Model Based Metrics for Bulk Transport Capacity
draft-ietf-ippm-model-based-metrics-05
The information below is for an old version of the document.
| Document | Type |
This is an older version of an Internet-Draft that was ultimately published as RFC 8337.
|
|
|---|---|---|---|
| Authors | Matt Mathis , Al Morton | ||
| Last updated | 2015-06-13 | ||
| Replaces | draft-mathis-ippm-model-based-metrics | ||
| RFC stream | Internet Engineering Task Force (IETF) | ||
| Formats | |||
| Reviews |
GENART Telechat review
(of
-11)
by Robert Sparks
Ready w/nits
GENART Last Call review
(of
-10)
by Robert Sparks
Almost ready
|
||
| Additional resources | Mailing list discussion | ||
| Stream | WG state | In WG Last Call | |
| Document shepherd | Brian Trammell | ||
| IESG | IESG state | Became RFC 8337 (Experimental) | |
| Consensus boilerplate | Unknown | ||
| Telechat date | (None) | ||
| Responsible AD | (None) | ||
| Send notices to | (None) |
draft-ietf-ippm-model-based-metrics-05
IP Performance Working Group M. Mathis
Internet-Draft Google, Inc
Intended status: Experimental A. Morton
Expires: December 15, 2015 AT&T Labs
June 13, 2015
Model Based Metrics for Bulk Transport Capacity
draft-ietf-ippm-model-based-metrics-05.txt
Abstract
We introduce a new class of model based metrics designed to determine
if a complete Internet path can meet predefined bulk transport
performance targets by applying a suite of IP diagnostic tests to
successive subpaths. The subpath-at-a-time tests can be robustly
applied to key infrastructure, such as interconnects or even
individual devices, to accurately detect if any part of the
infrastructure will prevent any path traversing it from meeting the
specified target performance.
The diagnostic tests consist of precomputed traffic patterns and
statistical criteria for evaluating packet delivery. The traffic
patterns are precomputed to mimic TCP or other transport protocol
over a long path but are constructed in such a way that they are
independent of the actual details of the subpath under test, end
systems or applications. Likewise the success criteria depends on
the packet delivery statistics of the subpath, as evaluated against a
protocol model applied to the target performance. The success
criteria also does not depend on the details of the subpath, end
systems or application. This makes the measurements open loop,
eliminating most of the difficulties encountered by traditional bulk
transport metrics.
Model based metrics exhibit several important new properties not
present in other Bulk Capacity Metrics, including the ability to
reason about concatenated or overlapping subpaths. The results are
vantage independent which is critical for supporting independent
validation of tests results from multiple Measurement Points.
This document does not define diagnostic tests directly, but provides
a framework for designing suites of IP diagnostics tests that are
tailored to confirming that infrastructure can meet a predetermined
target performance.
Interim DRAFT Formatted: Sat Jun 13 16:25:01 PDT 2015
Status of this Memo
Mathis & Morton Expires December 15, 2015 [Page 1]
Internet-Draft Model Based Metrics June 2015
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 http://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 December 15, 2015.
Copyright Notice
Copyright (c) 2015 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
(http://trustee.ietf.org/license-info) in effect on the date of
publication of this document. 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 Simplified BSD License text as described in Section 4.e of
the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
Mathis & Morton Expires December 15, 2015 [Page 2]
Internet-Draft Model Based Metrics June 2015
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.1. Version Control . . . . . . . . . . . . . . . . . . . . . 6
2. Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 10
4. New requirements relative to RFC 2330 . . . . . . . . . . . . 14
5. Background . . . . . . . . . . . . . . . . . . . . . . . . . . 15
5.1. TCP properties . . . . . . . . . . . . . . . . . . . . . . 16
5.2. Diagnostic Approach . . . . . . . . . . . . . . . . . . . 17
6. Common Models and Parameters . . . . . . . . . . . . . . . . . 19
6.1. Target End-to-end parameters . . . . . . . . . . . . . . . 19
6.2. Common Model Calculations . . . . . . . . . . . . . . . . 19
6.3. Parameter Derating . . . . . . . . . . . . . . . . . . . . 20
7. Traffic generating techniques . . . . . . . . . . . . . . . . 21
7.1. Paced transmission . . . . . . . . . . . . . . . . . . . . 21
7.2. Constant window pseudo CBR . . . . . . . . . . . . . . . . 22
7.3. Scanned window pseudo CBR . . . . . . . . . . . . . . . . 23
7.4. Concurrent or channelized testing . . . . . . . . . . . . 23
8. Interpreting the Results . . . . . . . . . . . . . . . . . . . 24
8.1. Test outcomes . . . . . . . . . . . . . . . . . . . . . . 24
8.2. Statistical criteria for estimating run_length . . . . . . 26
8.3. Reordering Tolerance . . . . . . . . . . . . . . . . . . . 27
9. Test Preconditions . . . . . . . . . . . . . . . . . . . . . . 28
10. Diagnostic Tests . . . . . . . . . . . . . . . . . . . . . . . 29
10.1. Basic Data Rate and Delivery Statistics Tests . . . . . . 29
10.1.1. Delivery Statistics at Paced Full Data Rate . . . . . 30
10.1.2. Delivery Statistics at Full Data Windowed Rate . . . 30
10.1.3. Background Delivery Statistics Tests . . . . . . . . 30
10.2. Standing Queue Tests . . . . . . . . . . . . . . . . . . . 31
10.2.1. Congestion Avoidance . . . . . . . . . . . . . . . . 32
10.2.2. Bufferbloat . . . . . . . . . . . . . . . . . . . . . 32
10.2.3. Non excessive loss . . . . . . . . . . . . . . . . . 33
10.2.4. Duplex Self Interference . . . . . . . . . . . . . . 33
10.3. Slowstart tests . . . . . . . . . . . . . . . . . . . . . 34
10.3.1. Full Window slowstart test . . . . . . . . . . . . . 34
10.3.2. Slowstart AQM test . . . . . . . . . . . . . . . . . 34
10.4. Sender Rate Burst tests . . . . . . . . . . . . . . . . . 35
10.5. Combined and Implicit Tests . . . . . . . . . . . . . . . 35
10.5.1. Sustained Bursts Test . . . . . . . . . . . . . . . . 36
10.5.2. Streaming Media . . . . . . . . . . . . . . . . . . . 37
11. An Example . . . . . . . . . . . . . . . . . . . . . . . . . . 37
12. Validation . . . . . . . . . . . . . . . . . . . . . . . . . . 39
13. Security Considerations . . . . . . . . . . . . . . . . . . . 40
14. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 41
15. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 41
16. References . . . . . . . . . . . . . . . . . . . . . . . . . . 41
16.1. Normative References . . . . . . . . . . . . . . . . . . . 41
Mathis & Morton Expires December 15, 2015 [Page 3]
Internet-Draft Model Based Metrics June 2015
16.2. Informative References . . . . . . . . . . . . . . . . . . 41
Appendix A. Model Derivations . . . . . . . . . . . . . . . . . . 44
A.1. Queueless Reno . . . . . . . . . . . . . . . . . . . . . . 44
Appendix B. Complex Queueing . . . . . . . . . . . . . . . . . . 45
Appendix C. Version Control . . . . . . . . . . . . . . . . . . . 46
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 46
