei.com | ||
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| RFC Editor | RFC Editor state | EDIT | |
| Details |
draft-ietf-bmwg-mlrsearch-15
Benchmarking Working Group M. Konstantynowicz
Internet-Draft V. Polak
Intended status: Informational Cisco Systems
Expires: 8 May 2026 4 November 2025
Multiple Loss Ratio Search
draft-ietf-bmwg-mlrsearch-15
Abstract
This document describes an alternative to "Benchmarking Methodology
for Network Interconnect Devices" (RFC 2544) throughput by defining a
new methodology called Multiple Loss Ratio search (MLRsearch).
MLRsearch aims to minimize search duration, support multiple loss
ratio searches, and improve result repeatability and comparability.
MLRsearch is motivated by the pressing need to address the challenges
of evaluating and testing the various data plane solutions,
especially in software-based networking systems based on Commercial
Off-the-Shelf (COTS) CPU hardware vs purpose-built ASIC / NPU / FPGA
hardware.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
working documents as Internet-Drafts. The list of current Internet-
Drafts is at https://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on 8 May 2026.
Copyright Notice
Copyright (c) 2025 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents (https://trustee.ietf.org/
license-info) in effect on the date of publication of this document.
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Please review these documents carefully, as they describe your rights
and restrictions with respect to this document. Code Components
extracted from this document must include Revised BSD License text as
described in Section 4.e of the Trust Legal Provisions and are
provided without warranty as described in the Revised BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 4
1.1. Purpose . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2. Positioning within BMWG Methodologies . . . . . . . . . . 6
2. Overview of RFC 2544 Problems . . . . . . . . . . . . . . . . 7
2.1. Binary Search . . . . . . . . . . . . . . . . . . . . . . 7
2.2. Long Search Duration . . . . . . . . . . . . . . . . . . 8
2.3. DUT in SUT . . . . . . . . . . . . . . . . . . . . . . . 9
2.4. Repeatability and Comparability . . . . . . . . . . . . . 11
2.5. Throughput with Non-Zero Loss . . . . . . . . . . . . . . 12
2.6. Inconsistent Trial Results . . . . . . . . . . . . . . . 13
3. Requirements Language . . . . . . . . . . . . . . . . . . . . 14
4. MLRsearch Specification . . . . . . . . . . . . . . . . . . . 14
4.1. Scope . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.1.1. Relationship to RFC 2544 . . . . . . . . . . . . . . 15
4.1.2. Applicability of Other Specifications . . . . . . . . 16
4.1.3. Out of Scope . . . . . . . . . . . . . . . . . . . . 16
4.2. Architecture Overview . . . . . . . . . . . . . . . . . . 16
4.2.1. Test Report . . . . . . . . . . . . . . . . . . . . . 18
4.2.2. Behavior Correctness . . . . . . . . . . . . . . . . 18
4.3. Quantities . . . . . . . . . . . . . . . . . . . . . . . 18
4.3.1. Current and Final Values . . . . . . . . . . . . . . 19
4.4. Existing Terms . . . . . . . . . . . . . . . . . . . . . 19
4.4.1. SUT . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.4.2. DUT . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.4.3. Trial . . . . . . . . . . . . . . . . . . . . . . . . 20
4.5. Trial Terms . . . . . . . . . . . . . . . . . . . . . . . 22
4.5.1. Trial Duration . . . . . . . . . . . . . . . . . . . 22
4.5.2. Trial Load . . . . . . . . . . . . . . . . . . . . . 22
4.5.3. Trial Input . . . . . . . . . . . . . . . . . . . . . 24
4.5.4. Traffic Profile . . . . . . . . . . . . . . . . . . . 24
4.5.5. Trial Forwarding Ratio . . . . . . . . . . . . . . . 26
4.5.6. Trial Loss Ratio . . . . . . . . . . . . . . . . . . 27
4.5.7. Trial Forwarding Rate . . . . . . . . . . . . . . . . 27
4.5.8. Trial Effective Duration . . . . . . . . . . . . . . 28
4.5.9. Trial Output . . . . . . . . . . . . . . . . . . . . 28
4.5.10. Trial Result . . . . . . . . . . . . . . . . . . . . 29
4.6. Goal Terms . . . . . . . . . . . . . . . . . . . . . . . 29
4.6.1. Goal Final Trial Duration . . . . . . . . . . . . . . 30
4.6.2. Goal Duration Sum . . . . . . . . . . . . . . . . . . 30
4.6.3. Goal Loss Ratio . . . . . . . . . . . . . . . . . . . 31
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4.6.4. Goal Exceed Ratio . . . . . . . . . . . . . . . . . . 31
4.6.5. Goal Width . . . . . . . . . . . . . . . . . . . . . 32
4.6.6. Goal Initial Trial Duration . . . . . . . . . . . . . 32
4.6.7. Search Goal . . . . . . . . . . . . . . . . . . . . . 33
4.6.8. Controller Input . . . . . . . . . . . . . . . . . . 33
4.7. Auxiliary Terms . . . . . . . . . . . . . . . . . . . . . 35
4.7.1. Trial Classification . . . . . . . . . . . . . . . . 35
4.7.2. Load Classification . . . . . . . . . . . . . . . . . 36
4.8. Result Terms . . . . . . . . . . . . . . . . . . . . . . 38
4.8.1. Relevant Upper Bound . . . . . . . . . . . . . . . . 38
4.8.2. Relevant Lower Bound . . . . . . . . . . . . . . . . 39
4.8.3. Conditional Throughput . . . . . . . . . . . . . . . 39
4.8.4. Goal Results . . . . . . . . . . . . . . . . . . . . 40
4.8.5. Search Result . . . . . . . . . . . . . . . . . . . . 42
4.8.6. Controller Output . . . . . . . . . . . . . . . . . . 42
4.9. Architecture Terms . . . . . . . . . . . . . . . . . . . 42
4.9.1. Measurer . . . . . . . . . . . . . . . . . . . . . . 43
4.9.2. Controller . . . . . . . . . . . . . . . . . . . . . 44
4.9.3. Manager . . . . . . . . . . . . . . . . . . . . . . . 44
4.10. Compliance . . . . . . . . . . . . . . . . . . . . . . . 45
4.10.1. Test Procedure Compliant with MLRsearch . . . . . . 45
4.10.2. MLRsearch Compliant with RFC 2544 . . . . . . . . . 46
4.10.3. MLRsearch Compliant with TST009 . . . . . . . . . . 47
5. Methodology Rationale and Design Considerations . . . . . . . 47
5.1. Binary Search Commonalities . . . . . . . . . . . . . . . 48
5.2. Stopping Conditions and Precision . . . . . . . . . . . . 48
5.3. Loss Ratios and Loss Inversion . . . . . . . . . . . . . 49
5.3.1. Single Goal and Hard Bounds . . . . . . . . . . . . . 49
5.3.2. Multiple Goals and Loss Inversion . . . . . . . . . . 49
5.3.3. Conservativeness and Relevant Bounds . . . . . . . . 50
5.3.4. Consequences . . . . . . . . . . . . . . . . . . . . 50
5.4. Exceed Ratio and Multiple Trials . . . . . . . . . . . . 50
5.5. Short Trials and Duration Selection . . . . . . . . . . . 51
5.6. Generalized Throughput . . . . . . . . . . . . . . . . . 52
5.6.1. Hard Performance Limit . . . . . . . . . . . . . . . 52
5.6.2. Performance Variability . . . . . . . . . . . . . . . 53
6. MLRsearch Logic and Example . . . . . . . . . . . . . . . . . 53
6.1. Load Classification Logic . . . . . . . . . . . . . . . . 54
6.2. Conditional Throughput Logic . . . . . . . . . . . . . . 55
6.2.1. Conditional Throughput and Load Classification . . . 56
6.3. SUT Behaviors . . . . . . . . . . . . . . . . . . . . . . 57
6.3.1. Expert Predictions . . . . . . . . . . . . . . . . . 57
6.3.2. Exceed Probability . . . . . . . . . . . . . . . . . 57
6.3.3. Trial Duration Dependence . . . . . . . . . . . . . . 57
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 58
8. Security Considerations . . . . . . . . . . . . . . . . . . . 58
9. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 59
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 59
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10.1. Normative References . . . . . . . . . . . . . . . . . . 59
10.2. Informative References . . . . . . . . . . . . . . . . . 60
Appendix A. Load Classification Code . . . . . . . . . . . . . . 61
Appendix B. Conditional Throughput Code . . . . . . . . . . . . 63
Appendix C. Example Search . . . . . . . . . . . . . . . . . . . 65
C.1. Example Goals . . . . . . . . . . . . . . . . . . . . . . 65
C.2. Example Trial Results . . . . . . . . . . . . . . . . . . 66
C.3. Load Classification Computations . . . . . . . . . . . . 68
C.3.1. Point 1 . . . . . . . . . . . . . . . . . . . . . . . 68
C.3.2. Point 2 . . . . . . . . . . . . . . . . . . . . . . . 69
C.3.3. Point 3 . . . . . . . . . . . . . . . . . . . . . . . 70
C.3.4. Point 4 . . . . . . . . . . . . . . . . . . . . . . . 72
C.3.5. Point 5 . . . . . . . . . . . . . . . . . . . . . . . 73
C.3.6. Point 6 . . . . . . . . . . . . . . . . . . . . . . . 74
C.4. Conditional Throughput Computations . . . . . . . . . . . 76
C.4.1. Goal 2 . . . . . . . . . . . . . . . . . . . . . . . 76
C.4.2. Goal 3 . . . . . . . . . . . . . . . . . . . . . . . 77
C.4.3. Goal 4 . . . . . . . . . . . . . . . . . . . . . . . 78
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 79
1. Introduction
This document describes the Multiple Loss Ratio search (MLRsearch)
methodology, optimized for determining data plane throughput in
software-based networking functions running on commodity systems with
generic CPUs (vs purpose-built ASIC / NPU / FPGA). Such network
functions can be deployed on dedicated physical appliance (e.g., a
standalone hardware device) or as virtual appliance (e.g., Virtual
Network Function running on shared servers in the compute cloud).
This document tightly couples terminology and methodology aspects.
Instead of a separate terminology section, Table of Contents
subsections of MLRsearch Specification (Section 4) act as list of
newly defined terms. If a phrase appears with first letters
capitalized, it likely refers to a specific term defined in eponymous
subsection of MLRsearch Specification (Section 4).
For first time readers, the information in MLRsearch Specification
(Section 4) might feel dense and lacking motivation. Subsequent
sections are there to provide explanations, making MLRsearch
Specification (Section 4) more approachable on repeated reads.
1.1. Purpose
The purpose of this document is to describe the Multiple Loss Ratio
search (MLRsearch) methodology, optimized for determining data plane
throughput in software-based networking devices and functions.
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Applying the Binary Search (Section 2.1) to software devices under
test (DUTs) results in several problems:
* Binary search takes a long time as most trials are done far from
the eventually found throughput.
* The required final trial duration and pauses between trials
prolong the overall search duration.
* Software DUTs show noisy trial results, leading to a big spread of
possible discovered throughput values.
* Throughput requires a loss of exactly zero frames, but the
industry best practices frequently allow for low but non-zero
losses tolerance ([Y.1564], test-equipment manuals).
* The definition of throughput is not clear when trial results are
inconsistent. (e.g., when successive trials at the same - or even
a higher - offered load yield different loss ratios, the classical
[RFC1242] / [RFC2544] throughput metric can no longer be pinned to
a single, unambiguous value.)
To address these problems, early MLRsearch implementations employed
the following enhancements:
1. Allow multiple short trials instead of one big trial per load.
* Optionally, tolerate a percentage of trial results with higher
loss.
2. Allow searching for multiple Search Goals (Section 4.6.7), with
differing loss ratios.
* Any trial result can affect each Search Goal in principle.
3. Insert multiple coarse targets for each Search Goal, earlier ones
need to spend less time on trials.
* Earlier targets also aim for lesser precision.
* Use Forwarding Rate (FR) at Maximum Offered Load (FRMOL), as
defined in Section 3.6.2 of [RFC2285], to initialize bounds.
4. Clarify handling of inconsistent trial results.
* Reported throughput should be smaller than the smallest load
with high loss.
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* Measure smaller load candidates first.
5. Apply several time-saving load selection heuristics that
deliberately prevent the bounds from narrowing unnecessarily.
Enhancements 1, 2 and partly 4 are formalized as MLRsearch
Specification within this document, other implementation details are
out the scope.
The remaining enhancements are treated as implementation details,
thus achieving high comparability without limiting future
improvements.
MLRsearch configuration supports both conservative settings and
aggressive settings. Results unconditionally compliant with
[RFC2544] are possible with conservative enough settings, but without
much improvement on search duration and repeatability - see MLRsearch
Compliant with RFC 2544 (Section 4.10.2). Conversely, aggressive
settings lead to shorter search durations and better repeatability,
but the results are not compliant with [RFC2544]. Exact settings are
not specified, but see the discussion in Overview of RFC 2544
Problems (Section 2) for the impact of different settings on result
quality.
This document does not change or obsolete any part of [RFC2544].
1.2. Positioning within BMWG Methodologies
The Benchmarking Methodology Working Group (BMWG) produces
recommendations (RFCs) that describe various benchmarking
methodologies for use in a controlled laboratory environment. A
large number of these benchmarks are based on the terminology from
[RFC1242] and the foundational methodology from [RFC2544]. A common
pattern has emerged where BMWG documents reference the methodology of
[RFC2544] and augment it with specific requirements for testing
particular network systems or protocols, without modifying the core
benchmark definitions.
While BMWG documents are formally recommendations, they are widely
treated as industry norms to ensure the comparability of results
between different labs. The set of benchmarks defined in [RFC2544],
in particular, became a de facto standard for performance testing.
In this context, the MLRsearch Specification formally defines a new
class of benchmarks that fits within the wider [RFC2544] framework
(see Scope (Section 4.1)).
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A primary consideration in the design of MLRsearch is the trade-off
between configurability and comparability. The methodology's
flexibility, especially the ability to define various sets of Search
Goals, supporting both single-goal and multiple-goal benchmarks in an
unified way is powerful for detailed characterization and internal
testing. However, this same flexibility is detrimental to inter-lab
comparability unless a specific, common set of Search Goals is agreed
upon.
Therefore, MLRsearch should not be seen as a direct extension nor a
replacement for the [RFC2544] Throughput benchmark. Instead, this
document provides a foundational methodology that future BMWG
documents can use to define new, specific, and comparable benchmarks
by mandating particular Search Goal configurations. For operators of
existing test procedures, it is worth noting that many test setups
measuring [RFC2544] Throughput can be adapted to produce results
compliant with the MLRsearch Specification, often without affecting
Trials, merely by augmenting the content of the final test report.
2. Overview of RFC 2544 Problems
This section describes the problems affecting usability of various
performance testing methodologies, mainly the Binary Search
(Section 2.1) for [RFC2544] unconditionally compliant throughput.
2.1. Binary Search
While [RFC2544] offers some flexibility when searching for
throughput, a particular algorithm is frequently used as a starting
point, as it is the simplest one among those that offer reasonable
effectivity.
This algorithm is based on (balanced) binary search over sorted
arrays, but does not have a specific name when searching for
throughput. Section 24. Trial duration of [RFC2544] mentions binary
search only in quotes, without providing specifics. In this document
we call that algorithm the Binary Search, as that is the title of
Section 12.3.2 of [TST009] describing a variant of it.
Here is a simplified description of the algorithm:
* Initialize lower-bound variable to line-rate value.
* Initialize upper-bound variable to a loss-free load value.
* Compute a midpoint, the arithmetic mean of current bound values.
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* Run a single 60-second trial at the midpoint (for [RFC2544]
unconditional compliance).
* If loss is zero, set lower-bound to midpoint value; else set
upper-bound to midpoint value.
* Repeat (computing new midpoint) until the gap between the bounds
meets the desired precision.
* Return the final lower-bound value as the throughput.
The [TST009] description had two more requirements (stopping
condition and rounding, both based on Offered Load Step Size
Parameter) but those are not required in this document.
Small modifications related to initial bound values are also allowed.
The load values currently held in the two variables are called
"tightest bounds", especially when discussing older trial results
(logically still bounds).
2.2. Long Search Duration
The proliferation of software DUTs, with frequent software updates
and a number of different frame processing modes and configurations,
has increased both the number of performance tests required to verify
the DUT update and the frequency of running those tests. This makes
the overall test execution time even more important than before.
The definition of throughput test methodology per [RFC2544] restricts
the potential for time-efficiency improvements. The Binary Search
(Section 2.1), when used in a manner unconditionally compliant with
[RFC2544], is excessively slow due to two main factors.
Firstly, a significant amount of time is spent on trials with loads
that, in retrospect, are far from the final determined throughput.
Secondly, [RFC2544] does not specify any stopping condition for
throughput search, so users of testing equipment implementing the
procedure already have access to a limited trade-off between search
duration and achieved precision, as each one of the full 60-second
trials halves the interval of possible results.
As such, not many trials can be removed without a substantial loss of
precision.
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2.3. DUT in SUT
Section 19 of [RFC2544] specifies a test setup with an external
tester stimulating the networking system, treating it either as a
single Device Under Test (DUT), or as a system of devices, a System
Under Test (SUT).
[RFC2285] defines:
DUT as:
* The network frame forwarding device to which stimulus is offered
and response measured Section 3.1.1 of [RFC2285].
SUT as:
* The collective set of network devices as a single entity to which
stimulus is offered and response measured Section 3.1.2 of
[RFC2285].
For software-based data-plane forwarding running on commodity x86/ARM
CPUs, the SUT comprises not only the forwarding application itself,
the DUT, but the entire execution environment: host hardware,
firmware and kernel/hypervisor services, as well as any other
software workloads that share the same CPUs, memory and I/O
resources.
Given that a SUT is a shared multi-tenant environment, the DUT might
inadvertently experience interference from the operating system or
from other software operating on the same server.
Some of this interference can be mitigated. For instance, in multi-
core CPU systems, pinning DUT program threads to specific CPU cores
and isolating those cores can prevent context switching.
Despite taking all feasible precautions, some adverse effects may
still impact the DUT's network performance. In this document, these
effects are collectively referred to as SUT noise, even if the
effects are not as unpredictable as what other engineering
disciplines call noise.
A DUT can also exhibit fluctuating performance itself, for reasons
not related to the rest of SUT. For example, this can be due to
pauses in execution as needed for internal stateful processing. In
many cases this may be an expected per-design behavior, as it would
be observable even in a hypothetical scenario where all sources of
SUT noise are eliminated. Such behavior affects trial results in a
way similar to SUT noise. As the two phenomena are hard to
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distinguish, in this document the term 'noise' is used to encompass
both the internal performance fluctuations of the DUT and the genuine
noise of the SUT.
A simple model of SUT performance consists of an idealized noiseless
performance, and additional noise effects. For a specific SUT, the
noiseless performance is assumed to be constant, with all observed
performance variations being attributed to noise. The impact of the
noise can vary in time, sometimes wildly, even within a single trial.
The noise can sometimes be negligible, but frequently it lowers the
observed SUT performance as observed in trial results.
In this simple model, a SUT does not have a single performance value,
it has a spectrum. One end of the spectrum is the idealized
noiseless performance value, the other end can be called a noiseful
performance. In practice, trial results close to the noiseful end of
the spectrum happen only rarely. The worse a possible performance
value is, the more rarely it is seen in a trial. Therefore, the
extreme noiseful end of the SUT spectrum is not observable among
trial results.
Furthermore, the extreme noiseless end of the SUT spectrum is
unlikely to be observable, this time because minor noise events
almost always occur during each trial, nudging the measured
performance slightly below the theoretical maximum.
Unless specified otherwise, this document's focus is on the
potentially observable ends of the SUT performance spectrum, as
opposed to the extreme ones.
When focusing on the DUT, the benchmarking effort should ideally aim
to eliminate only the SUT noise from SUT measurements. However, this
is currently not feasible in practice, as there are no realistic
enough models that would be capable to distinguish SUT noise from DUT
fluctuations (based on the available literature at the time of
writing).
Provided SUT execution environment and any co-resident workloads
place only negligible demands on SUT shared resources, so that the
DUT remains the principal performance limiter, the DUT's ideal
noiseless performance is defined as the noiseless end of the SUT
performance spectrum.
Note that by this definition, DUT noiseless performance also
minimizes the impact of DUT fluctuations, as much as realistically
possible for a given trial duration.
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The MLRsearch methodology aims to solve the DUT in SUT problem by
estimating the noiseless end of the SUT performance spectrum using a
limited number of trial results.
Improvements to the throughput search algorithm, aimed at better
dealing with software networking SUT and DUT setups, should adopt
methods that explicitly model SUT-generated noise, enabling to derive
surrogate metrics that approximate the (proxies for) DUT noiseless
performance across a range of SUT noise-tolerance levels.
2.4. Repeatability and Comparability
[RFC2544] does not suggest repeating throughput search. Also, note
that from simply one discovered throughput value, it cannot be
determined how repeatable that value is. Unsatisfactory
repeatability then leads to unacceptable comparability, as different
benchmarking teams may obtain varying throughput values for the same
SUT, exceeding the expected differences from search precision.
Repeatability is important also when the test procedure is kept the
same, but SUT is varied in small ways. For example, during
development of software-based DUTs, repeatability is needed to detect
small regressions.
[RFC2544] throughput requirements (60-second trial and no tolerance
of a single frame loss) affect the throughput result as follows:
The SUT behavior close to the noiseful end of its performance
spectrum consists of rare occasions of significantly low performance,
but the long trial duration makes those occasions not so rare on the
trial level. Therefore, the Binary Search (Section 2.1) results tend
to spread away from the noiseless end of SUT performance spectrum,
more frequently and more widely than shorter trials would, thus
causing unacceptable throughput repeatability.
The repeatability problem can be better addressed by defining a
search procedure that identifies a consistent level of performance,
even if it does not meet the strict definition of throughput test
methodology in [RFC2544].
According to the SUT performance spectrum model, better repeatability
will be at the noiseless end of the spectrum. Therefore, solutions
to the DUT in SUT problem will help also with the repeatability
problem.
Conversely, any alteration to [RFC2544] throughput search that
improves repeatability should be considered as less dependent on the
SUT noise.
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An alternative option is to simply run a search multiple times, and
report some statistics (e.g., average and standard deviation, and/or
percentiles like p95).
This can be used for a subset of tests deemed more important, but it
makes the search duration problem even more pronounced.
2.5. Throughput with Non-Zero Loss
Section 3.17 of [RFC1242] defines throughput as: The maximum rate at
which none of the offered frames are dropped by the device.
Then, it says: Since even the loss of one frame in a data stream can
cause significant delays while waiting for the higher-level
protocols to time out, it is useful to know the actual maximum
data rate that the device can support.
However, many benchmarking teams accept a low, non-zero loss ratio as
the goal for their load search.
Motivations are many:
* Networking protocols tolerate frame loss better, compared to the
time when [RFC1242] and [RFC2544] were specified.
* Increased link speeds require trials sending more frames within
the same duration, increasing the chance of a small SUT
performance fluctuation being enough to cause frame loss.
* Because noise-related drops usually arrive in small bursts, their
impact on the trial's overall frame loss ratio is diluted by the
longer intervals in which the SUT operates close to its noiseless
performance; consequently, the averaged Trial Loss Ratio can still
end up below the specified Goal Loss Ratio value.
* If an approximation of the SUT noise impact on the Trial Loss
Ratio is known, it can be set as the Goal Loss Ratio (see
definitions of Trial and Goal terms in Trial Terms (Section 4.5)
and Goal Terms (Section 4.6)).
For more information, see Section 5 of an earlier draft
[Lencze-Shima] (and references there) for few synthetic examples,
confirming that each protocol and application can have different
realistic loss ratio value.
Regardless of the validity of all similar motivations, support for
non-zero loss goals makes a search algorithm applicable for a wider
range of use cases than the approach defined in [RFC2544].
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Furthermore, allowing users to specify multiple loss ratio values,
and enabling a single search to find all relevant bounds,
significantly enhances the usefulness of the search algorithm.
Searching for multiple Search Goals also helps to describe the SUT
performance spectrum better than the result of a single Search Goal.
For example, the repeated wide gap between zero and non-zero loss
loads indicates the noise has a large impact on the observed
performance, which is not evident from a single goal load search
procedure result.
It is easy to modify the Binary Search (Section 2.1) to find a lower
bound for the load that satisfies a non-zero Goal Loss Ratio. But it
is not that obvious how to search for multiple goals at once, hence
the support for multiple Search Goals remains a problem.
At the time of writing there does not seem to be a consensus in the
industry on which loss ratio value is the best. For users,
performance of higher protocol layers is important, for example,
goodput of TCP connection (TCP throughput, [RFC6349]), but
relationship between goodput and loss ratio is not simple. Refer to
[Lencze-Kovacs-Shima] for examples of various corner cases, Section 3
of [RFC6349] for loss ratios acceptable for an accurate measurement
of TCP throughput, and [Ott-Mathis-Semke-Mahdavi] for models and
calculations of TCP performance in presence of packet loss.
2.6. Inconsistent Trial Results
While performing throughput search by executing a sequence of
measurement trials, there is a risk of encountering inconsistencies
between trial results.
Examples include, but are not limited to:
* A trial at the same load (same or different trial duration)
results in a different Trial Loss Ratio.
* A trial at a larger load (same or different trial duration)
results in a lower Trial Loss Ratio.
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The Binary Search (Section 2.1) never encounters inconsistent trials.
But [RFC2544] hints about the possibility of inconsistent trial
results, in two places in its text. The first place is Section 24 of
[RFC2544], where full trial durations are required, presumably
because they can be inconsistent with the results from short trial
durations. The second place is Section 26.3 of [RFC2544], where two
successive zero-loss trials are recommended, presumably because after
one zero-loss trial there can be a subsequent inconsistent non-zero-
loss trial.
A robust throughput search algorithm needs to decide how to continue
the search in the presence of such inconsistencies. Definitions of
throughput and its test methodology in [RFC1242] and [RFC2544] are
not specific enough to imply a unique way of handling such
inconsistencies.
Ideally, there will be a definition of a new quantity which both
generalizes throughput for non-zero Goal Loss Ratio values (and other
possible repeatability enhancements), while being precise enough to
force a specific way to resolve trial result inconsistencies. But
until such a definition is agreed upon, the correct way to handle
inconsistent trial results remains an open problem.
Relevant Lower Bound is the MLRsearch term that addresses this
problem.
3. Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in BCP
14, [RFC2119] and [RFC8174] when, and only when, they appear in all
capitals, as shown here.
This document is categorized as an Informational RFC. While it does
not mandate the adoption of the MLRsearch methodology, it uses the
normative language of BCP 14 to provide an unambiguous specification.
This ensures that if a test procedure or test report claims
compliance with the MLRsearch Specification, it MUST adhere to all
the absolute requirements defined herein. The use of normative
language is intended to promote repeatable and comparable results
among those who choose to implement this methodology.
4. MLRsearch Specification
This chapter provides all technical definitions needed for evaluating
whether a particular test procedure complies with MLRsearch
Specification.
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Some terms used in the specification are capitalized. It is just a
stylistic choice for this document, reminding the reader this term is
introduced, defined or explained elsewhere in the document. Lower
case variants are equally valid.
This document does not separate terminology from methodology. Terms
are fully specified and discussed in their own subsections, under
sections titled "Terms". This way, the list of terms is visible in
table of contents.
Each per term subsection contains a short _Definition_ paragraph
containing a minimal definition and all strict requirements, followed
by _Discussion_ paragraphs focusing on important consequences and
recommendations. Requirements about how other components can use the
defined quantity are also included in the discussion.
4.1. Scope
This document specifies the Multiple Loss Ratio search (MLRsearch)
methodology. The MLRsearch Specification details a new class of
benchmarks by listing all terminology definitions and methodology
requirements. The definitions support "multi-goal" benchmarks, with
"single-goal" as a subset.
The normative scope of this specification includes:
* The terminology for all required quantities and their attributes.
* An abstract architecture consisting of functional components
(Manager, Controller, Measurer) and the requirements for their
inputs and outputs.
* The required structure and attributes of the Controller Input,
including one or more Search Goal instances.
* The required logic for Load Classification, which determines
whether a given Trial Load qualifies as a Lower Bound or an Upper
Bound for a Search Goal.
* The required structure and attributes of the Controller Output,
including a Goal Result for each Search Goal.
4.1.1. Relationship to RFC 2544
MLRsearch Specification is an independent methodology and does not
change or obsolete any part of [RFC2544].
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This specification permits deviations from the Trial procedure as
described in [RFC2544]. Any deviation from the [RFC2544] procedure
must be documented explicitly in the Test Report, and such variations
remain outside the scope of the original [RFC2544] benchmarks.
A specific single-goal MLRsearch benchmark can be configured to be
compliant with [RFC2544] Throughput, and most procedures reporting
[RFC2544] Throughput can be adapted to satisfy also MLRsearch
requirements for specific search goal.
4.1.2. Applicability of Other Specifications
Methodology extensions from other BMWG documents that specify details
for testing particular DUTs, configurations, or protocols (e.g., by
defining a particular Traffic Profile) are considered orthogonal to
MLRsearch and are applicable to a benchmark conducted using MLRsearch
methodology.
4.1.3. Out of Scope
The following aspects are explicitly out of the normative scope of
this document:
* This specification does not mandate or recommend any single,
universal Search Goal configuration for all use cases. The
selection of Search Goal parameters is left to the operator of the
test procedure or may be defined by future specifications.
* The internal heuristics or algorithms used by the Controller to
select Trial Input values (e.g., the load selection strategy) are
considered implementation details.
* The potential for, and the effects of, interference between
different Search Goal instances within a multiple-goal search are
considered outside the normative scope of this specification.
4.2. Architecture Overview
Although the normative text references only terminology that has
already been introduced, explanatory passages beside it sometimes
profit from terms that are defined later in the document. To keep
the initial read-through clear, this informative section offers a
concise, top-down sketch of the complete MLRsearch architecture.
The architecture is modelled as a set of abstract, interacting
components. Information exchange between components is expressed in
an imperative-programming style: one component "calls" another,
supplying inputs (arguments) and receiving outputs (return values).
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This notation is purely conceptual; actual implementations need not
exchange explicit messages. When the text contrasts alternative
behaviours, it refers to the different implementations of the same
component.
A test procedure is considered compliant with the MLRsearch
Specification if it can be conceptually decomposed into the abstract
components defined herein, and each component satisfies the
requirements defined for its corresponding MLRsearch element.
The Measurer component is tasked to perform Trials, the Controller
component is tasked to select Trial Durations and Loads, the Manager
component is tasked to pre-configure involved entities and to produce
the Test Report. The Test Report explicitly states Search Goals (as
Controller Input) and corresponding Goal Results (Controller Output).
This constitutes one benchmark (single-goal or multi-goal). Repeated
or slightly differing benchmarks are realized by calling Controller
once for each benchmark.
The Manager calls a Controller once, and the Controller then invokes
the Measurer repeatedly until Controller decides it has enough
information to return outputs.
The part during which the Controller invokes the Measurer is termed
the Search. Any work the Manager performs either before invoking the
Controller or after Controller returns, falls outside the scope of
the Search.
MLRsearch Specification prescribes Regular Goal Results
(Section 4.8.4.1) and recommends corresponding search completion
conditions. Irregular Goal Results (Section 4.8.4.2) are also
allowed, they have different requirements and their corresponding
stopping conditions are out of scope. The Search Result is the
combination of regular and irregular results for each goal.
Search Results are based on Load Classification. When measured
enough, a chosen Load can either achieve or fail each Search Goal
(separately), thus becoming a Lower Bound or an Upper Bound for that
Search Goal.
When the Relevant Lower Bound is close enough to Relevant Upper Bound
according to Goal Width, the Regular Goal Result is found. Search
stops when all Regular Goal Results are found, or when some Search
Goals are proven to have only Irregular Goal Results.
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4.2.1. Test Report
A primary responsibility of the Manager is to produce a Test Report,
which serves as the final and formal output of the test procedure.
This document does not provide a single, complete, normative
definition for the structure of the Test Report. For example, Test
Report may contain results for a single benchmark, or it could
aggregate results of many benchmarks.
Instead, normative requirements for the content of the Test Report
are specified throughout this document in conjunction with the
definitions of the quantities and procedures to which they apply.
Readers should note that any clause requiring a value to be
"reported" or "stated in the test report" constitutes a normative
requirement on the content of this final artifact.
Even where not stated explicitly, the "Reporting format" paragraphs
in [RFC2544] sections are still requirements on Test Report if they
apply to a MLRsearch benchmark.
4.2.2. Behavior Correctness
MLRsearch Specification by itself does not guarantee that the Search
ends in finite time, as the freedom the Controller has for Load
selection also allows for clearly deficient choices.
For deeper insights on these matters, refer to [FDio-CSIT-MLRsearch].
The primary MLRsearch implementation, used as the prototype for this
specification, is [PyPI-MLRsearch].
4.3. Quantities
MLRsearch Specification uses a number of specific quantities, some of
them can be expressed in several different units.
In general, MLRsearch Specification does not require particular units
to be used, but it is REQUIRED for the test report to state all the
units. For example, ratio quantities can be dimensionless numbers
between zero and one, but may be expressed as percentages instead.
For convenience, a group of quantities can be treated as a composite
quantity. One constituent of a composite quantity is called an
attribute. A group of attribute values is called an instance of that
composite quantity.
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Some attributes may depend on others and can be calculated from other
attributes. Such quantities are called derived quantities.
4.3.1. Current and Final Values
Some quantities are defined in a way that makes it possible to
compute their values in the middle of a Search. Other quantities are
specified so that their values can be computed only after a Search
ends. Some quantities are important only after a Search ended, but
their values are computable also before a Search ends.
For a quantity that is computable before a Search ends, the adjective
*current* is used to mark a value of that quantity available before
the Search ends. When such value is relevant for the search result,
the adjective *final* is used to denote the value of that quantity at
the end of the Search.
If a time evolution of such a dynamic quantity is guided by
configuration quantities, those adjectives can be used to distinguish
quantities. For example, if the current value of "duration" (dynamic
quantity) increases from "initial duration" to "final duration"
(configuration quantities), all the quoted names denote separate but
related quantities. As the naming suggests, the final value of
"duration" is expected to be equal to "final duration" value.
4.4. Existing Terms
This specification relies on the following three documents that
should be consulted before attempting to make use of this document:
* "Benchmarking Terminology for Network Interconnect Devices"
[RFC1242] contains basic term definitions.
* "Benchmarking Terminology for LAN Switching Devices" [RFC2285]
adds more terms and discussions, describing some known network
benchmarking situations in a more precise way.
* "Benchmarking Methodology for Network Interconnect Devices"
[RFC2544] contains discussions about terms and additional
methodology requirements.
Definitions of some central terms from above documents are copied and
discussed in the following subsections.
4.4.1. SUT
Defined in Section 3.1.2 of [RFC2285] as follows.
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Definition:
The collective set of network devices to which stimulus is offered
as a single entity and response measured.
Discussion:
An SUT consisting of a single network device is allowed by this
definition.
In software-based networking SUT may comprise multitude of
networking applications and the entire host hardware and software
execution environment.
SUT is the only entity that can be benchmarked directly, even
though only the performance of some sub-components are of
interest.
4.4.2. DUT
Defined in Section 3.1.1 of [RFC2285] as follows.
Definition:
The network forwarding device to which stimulus is offered and
response measured.
Discussion:
Contrary to SUT, the DUT stimulus and response are frequently
initiated and observed only indirectly, on different parts of SUT.
DUT, as a sub-component of SUT, is only indirectly mentioned in
MLRsearch Specification, but is of key relevance for its
motivation. The device can represent a software-based networking
functions running on commodity x86/ARM CPUs (vs purpose-built ASIC
/ NPU / FPGA).
A well-designed SUTs should have the primary DUT as their
performance bottleneck. The ways to achieve that are outside of
MLRsearch Specification scope.
4.4.3. Trial
A trial is the part of the test described in Section 23 of [RFC2544].
Definition:
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A particular test consists of multiple trials. Each trial returns
one piece of information, for example the loss rate at a
particular input frame rate. Each trial consists of a number of
phases:
a) If the DUT is a router, send the routing update to the "input"
port and pause two seconds to be sure that the routing has
settled.
b) Send the "learning frames" to the "output" port and wait 2
seconds to be sure that the learning has settled. Bridge learning
frames are frames with source addresses that are the same as the
destination addresses used by the test frames. Learning frames
for other protocols are used to prime the address resolution
tables in the DUT. The formats of the learning frame that should
be used are shown in the Test Frame Formats document.
c) Run the test trial.
d) Wait for two seconds for any residual frames to be received.
e) Wait for at least five seconds for the DUT to restabilize.
Discussion:
The traffic is sent only in phase c) and received in phases c) and
d).
Trials are the only stimuli the SUT is expected to experience
during the Search.
In some discussion paragraphs, it is useful to consider the
traffic as sent and received by a tester, as implicitly defined in
Section 6 of [RFC2544].
The definition describes some traits, not using capitalized verbs
to signify strength of the requirements. For the purposes of the
MLRsearch Specification, the test procedure MAY deviate from the
[RFC2544] description, but any such deviation MUST be described
explicitly in the Test Report. It is still RECOMMENDED to not
deviate from the description, as any deviation weakens
comparability.
An example of deviation from [RFC2544] is using shorter wait
times, compared to those described in phases a), b), d) and e).
The [RFC2544] document itself seems to be treating phase b) as any
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type of configuration that cannot be configured only once (by
Manager, before Search starts), as some crucial SUT state could
time-out during the Search. It is RECOMMENDED to interpret the
"learning frames" to be any such time-sensitive per-trial
configuration method, with bridge MAC learning being only one
possible examples. Appendix C.2.4.1 of [RFC2544] lists another
example: ARP with wait time of 5 seconds.
Some methodologies describe recurring tests. If those are based
on Trials, they are treated as multiple independent Trials.
4.5. Trial Terms
This section defines new and redefine existing terms for quantities
relevant as inputs or outputs of a Trial, as used by the Measurer
component. This includes also any derived quantities related to
results of one Trial.
4.5.1. Trial Duration
Definition:
Trial Duration is the intended duration of the phase c) of a
Trial.
Discussion:
The value MUST be positive.
While any positive real value may be provided, some Measurer
implementations MAY limit possible values, e.g., by rounding down
to nearest integer in seconds. In that case, it is RECOMMENDED to
give such inputs to the Controller so that the Controller only
uses the accepted values.
4.5.2. Trial Load
Definition:
Trial Load is the per-interface Intended Load for a Trial.
Discussion:
Trial Load is equivalent to the quantities defined as constant
load (Section 3.4 of [RFC1242]), data rate (Section 14 of
[RFC2544]), and Intended Load (Section 3.5.1 of [RFC2285]), in the
sense that all three definitions specify that this value applies
to one (input or output) interface.
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For specification purposes, it is assumed that this is a constant
load by default, as specified in Section 3.4 of [RFC1242]).
Informally, Traffic Load is a single number that can "scale" any
traffic pattern as long as the intuition of load intended against
a single interface can be applied.
It MAY be possible to use a Trial Load value to describe a non-
constant traffic (using average load when the traffic consists of
repeated bursts of frames e.g., as suggested in Section 21 of
[RFC2544]). In the case of a non-constant load, the Test Report
MUST explicitly mention how exactly non-constant the traffic is
and how it reacts to Traffic Load value. But the rest of the
MLRsearch Specification assumes that is not the case, to avoid
discussing corner cases (e.g., which values are possible within
medium limitations).
Similarly, traffic patterns where different interfaces are subject
to different loads MAY be described by a single Trial Load value
(e.g. using largest load among interfaces), but again the Test
Report MUST explicitly describe how the traffic pattern reacts to
Traffic Load value, and this specification does not discuss all
the implications of that approach.
In the common case of bidirectional traffic, as described in
Section 14. Bidirectional Traffic of [RFC2544], Trial Load is the
data rate per direction, half of aggregate data rate.
Traffic patterns where a single Trial Load does not describe their
scaling cannot be used for MLRsearch benchmarks.
Similarly to Trial Duration, some Measurers MAY limit the possible
values of Trial Load. Contrary to Trial Duration, documenting
such behavior in the test report is OPTIONAL. This is because the
load differences are negligible (and frequently undocumented) in
practice.
The Controller MAY select Trial Load and Trial Duration values in
a way that would not be possible to achieve using any integer
number of data frames.
If a particular Trial Load value is not tied to a single Trial,
e.g., if there are no Trials yet or if there are multiple Trials,
this document uses a shorthand *Load*.
The test report MAY present the aggregate load across multiple
interfaces, treating it as the same quantity expressed using
different units. Each reported Trial Load value MUST state
unambiguously whether it refers to (i) a single interface, (ii) a
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specified subset of interfaces (such as all logical interfaces
mapped to one physical port), or (iii) the total across every
interface. For any aggregate load value, the report MUST also
give the fixed conversion factor that links the per-interface and
multi-interface load values.
The per-interface value remains the primary unit, consistent with
prevailing practice in [RFC1242], [RFC2544], and [RFC2285].
The last paragraph also applies to other terms related to Load.
For example, tests with symmetric bidirectional traffic can report
load-related values as "bidirectional load" (double of
"unidirectional load").
4.5.3. Trial Input
Definition:
Trial Input is a composite quantity, consisting of two attributes:
Trial Duration and Trial Load.
Discussion:
When talking about multiple Trials, it is common to say "Trial
Inputs" to denote all corresponding Trial Input instances.
A Trial Input instance acts as the input for one call of the
Measurer component.
Contrary to other composite quantities, MLRsearch implementations
MUST NOT add optional attributes into Trial Input. This improves
interoperability between various implementations of a Controller
and a Measurer.
Note that both attributes are *intended* quantities, as only those
can be fully controlled by the Controller. The actual offered
quantities, as realized by the Measurer, can be different (and
must be different if not multiplying into integer number of
frames), but questions around those offered quantities are
generally outside of the scope of this document.
4.5.4. Traffic Profile
Definition:
Traffic Profile is a composite quantity containing all attributes
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other than Trial Load and Trial Duration, that are needed for
unique determination of the Trial to be performed.
Discussion:
All the attributes are assumed to be constant during the Search,
and the composite is configured on the Measurer by the Manager
before the Search starts. This is why the traffic profile is not
part of the Trial Input.
Specification of traffic properties included in the Traffic
Profile is the responsibility of the Manager, but the specific
configuration mechanisms are outside of the scope of this
document.
Informally, implementations of the Manager and the Measurer must
be aware of their common set of capabilities, so that Traffic
Profile instance uniquely defines the traffic during the Search.
Typically, Manager and Measurer implementations are tightly
integrated.
Integration efforts between independent Manager and Measurer
implementations are outside of the scope of this document. An
example standardization effort is [Vassilev].
Examples of traffic properties include:
Data link frame size:
* Fixed sizes as listed in Section 3.5 of [RFC1242] and in
Section 9 of [RFC2544]
* IMIX mixed sizes as defined in [RFC6985]
Frame formats and protocol addresses:
* Sections 8, 12 and Appendix C of [RFC2544]
Symmetric bidirectional traffic:
* Section 14 of [RFC2544].
Other traffic properties that need to be somehow specified in
Traffic Profile, and MUST be mentioned in Test Report if they
apply to the benchmark, include:
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* bidirectional traffic from Section 14 of [RFC2544],
* fully meshed traffic from Section 3.3.3 of [RFC2285],
* modifiers from Section 11 of [RFC2544].
* IP version mixing from Section 5.3 of [RFC8219].
4.5.5. Trial Forwarding Ratio
Definition:
The Trial Forwarding Ratio is a dimensionless floating point
value. It MUST range between 0.0 and 1.0, both inclusive. It is
calculated by dividing the number of frames successfully forwarded
by the SUT by the total number of frames expected to be forwarded
during the trial.
Discussion:
For most Traffic Profiles, "expected to be forwarded" means
"intended to get received by SUT from tester". This SHOULD be the
default interpretation. Only if this is not the case, the test
report MUST describe the Traffic Profile in a detail sufficient to
imply how Trial Forwarding Ratio should be calculated.
Trial Forwarding Ratio MAY be expressed in other units (e.g., as a
percentage) in the test report.
Note that, contrary to Load terms, frame counts used to compute
Trial Forwarding Ratio are generally aggregates over all SUT
output interfaces, as most test procedures verify all outgoing
frames. The procedure for [RFC2544] Throughput counts received
frames, so implicitly it implies bidirectional counts for
bidirectional traffic, even though the final value is "rate" that
is still per-interface.
For example, in a test with symmetric bidirectional traffic, if
one direction is forwarded without losses, but the opposite
direction does not forward at all, the Trial Forwarding Ratio
would be 0.5 (50%).
In future extensions, more general ways to compute Trial
Forwarding Ratio may be allowed, but the current MLRsearch
Specification relies on this specific averaged counters approach.
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4.5.6. Trial Loss Ratio
Definition:
The Trial Loss Ratio is equal to one minus the Trial Forwarding
Ratio.
Discussion:
100% minus the Trial Forwarding Ratio, when expressed as a
percentage.
This is almost identical to Frame Loss Rate of Section 3.6 of
[RFC1242]. The only minor differences are that Trial Loss Ratio
does not need to be expressed as a percentage, and Trial Loss
Ratio is explicitly based on averaged frame counts when more than
one data stream is present.
4.5.7. Trial Forwarding Rate
Definition:
The Trial Forwarding Rate is a derived quantity, calculated by
multiplying the Trial Load by the Trial Forwarding Ratio.
Discussion:
This quantity differs from the Forwarding Rate described in
Section 3.6.1 of [RFC2285]. Under the RFC 2285 method, each
output interface is measured separately, so every interface may
report a distinct rate. The Trial Forwarding Rate, by contrast,
uses a single set of frame counts and therefore yields one value
that represents the whole system, while still preserving the
direct link to the per-interface load.
When the Traffic Profile is symmetric and bidirectional, as
defined in Section 14 of [RFC2544], the Trial Forwarding Rate is
numerically equal to the arithmetic average of the individual per-
interface forwarding rates that would be produced by the RFC 2285
procedure.
For more complex traffic patterns, such as many-to-one as
mentioned in Section 3.3.2 Partially Meshed Traffic of [RFC2285],
the meaning of Trial Forwarding Rate is less straightforward. For
example, if two input interfaces receive one million frames per
second each, and a single interface outputs 1.4 million frames per
second (fps), Trial Load is 1 million fps, Trial Loss Ratio is
30%, and Trial Forwarding Rate is 0.7 million fps.
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Because this rate is anchored to the Load defined for one
interface, a test report MAY show it either as the single averaged
figure just described, or as the sum of the separate per-interface
forwarding rates. For the example above, the aggregate trial
forwarding rate is 1.4 million fps.
4.5.8. Trial Effective Duration
Definition:
Trial Effective Duration is a time quantity related to a Trial, by
default equal to the Trial Duration.
Discussion:
This is an optional feature. If the Measurer does not return any
Trial Effective Duration value, the Controller MUST use the Trial
Duration value instead.
Trial Effective Duration may be any positive time quantity chosen
by the Measurer to be used for time-based decisions in the
Controller.
The test report MUST explain how the Measurer computes the
returned Trial Effective Duration values, if they are not always
equal to the Trial Duration.
This feature can be beneficial for time-critical benchmarks
designed to manage the overall search duration, rather than solely
the traffic portion of it. An approach is to measure the duration
of the whole trial (including all wait times) and use that as the
Trial Effective Duration.
This is also a way for the Measurer to inform the Controller about
its surprising behavior, for example, when rounding the Trial
Duration value.
4.5.9. Trial Output
Definition:
Trial Output is a composite quantity consisting of several
attributes. Required attributes are: Trial Loss Ratio, Trial
Effective Duration and Trial Forwarding Rate.
Discussion:
When referring to more than one trial, plural term "Trial Outputs"
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is used to collectively describe multiple Trial Output instances.
Measurer implementations may provide additional optional
attributes. The Controller implementations SHOULD ignore values
of any optional attribute they are not familiar with, except when
passing Trial Output instances to the Manager.
Example of an optional attribute: The aggregate number of frames
expected to be forwarded during the trial, especially if it is not
(a rounded-down value) implied by Trial Load and Trial Duration.
While Section 3.5.2 of [RFC2285] requires the Offered Load value
to be reported for forwarding rate measurements, it is not
required in MLRsearch Specification, as search results do not
depend on it.
4.5.10. Trial Result
Definition:
Trial Result is a composite quantity, consisting of the Trial
Input and the Trial Output.
Discussion:
When referring to more than one trial, plural term "Trial Results"
is used to collectively describe multiple Trial Result instances.
4.6. Goal Terms
This section defines new terms for quantities relevant (directly or
indirectly) for inputs and outputs of the Controller component.
Several goal attributes are defined before introducing the main
composite quantity: the Search Goal.
Contrary to other sections, definitions in subsections of this
section are necessarily vague, as their fundamental meaning is to act
as coefficients in formulas for Controller Output, which are not
defined yet.
The discussions in this section relate the attributes to concepts
mentioned in Overview of RFC 2544 Problems (Section 2), but even
these discussion paragraphs are short, informal, and mostly
referencing later sections, where the impact on search results is
discussed after introducing the complete set of auxiliary terms.
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4.6.1. Goal Final Trial Duration
Definition:
Minimal value for Trial Duration that must be reached. The value
MUST be positive.
Discussion:
Certain trials must reach this minimum duration before a load can
be classified as a lower bound.
The Controller may choose shorter durations, results of those may
be enough for classification as an Upper Bound.
It is RECOMMENDED for all search goals to share the same Goal
Final Trial Duration value. Otherwise, Trial Duration values
larger than the Goal Final Trial Duration may occur, weakening the
assumptions the Load Classification Logic (Section 6.1) is based
on.
4.6.2. Goal Duration Sum
Definition:
A threshold value for a particular sum of Trial Effective Duration
values. The value MUST be positive.
Discussion:
Informally, this prescribes the sufficient number of trials
performed at a specific Trial Load and Goal Final Trial Duration
during the search.
If the Goal Duration Sum is larger than the Goal Final Trial
Duration, multiple trials may be needed to be performed at the
same load.
Refer to MLRsearch Compliant with TST009 (Section 4.10.3) for an
example where the possibility of multiple trials at the same load
is intended.
A Goal Duration Sum value shorter than the Goal Final Trial
Duration (of the same goal) could save some search time, but is
NOT RECOMMENDED, as the time savings come at the cost of decreased
repeatability.
In practice, the Search can spend less than Goal Duration Sum
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measuring a Load value when the results are particularly one-
sided, but also, the Search can spend more than Goal Duration Sum
measuring a Load when the results are balanced and include trials
shorter than Goal Final Trial Duration.
4.6.3. Goal Loss Ratio
Definition:
A threshold value for Trial Loss Ratio values. The value MUST be
non-negative and smaller than one.
Discussion:
A trial with Trial Loss Ratio larger than this value signals the
SUT may be unable to process this Trial Load well enough.
See Throughput with Non-Zero Loss (Section 2.5) for reasons why
users may want to set this value above zero.
Since multiple trials may be needed for one Load value, the Load
Classification may be more complicated than mere comparison of
Trial Loss Ratio to Goal Loss Ratio.
4.6.4. Goal Exceed Ratio
Definition:
A threshold value for a particular ratio of sums of Trial
Effective Duration values. The value MUST be non-negative and
smaller than one.
Discussion:
Informally, up to this proportion of Trial Results with Trial Loss
Ratio above Goal Loss Ratio is tolerated at a Lower Bound. This
is the full impact if every Trial was measured at Goal Final Trial
Duration. The actual full logic is more complicated, as shorter
Trials are allowed.
For explainability reasons, the RECOMMENDED value for exceed ratio
is 0.5 (50%), as in practice that value leads to the smallest
variation in overall Search Duration.
Refer to Exceed Ratio and Multiple Trials (Section 5.4) for more
details.
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4.6.5. Goal Width
Definition:
A threshold value for deciding whether two Trial Load values are
close enough. This is an OPTIONAL attribute. If present, the
value MUST be positive.
Discussion:
Informally, this acts as a stopping condition, controlling the
precision of the search result. The search stops if every goal
has reached its precision.
Implementations without this attribute MUST provide the Controller
with other means to control the search stopping conditions.
Absolute load difference and relative load difference are two
popular choices, but implementations may choose a different way to
specify width.
The test report MUST make it clear what specific quantity is used
as Goal Width.
It is RECOMMENDED to express Goal Width as a relative difference
and setting it to a value not lower than the Goal Loss Ratio.
Refer to Generalized Throughput (Section 5.6) for more elaboration
on the reasoning.
4.6.6. Goal Initial Trial Duration
Definition:
Minimal value for Trial Duration suggested to use for this goal.
If present, this value MUST be positive.
Discussion:
This is an example of an optional Search Goal.
A typical default value is equal to the Goal Final Trial Duration
value.
Informally, this is the shortest Trial Duration the Controller
should select when focusing on the goal.
Note that shorter Trial Duration values can still be used, for
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example, selected while focusing on a different Search Goal. Such
results MUST be still accepted by the Load Classification logic.
Goal Initial Trial Duration is a mechanism for a user to
discourage trials with Trial Duration values deemed as too
unreliable for a particular SUT and a given Search Goal.
4.6.7. Search Goal
Definition:
The Search Goal is a composite quantity consisting of several
attributes, some of them are required.
Required attributes: Goal Final Trial Duration, Goal Duration Sum,
Goal Loss Ratio and Goal Exceed Ratio.
Optional attributes: Goal Initial Trial Duration and Goal Width.
Discussion:
Implementations MAY add their own attributes. Those additional
attributes may be required by an implementation even if they are
not required by MLRsearch Specification. However, it is
RECOMMENDED for those implementations to support missing
attributes by providing typical default values.
For example, implementations with Goal Initial Trial Durations may
also require users to specify "how quickly" should Trial Durations
increase.
Refer to Section 4.10 for important Search Goal settings.
4.6.8. Controller Input
Definition:
Controller Input is a composite quantity required as an input for
the Controller. The only REQUIRED attribute is a list of Search
Goal instances.
Discussion:
MLRsearch implementations MAY use additional attributes. Those
additional attributes may be required by an implementation even if
they are not required by MLRsearch Specification.
Formally, the Manager does not apply any Controller configuration
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apart from one Controller Input instance.
For example, Traffic Profile is configured on the Measurer by the
Manager, without explicit assistance of the Controller.
The order of Search Goal instances in a list SHOULD NOT have a big
impact on Controller Output, but MLRsearch implementations MAY
base their behavior on the order of Search Goal instances in a
list.
4.6.8.1. Max Load
Definition:
Max Load is an optional attribute of Controller Input. It is the
maximal value the Controller is allowed to use for Trial Load
values.
Discussion:
Max Load is an example of an optional attribute (outside the list
of Search Goals) required by some implementations of MLRsearch.
If the Max Load value is provided, Controller MUST NOT select
Trial Load values larger than that value.
In theory, each search goal could have its own Max Load value, but
as all Trial Results are possibly affecting all Search Goals, it
makes more sense for a single Max Load value to apply to all
Search Goal instances.
While Max Load is a frequently used configuration parameter,
already governed (as maximum frame rate) by Section 20 of
[RFC2544] and (as maximum offered load) by Section 3.5.3 of
[RFC2285], some implementations may detect or discover it (instead
of requiring a user-supplied value).
In MLRsearch Specification, one reason for listing the Relevant
Upper Bound (Section 4.8.1) as a required attribute is that it
makes the search result independent of Max Load value.
Given that Max Load is a quantity based on Load, Test Report MAY
express this quantity using multi-interface values, as sum of per-
interface maximal loads.
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4.6.8.2. Min Load
Definition:
Min Load is an optional attribute of Controller Input. It is the
minimal value the Controller is allowed to use for Trial Load
values.
Discussion:
Min Load is another example of an optional attribute required by
some implementations of MLRsearch. Similarly to Max Load, it
makes more sense to prescribe one common value, as opposed to
using a different value for each Search Goal.
If the Min Load value is provided, Controller MUST NOT select
Trial Load values smaller than that value.
Min Load is mainly useful for saving time by failing early,
arriving at an Irregular Goal Result when Min Load gets classified
as an Upper Bound.
For implementations, it is RECOMMENDED to require Min Load to be
non-zero and large enough to result in at least one frame being
forwarded even at shortest allowed Trial Duration, so that Trial
Loss Ratio is always well-defined, and the implementation can
apply relative Goal Width safely.
Given that Min Load is a quantity based on Load, Test Report MAY
express this quantity using multi-interface values, as sum of per-
interface minimal loads.
4.7. Auxiliary Terms
While the terms defined in this section are not strictly needed when
formulating MLRsearch requirements, they simplify the language used
in discussion paragraphs and explanation sections.
4.7.1. Trial Classification
When one Trial Result instance is compared to one Search Goal
instance, several relations can be named using short adjectives.
As trial results do not affect each other, this *Trial
Classification* does not change during a Search.
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4.7.1.1. High-Loss Trial
A trial with Trial Loss Ratio larger than a Goal Loss Ratio value is
called a *high-loss trial*, with respect to given Search Goal (or
lossy trial, if Goal Loss Ratio is zero).
4.7.1.2. Low-Loss Trial
If a trial is not high-loss, it is called a *low-loss trial* (or
zero-loss trial, if Goal Loss Ratio is zero).
4.7.1.3. Short Trial
A trial with Trial Duration shorter than the Goal Final Trial
Duration is called a *short trial* (with respect to the given Search
Goal).
4.7.1.4. Full-Length Trial
A trial that is not short is called a *full-length* trial.
Note that this includes Trial Durations larger than Goal Final Trial
Duration.
4.7.1.5. Long Trial
A trial with Trial Duration longer than the Goal Final Trial Duration
is called a *long trial*.
4.7.2. Load Classification
When a set of all Trial Result instances, performed so far at one
Trial Load, is compared to one Search Goal instance, their relation
can be named using the concept of a bound.
In general, such bounds are a current quantity, even though cases of
a Load changing its classification more than once during the Search
is rare in practice.
4.7.2.1. Upper Bound
Definition:
A Load value is called an Upper Bound if and only if it is
classified as such by load classification code (Appendix A)
algorithm for the given Search Goal at the current moment of the
Search.
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Discussion:
In more detail, the set of all Trial Result instances performed so
far at the Trial Load (and any Trial Duration) is certain to fail
to uphold all the requirements of the given Search Goal, mainly
the Goal Loss Ratio in combination with the Goal Exceed Ratio. In
this context, "certain to fail" relates to any possible results
within the time remaining till Goal Duration Sum.
One search goal can have multiple different Trial Load values
classified as its Upper Bounds. While search progresses and more
trials are measured, any load value can become an Upper Bound in
principle.
Moreover, a Load can stop being an Upper Bound, but that can only
happen when more than Goal Duration Sum of trials are measured
(e.g., because another Search Goal needs more trials at this
load). Informally, the previous Upper Bound got invalidated. In
practice, the Load frequently becomes a Lower Bound
(Section 4.7.2.2) instead.
4.7.2.2. Lower Bound
Definition:
A Load value is called a Lower Bound if and only if it is
classified as such by load classification code (Appendix A)
algorithm for the given Search Goal at the current moment of the
search.
Discussion:
In more detail, the set of all Trial Result instances performed so
far at the Trial Load (and any Trial Duration) is certain to
uphold all the requirements of the given Search Goal, mainly the
Goal Loss Ratio in combination with the Goal Exceed Ratio. Here
"certain to uphold" relates to any possible results within the
time remaining till Goal Duration Sum.
One search goal can have multiple different Trial Load values
classified as its Lower Bounds. As search progresses and more
trials are measured, any load value can become a Lower Bound in
principle.
No load can be both an Upper Bound and a Lower Bound for the same
Search goal at the same time, but it is possible for a larger load
to be a Lower Bound while a smaller load is an Upper Bound.
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Moreover, a Load can stop being a Lower Bound, but that can only
happen when more than Goal Duration Sum of trials are measured
(e.g., because another Search Goal needs more trials at this
load). Informally, the previous Lower Bound got invalidated. In
practice, the Load frequently becomes an Upper Bound
(Section 4.7.2.1) instead.
4.7.2.3. Undecided
Definition:
A Load value is called Undecided if it is currently neither an
Upper Bound nor a Lower Bound.
Discussion:
A Load value that has not been measured so far is Undecided.
It is possible for a Load to transition from an Upper Bound to
Undecided by adding Short Trials with Low-Loss results. That is
yet another reason for users to avoid using Search Goal instances
with different Goal Final Trial Duration values.
4.8. Result Terms
Before defining the full structure of a Controller Output, it is
useful to define the composite quantity, called Goal Result. The
following subsections define its attribute first, before describing
the Goal Result quantity.
There is a correspondence between Search Goals and Goal Results.
Most of the following subsections refer to a given Search Goal, when
defining their terms. Conversely, at the end of the search, each
Search Goal instance has its corresponding Goal Result instance.
4.8.1. Relevant Upper Bound
Definition:
The Relevant Upper Bound is the smallest Trial Load value
classified as an Upper Bound for a given Search Goal at the end of
the Search.
Discussion:
If no measured load had enough High-Loss Trials, the Relevant
Upper Bound MAY be non-existent. For example, when Max Load is
classified as a Lower Bound.
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Conversely, when Relevant Upper Bound does exist, it is not
affected by Max Load value.
Given that Relevant Upper Bound is a quantity based on Load, Test
Report MAY express this quantity using multi-interface values, as
sum of per-interface loads.
4.8.2. Relevant Lower Bound
Definition:
The Relevant Lower Bound is the largest Trial Load value among
those smaller than the Relevant Upper Bound, that got classified
as a Lower Bound for a given Search Goal at the end of the search.
Discussion:
If no load had enough Low-Loss Trials, the Relevant Lower Bound
MAY be non-existent.
Strictly speaking, if the Relevant Upper Bound does not exist, the
Relevant Lower Bound also does not exist. In a typical case, Max
Load is classified as a Lower Bound, making it impossible to
increase the Load to continue the search for an Upper Bound.
Thus, it is not clear whether a larger value would be found for a
Relevant Lower Bound if larger Loads were possible.
Given that Relevant Lower Bound is a quantity based on Load, Test
Report MAY express this quantity using multi-interface values, as
sum of per-interface loads.
4.8.3. Conditional Throughput
Definition:
Conditional Throughput is a value computed at the Relevant Lower
Bound according to algorithm defined in conditional throughput
code (Appendix B).
Discussion:
The Relevant Lower Bound is defined only at the end of the Search,
and so is the Conditional Throughput. But the algorithm can be
applied at any time on any Lower Bound load, so the final
Conditional Throughput value may appear sooner than at the end of
a Search.
Informally, the Conditional Throughput should be a typical Trial
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Forwarding Rate, expected to be seen at the Relevant Lower Bound
of a given Search Goal.
But frequently it is only a conservative estimate thereof, as
MLRsearch implementations tend to stop measuring more Trials as
soon as they confirm the value cannot get worse than this estimate
within the Goal Duration Sum.
This value is RECOMMENDED to be used when evaluating repeatability
and comparability of different MLRsearch implementations.
Refer to Generalized Throughput (Section 5.6) for more details.
Given that Conditional Throughput is a quantity based on Load,
Test Report MAY express this quantity using multi-interface
values, as sum of per-interface forwarding rates.
4.8.4. Goal Results
MLRsearch Specification is based on a set of requirements for a
"regular" result. But in practice, it is not always possible for
such result instance to exist, so also "irregular" results need to be
supported.
4.8.4.1. Regular Goal Result
Definition:
Regular Goal Result is a composite quantity consisting of several
attributes. Relevant Upper Bound and Relevant Lower Bound are
REQUIRED attributes. Conditional Throughput is a RECOMMENDED
attribute.
Discussion:
Implementations MAY add their own attributes.
Test report MUST display Relevant Lower Bound. Displaying
Relevant Upper Bound is RECOMMENDED, especially if the
implementation does not use Goal Width.
In general, stopping conditions for the corresponding Search Goal
MUST be satisfied to produce a Regular Goal Result. Specifically,
if an implementation offers Goal Width as a Search Goal attribute,
the distance between the Relevant Lower Bound and the Relevant
Upper Bound MUST NOT be larger than the Goal Width.
For stopping conditions refer to Goal Width (Section 4.6.5) and
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Stopping Conditions and Precision (Section 5.2).
4.8.4.2. Irregular Goal Result
Definition:
Irregular Goal Result is a composite quantity. No attributes are
required.
Discussion:
It is RECOMMENDED to report any useful quantity even if it does
not satisfy all the requirements. For example, if Max Load is
classified as a Lower Bound, it is fine to report it as an
"effective" Relevant Lower Bound (although not a real one, as that
requires Relevant Upper Bound which does not exist in this case),
and compute Conditional Throughput for it. In this case, only the
missing Relevant Upper Bound signals this result instance is
irregular.
Similarly, if both relevant bounds exist, it is RECOMMENDED to
include them as Irregular Goal Result attributes, and let the
Manager decide if their distance is too far for Test Report
purposes.
If Test Report displays some Irregular Goal Result attribute
values, they MUST be clearly marked as coming from irregular
results.
The implementation MAY define additional attributes, for example
explicit flags for expected situations, so the Manager logic can
be simpler.
4.8.4.3. Goal Result
Definition:
Goal Result is a composite quantity. Each instance is either a
Regular Goal Result or an Irregular Goal Result.
Discussion:
The Manager MUST be able of distinguishing whether the instance is
regular or not.
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4.8.5. Search Result
Definition:
The Search Result is a single composite object that maps each
Search Goal instance to a corresponding Goal Result instance.
Discussion:
As an alternative to mapping, the Search Result may be represented
as an ordered list of Goal Result instances that appears in the
exact sequence of their corresponding Search Goal instances.
When the Search Result is expressed as a mapping, it MUST contain
an entry for every Search Goal instance supplied in the Controller
Input.
Identical Goal Result instances MAY be listed for different Search
Goals, but their status as regular or irregular may be different.
For example, if two goals differ only in Goal Width value, and the
relevant bound values are close enough according to only one of
them.
4.8.6. Controller Output
Definition:
The Controller Output is a composite quantity returned from the
Controller to the Manager at the end of the search. The Search
Result instance is its only required attribute.
Discussion:
MLRsearch implementation MAY return additional data in the
Controller Output, e.g., number of trials performed and the total
Search Duration.
4.9. Architecture Terms
MLRsearch architecture consists of three main system components: the
Manager, the Controller, and the Measurer. The components were
introduced in Architecture Overview (Section 4.2), and the following
subsections finalize their definitions using terms from previous
sections.
Note that the architecture also implies the presence of other
components, such as the SUT and the tester (as a sub-component of the
Measurer).
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Communication protocols and interfaces between components are left
unspecified. For example, when MLRsearch Specification mentions
"Controller calls Measurer", it is possible that the Controller
notifies the Manager to call the Measurer indirectly instead. In
doing so, the Measurer implementations can be fully independent from
the Controller implementations, e.g., developed in different
programming languages.
4.9.1. Measurer
Definition:
The Measurer is a functional element that when called with a Trial
Input (Section 4.5.3) instance, performs one Trial (Section 4.4.3)
and returns a Trial Output (Section 4.5.9) instance.
Discussion:
This definition assumes the Measurer is already initialized. In
practice, there may be additional steps before the Search, e.g.,
when the Manager configures the traffic profile (either on the
Measurer or on its tester sub-component directly) and performs a
warm-up (if the tester or the test procedure requires one).
It is the responsibility of the Measurer implementation to uphold
any requirements and assumptions present in MLRsearch
Specification, e.g., Trial Forwarding Ratio not being larger than
one.
Implementers have some freedom. For example, Section 10 of
[RFC2544] gives some suggestions (but not requirements) related to
duplicated or reordered frames. Implementations are RECOMMENDED
to document their behavior related to such freedoms in as detailed
a way as possible.
It is RECOMMENDED to benchmark the test equipment first, e.g.,
connect sender and receiver directly (without any SUT in the
path), find a load value that guarantees the Offered Load is not
too far from the Intended Load and use that value as the Max Load
value. When testing the real SUT, it is RECOMMENDED to turn any
severe deviation between the Intended Load and the Offered Load
into increased Trial Loss Ratio.
Neither of the two recommendations are made into mandatory
requirements, because it is not easy to provide guidance about
when the difference is severe enough, in a way that would be
disentangled from other Measurer freedoms.
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For a sample situation where the Offered Load cannot keep up with
the Intended Load, and the consequences on MLRsearch result, refer
to Hard Performance Limit (Section 5.6.1).
4.9.2. Controller
Definition:
The Controller is a functional element that, upon receiving a
Controller Input instance, repeatedly generates Trial Input
instances for the Measurer and collects the corresponding Trial
Output instances. This cycle continues until the stopping
conditions are met, at which point the Controller produces a final
Controller Output instance and terminates.
Discussion:
Informally, the Controller has big freedom in selection of Trial
Inputs, and the implementations want to achieve all the Search
Goals in the shortest average time.
The Controller's role in optimizing the overall Search Duration
distinguishes MLRsearch algorithms from simpler search procedures.
Informally, each implementation can have different stopping
conditions. Goal Width is only one example. In practice,
implementation details do not matter, as long as Goal Result
instances are regular.
4.9.3. Manager
Definition:
The Manager is a functional element that is responsible for
provisioning other components, calling a Controller component
once, and for creating the test report following the reporting
format as defined in Section 26 of [RFC2544].
Discussion:
The Manager initializes the SUT, the Measurer (and the tester if
independent from Measurer) with their intended configurations
before calling the Controller.
Note that Section 7 of [RFC2544] already puts requirements on SUT
setups:
"It is expected that all of the tests will be run without changing
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the configuration or setup of the DUT in any way other than that
required to do the specific test. For example, it is not
acceptable to change the size of frame handling buffers between
tests of frame handling rates or to disable all but one transport
protocol when testing the throughput of that protocol."
It is REQUIRED for the test report to encompass all the SUT
configuration details, including description of a "default"
configuration common for most tests and configuration changes if
required by a specific test.
For example, Section 5.1.1 of [RFC5180] recommends testing jumbo
frames if SUT can forward them, even though they are outside the
scope of the 802.3 IEEE standard. In this case, it is acceptable
for the SUT default configuration to not support jumbo frames, and
only enable this support when testing jumbo traffic profiles, as
the handling of jumbo frames typically has different packet buffer
requirements and potentially higher processing overhead. Non-
jumbo frame sizes should also be tested on the jumbo-enabled
setup.
The Manager does not need to be able to tweak any Search Goal
attributes, but it MUST report all applied attribute values even
if not tweaked.
A "user" - human or automated - invokes the Manager once to launch
a single Search and receive its report. Every new invocation is
treated as a fresh, independent Search; how the system behaves
across multiple calls (for example, combining or comparing their
results) is explicitly out of scope for this document.
4.10. Compliance
This section discusses compliance relations between MLRsearch and
other test procedures.
4.10.1. Test Procedure Compliant with MLRsearch
Any networking measurement setup that could be understood as
consisting of functional elements satisfying requirements for the
Measurer, the Controller and the Manager, is compliant with MLRsearch
Specification.
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These components can be seen as abstractions present in any testing
procedure. For example, there can be a single component acting both
as the Manager and the Controller, but if values of required
attributes of Search Goals and Goal Results are visible in the test
report, the Controller Input instance and Controller Output instance
are implied.
For example, any setup for conditionally (or unconditionally)
compliant [RFC2544] throughput testing can be understood as a
MLRsearch architecture, if there is enough data to reconstruct the
Relevant Upper Bound.
Refer to MLRsearch Compliant with RFC 2544 (Section 4.10.2) for an
equivalent Search Goal.
Any test procedure that can be understood as one call to the Manager
of MLRsearch architecture is said to be compliant with MLRsearch
Specification.
4.10.2. MLRsearch Compliant with RFC 2544
The following Search Goal instance makes the corresponding Search
Result unconditionally compliant with Section 24 of [RFC2544].
* Goal Final Trial Duration = 60 seconds
* Goal Duration Sum = 60 seconds
* Goal Loss Ratio = 0%
* Goal Exceed Ratio = 0%
Goal Loss Ratio and Goal Exceed Ratio attributes, are enough to make
the Search Goal conditionally compliant. Adding Goal Final Trial
Duration makes the Search Goal unconditionally compliant.
Goal Duration Sum prevents MLRsearch from repeating zero-loss Full-
Length Trials.
The presence of other Search Goals does not affect the compliance of
this Goal Result. The Relevant Lower Bound and the Conditional
Throughput are in this case equal to each other, and the value is the
[RFC2544] throughput.
Non-zero exceed ratio is not strictly disallowed, but it could
needlessly prolong the search when Low-Loss short trials are present.
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4.10.3. MLRsearch Compliant with TST009
One of the alternatives to [RFC2544] is Binary search with loss
verification as described in Section 12.3.3 of [TST009].
The rationale of such search is to repeat high-loss trials, hoping
for zero loss on second try, so the results are closer to the
noiseless end of performance spectrum, thus more repeatable and
comparable.
Only the variant with "z = infinity" is achievable with MLRsearch.
For example, for "max(r) = 2" variant, the following Search Goal
instance should be used to get a compatible Search Result:
* Goal Final Trial Duration = 60 seconds
* Goal Duration Sum = 120 seconds
* Goal Loss Ratio = 0%
* Goal Exceed Ratio = 50%
If the first 60-second trial has zero loss, it is enough for
MLRsearch to stop measuring at that load, as even a second high-loss
trial would still fit within the exceed ratio.
But if the first trial is high-loss, MLRsearch needs to perform also
the second trial to classify that load. Goal Duration Sum is twice
as long as Goal Final Trial Duration, so third full-length trial is
never needed.
5. Methodology Rationale and Design Considerations
This section explains the Why behind MLRsearch. Building on the
normative specification in MLRsearch Specification (Section 4), it
contrasts MLRsearch with the classic [RFC2544] single-ratio Binary
Search (Section 2.1) and walks through the key design choices: search
mechanics, stopping-rule precision, loss-inversion for multiple
goals, exceed-ratio handling, short-trial strategies, and the
generalised throughput concept. Together, these considerations show
how the methodology reduces test time, supports multiple loss ratios,
and improves repeatability.
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5.1. Binary Search Commonalities
A typical search implementation for [RFC2544] such as Binary Search
(Section 2.1) tracks only the two tightest bounds (in variables
"lower-bound" and "upper-bound"). To start, the search needs both
Max Load and Min Load values. Then, one trial is used to confirm Max
Load is an Upper Bound, and one trial to confirm Min Load is a Lower
Bound.
Then, next Trial Load is chosen as the mean of the current tightest
upper bound and the current tightest lower bound, and becomes a new
tightest bound depending on the Trial Loss Ratio.
After some number of trials, the tightest lower bound becomes the
throughput, but [RFC2544] does not specify when, if ever, the search
should stop. In practice, the search stops either at some distance
between the tightest upper bound and the tightest lower bound, or
after some number of Trials.
For a given pair of Max Load and Min Load values, there is one-to-one
correspondence between number of Trials and final distance between
the tightest bounds. Thus, the search always takes the same time,
assuming initial bounds are confirmed.
5.2. Stopping Conditions and Precision
MLRsearch Specification requires listing both Relevant Bounds for
each Search Goal, and the difference between the bounds implies
whether the result precision is achieved. Therefore, it is not
necessary to report the specific stopping condition used.
MLRsearch implementations may use Goal Width to allow direct control
of result precision and indirect control of the Search Duration.
Other MLRsearch implementations may use different stopping
conditions: for example based on the Search Duration, trading off
precision control for duration control.
Due to various possible time optimizations, there is no strict
correspondence between the Search Duration and Goal Width values. In
practice, noisy SUT performance increases both average search time
and its variance.
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5.3. Loss Ratios and Loss Inversion
The biggest difference between MLRsearch and Binary Search
(Section 2.1) is in the goals of the search. [RFC2544] has a single
goal, based on classifying a single full-length trial as either zero-
loss or non-zero-loss. MLRsearch supports searching for multiple
Search Goals at once, usually differing in their Goal Loss Ratio
values.
5.3.1. Single Goal and Hard Bounds
Each bound in Binary Search (Section 2.1) is "hard", in the sense
that all further Trial Load values are smaller than any current upper
bound and larger than any current lower bound.
This is also possible for MLRsearch implementations, when the search
is started with only one Search Goal instance.
5.3.2. Multiple Goals and Loss Inversion
MLRsearch Specification supports multiple Search Goals, making the
search procedure more complicated compared to Binary Search
(Section 2.1) with single goal, but most of the complications do not
affect the final results much. Except for one phenomenon: Loss
Inversion.
Depending on Search Goal attributes, Load Classification results may
be resistant to small amounts of Inconsistent Trial Results
(Section 2.6). However, for larger amounts, a Load that is
classified as an Upper Bound for one Search Goal may still be a Lower
Bound for another Search Goal. Due to this other goal, MLRsearch
will probably perform subsequent Trials at Trial Loads even larger
than the original value.
This introduces questions any many-goals search algorithm has to
address. For example: What to do when all such larger load trials
happen to have zero loss? Does it mean the earlier upper bound was
not real? Does it mean the later Low-Loss trials are not considered
a lower bound?
The situation where a smaller Load is classified as an Upper Bound,
while a larger Load is classified as a Lower Bound (for the same
search goal), is called Loss Inversion.
Conversely, only single-goal search algorithms can have hard bounds
that shield them from Loss Inversion.
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5.3.3. Conservativeness and Relevant Bounds
MLRsearch is conservative when dealing with Loss Inversion: the Upper
Bound is considered real, and the Lower Bound is considered to be a
fluke, at least when computing the final result.
This is formalized using definitions of Relevant Upper Bound
(Section 4.8.1) and Relevant Lower Bound (Section 4.8.2).
The Relevant Upper Bound (for specific goal) is the smallest Load
classified as an Upper Bound. But the Relevant Lower Bound is not
simply the largest among Lower Bounds. It is the largest Load among
Loads that are Lower Bounds while also being smaller than the
Relevant Upper Bound.
With these definitions, the Relevant Lower Bound is always smaller
than the Relevant Upper Bound (if both exist), and the two relevant
bounds are used analogously as the two tightest bounds in the Binary
Search (Section 2.1). When they meet the stopping conditions, the
Relevant Bounds are used in the output.
5.3.4. Consequences
The consequence of the way the Relevant Bounds are defined is that
every Trial Result can have an impact on any current Relevant Bound
larger than that Trial Load, namely by becoming a new Upper Bound.
This also applies when that Load is measured before another Load gets
enough measurements to become a current Relevant Bound.
This also implies that if the SUT tested (or the Traffic Generator
used) needs a warm-up, it should be warmed up before starting the
Search, otherwise the first few measurements could become unjustly
limiting.
For MLRsearch implementations, it means it is better to measure at
smaller Loads first, so bounds found earlier are less likely to get
invalidated later.
5.4. Exceed Ratio and Multiple Trials
The idea of performing multiple Trials at the same Trial Load comes
from a model where some Trial Results (those with high Trial Loss
Ratio) are affected by infrequent effects, causing unsatisfactory
repeatability
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of [RFC2544] Throughput results. Refer to DUT in SUT (Section 2.3)
for a discussion about noiseful and noiseless ends of the SUT
performance spectrum. Stable results are closer to the noiseless end
of the SUT performance spectrum, so MLRsearch may need to allow some
frequency of high-loss trials to ignore the rare but big effects near
the noiseful end.
For MLRsearch to perform such Trial Result filtering, it needs a
configuration option to tell how frequent the "infrequent" big loss
can be. This option is called the Goal Exceed Ratio (Section 4.6.4).
It tells MLRsearch what ratio of trials (more specifically, what
ratio of Trial Effective Duration seconds) can have a Trial Loss
Ratio (Section 4.5.6) larger than the Goal Loss Ratio (Section 4.6.3)
and still be classified as a Lower Bound (Section 4.7.2.2).
Zero exceed ratio means all Trials must have a Trial Loss Ratio equal
to or lower than the Goal Loss Ratio.
When more than one Trial is intended to classify a Load, MLRsearch
also needs something that controls the number of trials needed.
Therefore, each goal also has an attribute called Goal Duration Sum.
The meaning of a Goal Duration Sum (Section 4.6.2) is that when a
Load has (Full-Length) Trials whose Trial Effective Durations when
summed up give a value at least as big as the Goal Duration Sum
value, the Load is guaranteed to be classified either as an Upper
Bound or a Lower Bound for that Search Goal instance.
5.5. Short Trials and Duration Selection
MLRsearch requires each Search Goal to specify its Goal Final Trial
Duration.
Section 24 of [RFC2544] already anticipates possible time savings
when Short Trials are used.
An MLRsearch implementation MAY expose configuration parameters that
decide whether, when, and how short trial durations are used. The
exact heuristics and controls are left to the discretion of the
implementer.
While MLRsearch implementations are free to use any logic to select
Trial Input values, comparability between MLRsearch implementations
is only assured when the Load Classification logic handles any
possible set of Trial Results in the same way.
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The presence of Short Trial Results complicates the Load
Classification logic, see more details in Load Classification Logic
(Section 6.1).
While the Load Classification algorithm is designed to avoid any
unneeded Trials, for explainability reasons it is recommended for
users to use such Controller Input instances that lead to all Trial
Duration values selected by Controller to be the same, e.g., by
setting any Goal Initial Trial Duration to be a single value also
used in all Goal Final Trial Duration attributes.
5.6. Generalized Throughput
Because testing equipment takes the Intended Load as an input
parameter for a Trial measurement, any load search algorithm needs to
deal with Intended Load values internally.
But in the presence of Search Goals with a non-zero Goal Loss Ratio
(Section 4.6.3), the Load usually does not match the user's intuition
of what a throughput is. The forwarding rate as defined in
Section 3.6.1 of [RFC2285] is better, but it is not obvious how to
generalize it for Loads with multiple Trials and a non-zero Goal Loss
Ratio.
The clearest illustration - and the chief reason for adopting a
generalized throughput definition - is the presence of a hard
performance limit.
5.6.1. Hard Performance Limit
Even if bandwidth of a medium allows higher traffic forwarding
performance, the SUT interfaces may have their additional own
limitations, e.g., a specific frames-per-second limit on the NIC (a
common occurrence).
Those limitations should be known and provided as Max Load
(Section 4.6.8.1).
But if Max Load is set larger than what the interface can receive or
transmit, there will be a "hard limit" behavior observed in Trial
Results.
Consider that the hard limit is at hundred million frames per second
(100 Mfps), Max Load is larger, and the Goal Loss Ratio is 0.5%. If
DUT has no additional losses, 0.5% Trial Loss Ratio will be achieved
at Relevant Lower Bound of 100.5025 Mfps.
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Reporting a throughput that exceeds the SUT's verified hard limit is
counter-intuitive. Accordingly, the [RFC2544] Throughput metric
should be generalized - rather than relying solely on the Relevant
Lower Bound - to reflect realistic, limit-aware performance.
MLRsearch defines one such generalization, the Conditional Throughput
(Section 4.8.3). It is the Trial Forwarding Rate from one of the
Full-Length Trials performed at the Relevant Lower Bound. The
algorithm to determine which trial exactly is in conditional
throughput code (Appendix B).
In the hard limit example, 100.5025 Mfps Load will still have only
100.0 Mfps forwarding rate, nicely confirming the known limitation.
5.6.2. Performance Variability
With non-zero Goal Loss Ratio, and without hard performance limits,
Low-Loss trials at the same Load may achieve different Trial
Forwarding Rate values just due to DUT performance variability.
By comparing the best case (all Relevant Lower Bound trials have zero
loss) and the worst case (all Trial Loss Ratios at Relevant Lower
Bound are equal to the Goal Loss Ratio), one can prove that
Conditional Throughput values may have up to the Goal Loss Ratio
relative difference.
Setting the Goal Width below the Goal Loss Ratio may cause the
Conditional Throughput for a larger Goal Loss Ratio to become smaller
than a Conditional Throughput for a goal with a lower Goal Loss
Ratio, which is counter-intuitive, considering they come from the
same Search. Therefore, it is RECOMMENDED to set the Goal Width to a
value no lower than the Goal Loss Ratio of the higher-loss Search
Goal.
Although Conditional Throughput can fluctuate from one run to the
next, it still offers a more discriminating basis for comparison than
the Relevant Lower Bound - particularly when deterministic load
selection yields the same Lower Bound value across multiple runs.
6. MLRsearch Logic and Example
This section uses informal language to describe two aspects of
MLRsearch logic: Load Classification and Conditional Throughput,
reflecting formal pseudocode representation provided in load
classification code (Appendix A) and conditional throughput code
(Appendix B). This is followed by example search.
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The logic is equivalent but not identical to the pseudocode on
appendices. The pseudocode is designed to be short and frequently
combines multiple operations into one expression. The logic as
described in this section lists each operation separately and uses
more intuitive names for the intermediate values.
6.1. Load Classification Logic
Note: For clarity of explanation, variables are tagged as (I)nput,
(T)emporary, (O)utput.
* Collect Trial Results:
- Take all Trial Result instances (I) measured at a given load.
* Aggregate Trial Durations:
- Full-length high-loss sum (T) is the sum of Trial Effective
Duration values of all full-length high-loss trials (I).
- Full-length low-loss sum (T) is the sum of Trial Effective
Duration values of all full-length low-loss trials (I).
- Short high-loss sum is the sum (T) of Trial Effective Duration
values of all short high-loss trials (I).
- Short low-loss sum is the sum (T) of Trial Effective Duration
values of all short low-loss trials (I).
* Derive goal-based ratios:
- Subceed ratio (T) is One minus the Goal Exceed Ratio (I).
- Exceed coefficient (T) is the Goal Exceed Ratio divided by the
subceed ratio.
* Balance short-trial effects:
- Balancing sum (T) is the short low-loss sum multiplied by the
exceed coefficient.
- Excess sum (T) is the short high-loss sum minus the balancing
sum.
- Positive excess sum (T) is the maximum of zero and excess sum.
* Compute effective duration totals
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- Effective high-loss sum (T) is the full-length high-loss sum
plus the positive excess sum.
- Effective full sum (T) is the effective high-loss sum plus the
full-length low-loss sum.
- Effective whole sum (T) is the larger of the effective full sum
and the Goal Duration Sum.
- Missing sum (T) is the effective whole sum minus the effective
full sum.
* Estimate exceed ratios:
- Pessimistic high-loss sum (T) is the effective high-loss sum
plus the missing sum.
- Optimistic exceed ratio (T) is the effective high-loss sum
divided by the effective whole sum.
- Pessimistic exceed ratio (T) is the pessimistic high-loss sum
divided by the effective whole sum.
* Classify the Load:
- The load is classified as an Upper Bound (O) if the optimistic
exceed ratio is larger than the Goal Exceed Ratio.
- The load is classified as a Lower Bound (O) if the pessimistic
exceed ratio is not larger than the Goal Exceed Ratio.
- The load is classified as undecided (O) otherwise.
6.2. Conditional Throughput Logic
* Collect Trial Results
- Take all Trial Result instances (I) measured at a given Load.
* Sum Full-Length Durations:
- Full-length high-loss sum (T) is the sum of Trial Effective
Duration values of all full-length high-loss trials (I).
- Full-length low-loss sum (T) is the sum of Trial Effective
Duration values of all full-length low-loss trials (I).
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- Full-length sum (T) is the full-length high-loss sum (I) plus
the full-length low-loss sum (I).
* Derive initial thresholds:
- Subceed ratio (T) is One minus the Goal Exceed Ratio (I) is
called.
- Remaining sum (T) initially is full-lengths sum multiplied by
subceed ratio.
- Current loss ratio (T) initially is 100%.
* Iterate through ordered trials
- For each full-length trial result, sorted in increasing order
by Trial Loss Ratio:
o If remaining sum is not larger than zero, exit the loop.
o Set current loss ratio to this trial's Trial Loss Ratio (I).
o Decrease the remaining sum by this trial's Trial Effective
Duration (I).
* Compute Conditional Throughput
- Current forwarding ratio (T) is One minus the current loss
ratio.
- Conditional Throughput (T) is the current forwarding ratio
multiplied by the Load value.
6.2.1. Conditional Throughput and Load Classification
Conditional Throughput and results of Load Classification overlap but
are not identical.
* When a load is marked as a Relevant Lower Bound, its Conditional
Throughput is taken from a trial whose loss ratio never exceeds
the Goal Loss Ratio.
* The reverse is not guaranteed: if the Goal Width is narrower than
the Goal Loss Ratio, Conditional Throughput can still end up
higher than the Relevant Upper Bound.
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6.3. SUT Behaviors
In DUT in SUT (Section 2.3), the notion of noise has been introduced.
This section uses new terms to describe possible SUT behaviors more
precisely.
From measurement point of view, noise is visible as inconsistent
trial results. See Inconsistent Trial Results (Section 2.6) for
general points and Loss Ratios and Loss Inversion (Section 5.3) for
specifics when comparing different Load values.
Load Classification and Conditional Throughput apply to a single Load
value, but even the set of Trial Results measured at that Trial Load
value may appear inconsistent.
As MLRsearch aims to save time, it executes only a small number of
Trials, getting only a limited amount of information about SUT
behavior. It is useful to introduce an "SUT expert" point of view to
contrast with that limited information.
6.3.1. Expert Predictions
Imagine that before the Search starts, a human expert had unlimited
time to measure SUT and obtain all reliable information about it.
The information is not perfect, as there is still random noise
influencing SUT. But the expert is familiar with possible noise
events, even the rare ones, and thus the expert can do probabilistic
predictions about future Trial Outputs.
When several outcomes are possible, the expert can assess probability
of each outcome.
6.3.2. Exceed Probability
When the Controller selects new Trial Duration and Trial Load, and
just before the Measurer starts performing the Trial, the SUT expert
can envision possible Trial Results.
With respect to a particular Search Goal instance, the possibilities
can be summarized into a single number: Exceed Probability. It is
the probability (according to the expert) that the measured Trial
Loss Ratio will be higher than the Goal Loss Ratio.
6.3.3. Trial Duration Dependence
When comparing Exceed Probability values for the same Trial Load
value but different Trial Duration values, there are several patterns
that commonly occur in practice.
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6.3.3.1. Strong Increase
Exceed Probability is very low at short durations but very high at
full-length. This SUT behavior is undesirable, and may hint at
faulty SUT, e.g., SUT leaks resources and is unable to sustain the
desired performance.
But this behavior is also seen when SUT uses large amount of buffers.
This is the main reasons users may want to set large Goal Final Trial
Duration.
6.3.3.2. Mild Increase
Short trials are slightly less likely to exceed the loss-ratio limit,
but the improvement is modest. This mild benefit is typical when
noise is dominated by rare, large loss spikes: during a full-length
trial, the good-performing periods cannot fully offset the heavy
frame loss that occurs in the brief low-performing bursts.
6.3.3.3. Independence
Short trials have basically the same Exceed Probability as full-
length trials. This is possible only if loss spikes are small (so
other parts can compensate) and if Goal Loss Ratio is more than zero
(otherwise, other parts cannot compensate at all).
6.3.3.4. Decrease
Short trials have larger Exceed Probability than full-length trials.
This can be possible only for non-zero Goal Loss Ratio, for example
if SUT needs to "warm up" to best performance within each trial. Not
commonly seen in practice.
7. IANA Considerations
This document does not make any request to IANA.
8. Security Considerations
Benchmarking activities as described in this memo are limited to
technology characterization of a DUT/SUT using controlled stimuli in
a laboratory environment, with dedicated address space and the
constraints specified in the sections above.
The benchmarking network topology will be an independent test setup
and MUST NOT be connected to devices that may forward the test
traffic into a production network or misroute traffic to the test
management network.
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Further, benchmarking is performed on an "opaque" basis, relying
solely on measurements observable external to the DUT/SUT.
The DUT/SUT SHOULD NOT include features that serve only to boost
benchmark scores - such as a dedicated "fast-track" test mode that is
never used in normal operation.
Any implications for network security arising from the DUT/SUT SHOULD
be identical in the lab and in production networks.
9. Acknowledgements
Special wholehearted gratitude and thanks to the late Al Morton for
his thorough reviews filled with very specific feedback and
constructive guidelines. Thank You Al for the close collaboration
over the years, Your Mentorship, Your continuous unwavering
encouragement full of empathy and energizing positive attitude. Al,
You are dearly missed.
Thanks to Gabor Lencse, Giuseppe Fioccola, Carsten Rossenhoevel and
BMWG contributors for good discussions and thorough reviews, guiding
and helping us to improve the clarity and formality of this document.
Many thanks to Alec Hothan of the OPNFV NFVbench project for a
thorough review and numerous useful comments and suggestions in the
earlier versions of this document.
We are equally indebted to Mohamed Boucadair for a very thorough and
detailed AD review and providing many good comments and suggestions,
helping us make this document complete.
Our appreciation is also extended to Shawn Emery, Yoshifumi Nishida,
David Dong, Nabeel Cocker, Lars Eggert, Jen Linkova, Mike Bishop and
Eric Vyncke for their reviews and valuable comments.
10. References
10.1. Normative References
[RFC1242] Bradner, S., "Benchmarking Terminology for Network
Interconnection Devices", RFC 1242, DOI 10.17487/RFC1242,
July 1991, <https://www.rfc-editor.org/info/rfc1242>.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
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[RFC2285] Mandeville, R., "Benchmarking Terminology for LAN
Switching Devices", RFC 2285, DOI 10.17487/RFC2285,
February 1998, <https://www.rfc-editor.org/info/rfc2285>.
[RFC2544] Bradner, S. and J. McQuaid, "Benchmarking Methodology for
Network Interconnect Devices", RFC 2544,
DOI 10.17487/RFC2544, March 1999,
<https://www.rfc-editor.org/info/rfc2544>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
10.2. Informative References
[FDio-CSIT-MLRsearch]
"FD.io CSIT Test Methodology - MLRsearch", October 2023,
<https://csit.fd.io/cdocs/methodology/measurements/
data_plane_throughput/mlr_search/>.
[Lencze-Kovacs-Shima]
"Gaming with the Throughput and the Latency Benchmarking
Measurement Procedures of RFC 2544", n.d.,
<http://dx.doi.org/10.11601/ijates.v9i2.288>.
[Lencze-Shima]
"An Upgrade to Benchmarking Methodology for Network
Interconnect Devices - expired", n.d.,
<https://datatracker.ietf.org/doc/html/draft-lencse-bmwg-
rfc2544-bis-00>.
[Ott-Mathis-Semke-Mahdavi]
"The Macroscopic Behavior of the TCP Congestion Avoidance
Algorithm", n.d.,
<https://www.cs.cornell.edu/people/egs/cornellonly/
syslunch/fall02/ott.pdf>.
[PyPI-MLRsearch]
"MLRsearch 1.2.1, Python Package Index", October 2023,
<https://pypi.org/project/MLRsearch/1.2.1/>.
[RFC5180] Popoviciu, C., Hamza, A., Van de Velde, G., and D.
Dugatkin, "IPv6 Benchmarking Methodology for Network
Interconnect Devices", RFC 5180, DOI 10.17487/RFC5180, May
2008, <https://www.rfc-editor.org/info/rfc5180>.
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[RFC6349] Constantine, B., Forget, G., Geib, R., and R. Schrage,
"Framework for TCP Throughput Testing", RFC 6349,
DOI 10.17487/RFC6349, August 2011,
<https://www.rfc-editor.org/info/rfc6349>.
[RFC6985] Morton, A., "IMIX Genome: Specification of Variable Packet
Sizes for Additional Testing", RFC 6985,
DOI 10.17487/RFC6985, July 2013,
<https://www.rfc-editor.org/info/rfc6985>.
[RFC8219] Georgescu, M., Pislaru, L., and G. Lencse, "Benchmarking
Methodology for IPv6 Transition Technologies", RFC 8219,
DOI 10.17487/RFC8219, August 2017,
<https://www.rfc-editor.org/info/rfc8219>.
[TST009] "TST 009", n.d., <https://www.etsi.org/deliver/etsi_gs/
NFV-TST/001_099/009/03.04.01_60/gs_NFV-
TST009v030401p.pdf>.
[Vassilev] "A YANG Data Model for Network Tester Management", n.d.,
<https://datatracker.ietf.org/doc/draft-ietf-bmwg-network-
tester-cfg/06>.
[Y.1564] "Y.1564", n.d., <https://www.itu.int/rec/
dologin_pub.asp?lang=e&id=T-REC-Y.1564-201602-I!!PDF-
E&type=items>.
Appendix A. Load Classification Code
This appendix specifies how to perform the Load Classification.
Any Trial Load value can be classified, according to a given Search
Goal (Section 4.6.7) instance.
The algorithm uses (some subsets of) the set of all available Trial
Results from Trials measured at a given Load at the end of the
Search.
The block at the end of this appendix holds pseudocode which computes
two values, stored in variables named optimistic_is_lower and
pessimistic_is_lower.
Although presented as pseudocode, the listing is syntactically valid
Python and can be executed without modification.
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If values of both variables are computed to be true, the Load in
question is classified as a Lower Bound according to the given Search
Goal instance. If values of both variables are false, the Load is
classified as an Upper Bound. Otherwise, the load is classified as
Undecided.
Some variable names are shortened to fit expressions in one line.
Namely, variables holding sum quantities end in _s instead of _sum,
and variables holding effective quantities start in effect_ instead
of effective_.
The pseudocode expects the following variables to hold the following
values:
* goal_duration_s: The Goal Duration Sum value of the given Search
Goal.
* goal_exceed_ratio: The Goal Exceed Ratio value of the given Search
Goal.
* full_length_low_loss_s: Sum of Trial Effective Durations across
Trials with Trial Duration at least equal to the Goal Final Trial
Duration and with Trial Loss Ratio not higher than the Goal Loss
Ratio (across Full-Length Low-Loss Trials).
* full_length_high_loss_s: Sum of Trial Effective Durations across
Trials with Trial Duration at least equal to the Goal Final Trial
Duration and with Trial Loss Ratio higher than the Goal Loss Ratio
(across Full-Length High-Loss Trials).
* short_low_loss_s: Sum of Trial Effective Durations across Trials
with Trial Duration shorter than the Goal Final Trial Duration and
with Trial Loss Ratio not higher than the Goal Loss Ratio (across
Short Low-Loss Trials).
* short_high_loss_s: Sum of Trial Effective Durations across Trials
with Trial Duration shorter than the Goal Final Trial Duration and
with Trial Loss Ratio higher than the Goal Loss Ratio (across
Short High-Loss Trials).
The code works correctly also when there are no Trial Results at a
given Load.
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<CODE BEGINS>
exceed_coefficient = goal_exceed_ratio / (1.0 - goal_exceed_ratio)
balancing_s = short_low_loss_s * exceed_coefficient
positive_excess_s = max(0.0, short_high_loss_s - balancing_s)
effect_high_loss_s = full_length_high_loss_s + positive_excess_s
effect_full_length_s = full_length_low_loss_s + effect_high_loss_s
effect_whole_s = max(effect_full_length_s, goal_duration_s)
quantile_duration_s = effect_whole_s * goal_exceed_ratio
pessimistic_high_loss_s = effect_whole_s - full_length_low_loss_s
pessimistic_is_lower = pessimistic_high_loss_s <= quantile_duration_s
optimistic_is_lower = effect_high_loss_s <= quantile_duration_s
<CODE ENDS>
Appendix B. Conditional Throughput Code
This section specifies an example of how to compute Conditional
Throughput, as referred to in Conditional Throughput (Section 4.8.3).
Any Load value can be used as the basis for the following
computation, but only the Relevant Lower Bound (at the end of the
Search) leads to the value called the Conditional Throughput for a
given Search Goal.
The algorithm uses (some subsets of) the set of all available Trial
Results from Trials measured at a given Load at the end of the
Search.
The block at the end of this appendix holds pseudocode which computes
a value stored as variable conditional_throughput.
Although presented as pseudocode, the listing is syntactically valid
Python and can be executed without modification.
Some variable names are shortened in order to fit expressions in one
line. Namely, variables holding sum quantities end in _s instead of
_sum, and variables holding effective quantities start in effect_
instead of effective_.
The pseudocode expects the following variables to hold the following
values:
* goal_duration_s: The Goal Duration Sum value of the given Search
Goal.
* goal_exceed_ratio: The Goal Exceed Ratio value of the given Search
Goal.
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* full_length_low_loss_s: Sum of Trial Effective Durations across
Trials with Trial Duration at least equal to the Goal Final Trial
Duration and with Trial Loss Ratio not higher than the Goal Loss
Ratio (across Full-Length Low-Loss Trials).
* full_length_high_loss_s: Sum of Trial Effective Durations across
Trials with Trial Duration at least equal to the Goal Final Trial
Duration and with Trial Loss Ratio higher than the Goal Loss Ratio
(across Full-Length High-Loss Trials).
* full_length_trials: An iterable of all Trial Results from Trials
with Trial Duration at least equal to the Goal Final Trial
Duration (all Full-Length Trials), sorted by increasing Trial Loss
Ratio. One item trial is a composite with the following two
attributes available:
- trial.loss_ratio: The Trial Loss Ratio as measured for this
Trial.
- trial.effect_duration: The Trial Effective Duration of this
Trial.
The code works correctly only when there is at least one Trial Result
measured at a given Load.
<CODE BEGINS>
full_length_s = full_length_low_loss_s + full_length_high_loss_s
whole_s = max(goal_duration_s, full_length_s)
remaining = whole_s * (1.0 - goal_exceed_ratio)
quantile_loss_ratio = None
for trial in full_length_trials:
if quantile_loss_ratio is None or remaining > 0.0:
quantile_loss_ratio = trial.loss_ratio
remaining -= trial.effect_duration
else:
break
else:
if remaining > 0.0:
quantile_loss_ratio = 1.0
conditional_throughput = intended_load * (1.0 - quantile_loss_ratio)
<CODE ENDS>
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Appendix C. Example Search
The following example Search is related to one hypothetical run of a
Search test procedure that has been started with multiple Search
Goals. Several points in time are chosen, to show how the logic
works, with specific sets of Trial Result available. The trial
results themselves are not very realistic, as the intention is to
show several corner cases of the logic.
In all Trials, the Effective Trial Duration is equal to Trial
Duration.
Only one Trial Load is in focus, its value is one million frames per
second. Trial Results at other Trial Loads are not mentioned, as the
parts of logic present here do not depend on those. In practice,
Trial Results at other Load values would be present, e.g., MLRsearch
will look for a Lower Bound smaller than any Upper Bound found.
At any given moment, exactly one Search Goal is designated as in
focus. This designation affects only the Trial Duration chosen for
new trials; it does not alter the rest of the decision logic.
An MLRsearch implementation is free to evaluate several goals
simultaneously - the "focus" mechanism is optional and appears here
only to show that a load can still be classified against goals that
are not currently in focus.
C.1. Example Goals
The following four Search Goal instances are selected for the example
Search. Each goal has a readable name and dense code, the code is
useful to show Search Goal attribute values.
As the variable "exceed coefficient" does not depend on trial
results, it is also precomputed here.
Goal 1:
name: RFC2544
Goal Final Trial Duration: 60s
Goal Duration Sum: 60s
Goal Loss Ratio: 0%
Goal Exceed Ratio: 0%
exceed coefficient: 0% / (100% / 0%) = 0.0
code: 60f60d0l0e
Goal 2:
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name: TST009
Goal Final Trial Duration: 60s
Goal Duration Sum: 120s
Goal Loss Ratio: 0%
Goal Exceed Ratio: 50%
exceed coefficient: 50% / (100% - 50%) = 1.0
code: 60f120d0l50e
Goal 3:
name: 1s final
Goal Final Trial Duration: 1s
Goal Duration Sum: 120s
Goal Loss Ratio: 0.5%
Goal Exceed Ratio: 50%
exceed coefficient: 50% / (100% - 50%) = 1.0
code: 1f120d.5l50e
Goal 4:
name: 20% exceed
Goal Final Trial Duration: 60s
Goal Duration Sum: 60s
Goal Loss Ratio: 0.5%
Goal Exceed Ratio: 20%
exceed coefficient: 20% / (100% - 20%) = 0.25
code: 60f60d0.5l20e
The first two goals are important for compliance reasons, the other
two cover less frequent cases.
C.2. Example Trial Results
The following six sets of trial results are selected for the example
Search. The sets are defined as points in time, describing which
Trial Results were added since the previous point.
Each point has a readable name and dense code, the code is useful to
show Trial Output attribute values and number of times identical
results were added.
Point 1:
name: first short good
goal in focus: 1s final (1f120d.5l50e)
added Trial Results: 59 trials, each 1 second and 0% loss
code: 59x1s0l
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Point 2:
name: first short bad
goal in focus: 1s final (1f120d.5l50e)
added Trial Result: one trial, 1 second, 1% loss
code: 59x1s0l+1x1s1l
Point 3:
name: last short bad
goal in focus: 1s final (1f120d.5l50e)
added Trial Results: 59 trials, 1 second each, 1% loss each
code: 59x1s0l+60x1s1l
Point 4:
name: last short good
goal in focus: 1s final (1f120d.5l50e)
added Trial Results: one trial 1 second, 0% loss
code: 60x1s0l+60x1s1l
Point 5:
name: first long bad
goal in focus: TST009 (60f120d0l50e)
added Trial Results: one trial, 60 seconds, 0.1% loss
code: 60x1s0l+60x1s1l+1x60s.1l
Point 6:
name: first long good
goal in focus: TST009 (60f120d0l50e)
added Trial Results: one trial, 60 seconds, 0% loss
code: 60x1s0l+60x1s1l+1x60s.1l+1x60s0l
Comments on point in time naming:
* When a name contains "short", it means the added trial had Trial
Duration of 1 second, which is Short Trial for 3 of the Search
Goals, but it is a Full-Length Trial for the "1s final" goal.
* Similarly, "long" in name means the added trial had Trial Duration
of 60 seconds, which is Full-Length Trial for 3 goals but Long
Trial for the "1s final" goal.
* When a name contains "good" it means the added trial is Low-Loss
Trial for all the goals.
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* When a name contains "short bad" it means the added trial is High-
Loss Trial for all the goals.
* When a name contains "long bad", it means the added trial is a
High-Loss Trial for goals "RFC2544" and "TST009", but it is a Low-
Loss Trial for the two other goals.
C.3. Load Classification Computations
This section shows how Load Classification logic is applied by
listing all temporary values at the specific time point.
C.3.1. Point 1
This is the "first short good" point. Code for available results is:
59x1s0l
+==============+==========+============+============+=============+
|Goal name |RFC2544 |TST009 |1s final |20% exceed |
+==============+==========+============+============+=============+
|Goal code |60f60d0l0e|60f120d0l50e|1f120d.5l50e|60f60d0.5l20e|
+--------------+----------+------------+------------+-------------+
|Full-length |0s |0s |0s |0s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Full-length |0s |0s |59s |0s |
|low-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Short high- |0s |0s |0s |0s |
|loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Short low-loss|59s |59s |0s |59s |
|sum | | | | |
+--------------+----------+------------+------------+-------------+
|Balancing sum |0s |59s |0s |14.75s |
+--------------+----------+------------+------------+-------------+
|Excess sum |0s |-59s |0s |-14.75s |
+--------------+----------+------------+------------+-------------+
|Positive |0s |0s |0s |0s |
|excess sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective |0s |0s |0s |0s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective full|0s |0s |59s |0s |
|sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective |60s |120s |120s |60s |
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|whole sum | | | | |
+--------------+----------+------------+------------+-------------+
|Missing sum |60s |120s |61s |60s |
+--------------+----------+------------+------------+-------------+
|Pessimistic |60s |120s |61s |60s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Optimistic |0% |0% |0% |0% |
|exceed ratio | | | | |
+--------------+----------+------------+------------+-------------+
|Pessimistic |100% |100% |50.833% |100% |
|exceed ratio | | | | |
+--------------+----------+------------+------------+-------------+
|Classification|Undecided |Undecided |Undecided |Undecided |
|Result | | | | |
+--------------+----------+------------+------------+-------------+
Table 1
This is the last point in time where all goals have this load as
Undecided.
C.3.2. Point 2
This is the "first short bad" point. Code for available results is:
59x1s0l+1x1s1l
+==============+==========+============+============+=============+
|Goal name |RFC2544 |TST009 |1s final |20% exceed |
+==============+==========+============+============+=============+
|Goal code |60f60d0l0e|60f120d0l50e|1f120d.5l50e|60f60d0.5l20e|
+--------------+----------+------------+------------+-------------+
|Full-length |0s |0s |1s |0s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Full-length |0s |0s |59s |0s |
|low-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Short high- |1s |1s |0s |1s |
|loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Short low-loss|59s |59s |0s |59s |
|sum | | | | |
+--------------+----------+------------+------------+-------------+
|Balancing sum |0s |59s |0s |14.75s |
+--------------+----------+------------+------------+-------------+
|Excess sum |1s |-58s |0s |-13.75s |
+--------------+----------+------------+------------+-------------+
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|Positive |1s |0s |0s |0s |
|excess sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective |1s |0s |1s |0s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective full|1s |0s |60s |0s |
|sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective |60s |120s |120s |60s |
|whole sum | | | | |
+--------------+----------+------------+------------+-------------+
|Missing sum |59s |120s |60s |60s |
+--------------+----------+------------+------------+-------------+
|Pessimistic |60s |120s |61s |60s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Optimistic |1.667% |0% |0.833% |0% |
|exceed ratio | | | | |
+--------------+----------+------------+------------+-------------+
|Pessimistic |100% |100% |50.833% |100% |
|exceed ratio | | | | |
+--------------+----------+------------+------------+-------------+
|Classification|Upper |Undecided |Undecided |Undecided |
|Result |Bound | | | |
+--------------+----------+------------+------------+-------------+
Table 2
Due to zero Goal Loss Ratio, RFC2544 goal must have mild or strong
increase of exceed probability, so the one lossy trial would be lossy
even if measured at 60 second duration. Due to zero exceed ratio,
one High-Loss Trial is enough to preclude this Load from becoming a
Lower Bound for RFC2544. That is why this Load is classified as an
Upper Bound for RFC2544 this early.
This is an example how significant time can be saved, compared to
60-second trials.
C.3.3. Point 3
This is the "last short bad" point. Code for available trial results
is: 59x1s0l+60x1s1l
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+==============+==========+============+============+=============+
|Goal name |RFC2544 |TST009 |1s final |20% exceed |
+==============+==========+============+============+=============+
|Goal code |60f60d0l0e|60f120d0l50e|1f120d.5l50e|60f60d0.5l20e|
+--------------+----------+------------+------------+-------------+
|Full-length |0s |0s |60s |0s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Full-length |0s |0s |59s |0s |
|low-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Short high- |60s |60s |0s |60s |
|loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Short low-loss|59s |59s |0s |59s |
|sum | | | | |
+--------------+----------+------------+------------+-------------+
|Balancing sum |0s |59s |0s |14.75s |
+--------------+----------+------------+------------+-------------+
|Excess sum |60s |1s |0s |45.25s |
+--------------+----------+------------+------------+-------------+
|Positive |60s |1s |0s |45.25s |
|excess sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective |60s |1s |60s |45.25s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective full|60s |1s |119s |45.25s |
|sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective |60s |120s |120s |60s |
|whole sum | | | | |
+--------------+----------+------------+------------+-------------+
|Missing sum |0s |119s |1s |14.75s |
+--------------+----------+------------+------------+-------------+
|Pessimistic |60s |120s |61s |60s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Optimistic |100% |0.833% |50% |75.417% |
|exceed ratio | | | | |
+--------------+----------+------------+------------+-------------+
|Pessimistic |100% |100% |50.833% |100% |
|exceed ratio | | | | |
+--------------+----------+------------+------------+-------------+
|Classification|Upper |Undecided |Undecided |Upper Bound |
|Result |Bound | | | |
+--------------+----------+------------+------------+-------------+
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Table 3
This is the last point for "1s final" goal to have this Load still
Undecided. Only one 1-second trial is missing within the 120-second
Goal Duration Sum, but its result will decide the classification
result.
The "20% exceed" started to classify this load as an Upper Bound
somewhere between points 2 and 3.
C.3.4. Point 4
This is the "last short good" point. Code for available trial
results is: 60x1s0l+60x1s1l
+==============+==========+============+============+=============+
|Goal name |RFC2544 |TST009 |1s final |20% exceed |
+==============+==========+============+============+=============+
|Goal code |60f60d0l0e|60f120d0l50e|1f120d.5l50e|60f60d0.5l20e|
+--------------+----------+------------+------------+-------------+
|Full-length |0s |0s |60s |0s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Full-length |0s |0s |60s |0s |
|low-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Short high- |60s |60s |0s |60s |
|loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Short low-loss|60s |60s |0s |60s |
|sum | | | | |
+--------------+----------+------------+------------+-------------+
|Balancing sum |0s |60s |0s |15s |
+--------------+----------+------------+------------+-------------+
|Excess sum |60s |0s |0s |45s |
+--------------+----------+------------+------------+-------------+
|Positive |60s |0s |0s |45s |
|excess sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective |60s |0s |60s |45s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective full|60s |0s |120s |45s |
|sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective |60s |120s |120s |60s |
|whole sum | | | | |
+--------------+----------+------------+------------+-------------+
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|Missing sum |0s |120s |0s |15s |
+--------------+----------+------------+------------+-------------+
|Pessimistic |60s |120s |60s |60s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Optimistic |100% |0% |50% |75% |
|exceed ratio | | | | |
+--------------+----------+------------+------------+-------------+
|Pessimistic |100% |100% |50% |100% |
|exceed ratio | | | | |
+--------------+----------+------------+------------+-------------+
|Classification|Upper |Undecided |Lower Bound |Upper Bound |
|Result |Bound | | | |
+--------------+----------+------------+------------+-------------+
Table 4
The one missing trial for "1s final" was Low-Loss, half of trial
results are Low-Loss which exactly matches 50% exceed ratio. This
shows time savings are not guaranteed.
C.3.5. Point 5
This is the "first long bad" point. Code for available trial results
is: 60x1s0l+60x1s1l+1x60s.1l
+==============+==========+============+============+=============+
|Goal name |RFC2544 |TST009 |1s final |20% exceed |
+==============+==========+============+============+=============+
|Goal code |60f60d0l0e|60f120d0l50e|1f120d.5l50e|60f60d0.5l20e|
+--------------+----------+------------+------------+-------------+
|Full-length |60s |60s |60s |0s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Full-length |0s |0s |120s |60s |
|low-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Short high- |60s |60s |0s |60s |
|loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Short low-loss|60s |60s |0s |60s |
|sum | | | | |
+--------------+----------+------------+------------+-------------+
|Balancing sum |0s |60s |0s |15s |
+--------------+----------+------------+------------+-------------+
|Excess sum |60s |0s |0s |45s |
+--------------+----------+------------+------------+-------------+
|Positive |60s |0s |0s |45s |
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|excess sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective |120s |60s |60s |45s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective full|120s |60s |180s |105s |
|sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective |120s |120s |180s |105s |
|whole sum | | | | |
+--------------+----------+------------+------------+-------------+
|Missing sum |0s |60s |0s |0s |
+--------------+----------+------------+------------+-------------+
|Pessimistic |120s |120s |60s |45s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Optimistic |100% |50% |33.333% |42.857% |
|exceed ratio | | | | |
+--------------+----------+------------+------------+-------------+
|Pessimistic |100% |100% |33.333% |42.857% |
|exceed ratio | | | | |
+--------------+----------+------------+------------+-------------+
|Classification|Upper |Undecided |Lower Bound |Lower Bound |
|Result |Bound | | | |
+--------------+----------+------------+------------+-------------+
Table 5
As designed for TST009 goal, one Full-Length High-Loss Trial can be
tolerated. 120s worth of 1-second trials is not useful, as this is
allowed when Exceed Probability does not depend on Trial Duration.
As Goal Loss Ratio is zero, it is not possible for 60-second trials
to compensate for losses seen in 1-second results. But Load
Classification logic does not have that knowledge hardcoded, so
optimistic exceed ratio is still only 50%.
But the 0.1% Trial Loss Ratio is lower than "20% exceed" Goal Loss
Ratio, so this unexpected Full-Length Low-Loss trial changed the
classification result of this Load to Lower Bound.
C.3.6. Point 6
This is the "first long good" point. Code for available trial
results is: 60x1s0l+60x1s1l+1x60s.1l+1x60s0l
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+==============+==========+============+============+=============+
|Goal name |RFC2544 |TST009 |1s final |20% exceed |
+==============+==========+============+============+=============+
|Goal code |60f60d0l0e|60f120d0l50e|1f120d.5l50e|60f60d0.5l20e|
+--------------+----------+------------+------------+-------------+
|Full-length |60s |60s |60s |0s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Full-length |60s |60s |180s |120s |
|low-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Short high- |60s |60s |0s |60s |
|loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Short low-loss|60s |60s |0s |60s |
|sum | | | | |
+--------------+----------+------------+------------+-------------+
|Balancing sum |0s |60s |0s |15s |
+--------------+----------+------------+------------+-------------+
|Excess sum |60s |0s |0s |45s |
+--------------+----------+------------+------------+-------------+
|Positive |60s |0s |0s |45s |
|excess sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective |120s |60s |60s |45s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective full|180s |120s |240s |165s |
|sum | | | | |
+--------------+----------+------------+------------+-------------+
|Effective |180s |120s |240s |165s |
|whole sum | | | | |
+--------------+----------+------------+------------+-------------+
|Missing sum |0s |0s |0s |0s |
+--------------+----------+------------+------------+-------------+
|Pessimistic |120s |60s |60s |45s |
|high-loss sum | | | | |
+--------------+----------+------------+------------+-------------+
|Optimistic |66.667% |50% |25% |27.273% |
|exceed ratio | | | | |
+--------------+----------+------------+------------+-------------+
|Pessimistic |66.667% |50% |25% |27.273% |
|exceed ratio | | | | |
+--------------+----------+------------+------------+-------------+
|Classification|Upper |Lower Bound |Lower Bound |Lower Bound |
|Result |Bound | | | |
+--------------+----------+------------+------------+-------------+
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Table 6
This is the Low-Loss Trial the "TST009" goal was waiting for. This
Load is now classified for all goals; the search may end. Or, more
realistically, it can focus on larger load only, as the three goals
will want an Upper Bound (unless this Load is Max Load).
C.4. Conditional Throughput Computations
At the end of this hypothetical search, the "RFC2544" goal labels the
load as an Upper Bound, making it ineligible for Conditional-
Throughput calculations. By contrast, the other three goals treat
the same load as a Lower Bound; if it is also accepted as their
Relevant Lower Bound, Conditional Throughput values can be computed
for each of them.
(The load under discussion is 1 000 000 frames per second.)
C.4.1. Goal 2
The Conditional Throughput is computed from sorted list of Full-
Length Trial results. As TST009 Goal Final Trial Duration is 60
seconds, only two of 122 Trials are considered Full-Length Trials.
One has Trial Loss Ratio of 0%, the other of 0.1%.
* Full-length high-loss sum is 60 seconds.
* Full-length low-loss sum is 60 seconds.
* Full-length is 120 seconds.
* Subceed ratio is 50%.
* Remaining sum initially is 0.5x12s = 60 seconds.
* Current loss ratio initially is 100%.
* For first result (duration 60s, loss 0%):
- Remaining sum is larger than zero, not exiting the loop.
- Set current loss ratio to this trial's Trial Loss Ratio which
is 0%.
- Decrease the remaining sum by this trial's Trial Effective
Duration.
- New remaining sum is 60s - 60s = 0s.
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* For second result (duration 60s, loss 0.1%):
* Remaining sum is not larger than zero, exiting the loop.
* Current loss ratio was most recently set to 0%.
* Current forwarding ratio is one minus the current loss ratio, so
100%.
* Conditional Throughput is the current forwarding ratio multiplied
by the Load value.
* Conditional Throughput is one million frames per second.
C.4.2. Goal 3
The "1s final" has Goal Final Trial Duration of 1 second, so all 122
Trial Results are considered Full-Length Trials. They are ordered
like this:
60 1-second 0% loss trials,
1 60-second 0% loss trial,
1 60-second 0.1% loss trial,
60 1-second 1% loss trials.
The result does not depend on the order of 0% loss trials.
* Full-length high-loss sum is 60 seconds.
* Full-length low-loss sum is 180 seconds.
* Full-length is 240 seconds.
* Subceed ratio is 50%.
* Remaining sum initially is 0.5x240s = 120 seconds.
* Current loss ratio initially is 100%.
* For first 61 results (duration varies, loss 0%):
- Remaining sum is larger than zero, not exiting the loop.
- Set current loss ratio to this trial's Trial Loss Ratio which
is 0%.
- Decrease the remaining sum by this trial's Trial Effective
Duration.
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- New remaining sum varies.
* After 61 trials, duration of 60x1s + 1x60s has been subtracted
from 120s, leaving 0s.
* For 62-th result (duration 60s, loss 0.1%):
- Remaining sum is not larger than zero, exiting the loop.
* Current loss ratio was most recently set to 0%.
* Current forwarding ratio is one minus the current loss ratio, so
100%.
* Conditional Throughput is the current forwarding ratio multiplied
by the Load value.
* Conditional Throughput is one million frames per second.
C.4.3. Goal 4
The Conditional Throughput is computed from sorted list of Full-
Length Trial results. As "20% exceed" Goal Final Trial Duration is
60 seconds, only two of 122 Trials are considered Full-Length Trials.
One has Trial Loss Ratio of 0%, the other of 0.1%.
* Full-length high-loss sum is 60 seconds.
* Full-length low-loss sum is 60 seconds.
* Full-length is 120 seconds.
* Subceed ratio is 80%.
* Remaining sum initially is 0.8x120s = 96 seconds.
* Current loss ratio initially is 100%.
* For first result (duration 60s, loss 0%):
- Remaining sum is larger than zero, not exiting the loop.
- Set current loss ratio to this trial's Trial Loss Ratio which
is 0%.
- Decrease the remaining sum by this trial's Trial Effective
Duration.
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- New remaining sum is 96s - 60s = 36s.
* For second result (duration 60s, loss 0.1%):
- Remaining sum is larger than zero, not exiting the loop.
- Set current loss ratio to this trial's Trial Loss Ratio which
is 0.1%.
- Decrease the remaining sum by this trial's Trial Effective
Duration.
- New remaining sum is 36s - 60s = -24s.
* No more trials (and remaining sum is not larger than zero),
exiting loop.
* Current loss ratio was most recently set to 0.1%.
* Current forwarding ratio is one minus the current loss ratio, so
99.9%.
* Conditional Throughput is the current forwarding ratio multiplied
by the Load value.
* Conditional Throughput is 999 thousand frames per second.
Due to stricter Goal Exceed Ratio, this Conditional Throughput is
smaller than Conditional Throughput of the other two goals.
Authors' Addresses
Maciek Konstantynowicz
Cisco Systems
Email: mkonstan@cisco.com
Vratko Polak
Cisco Systems
Email: vrpolak@cisco.com
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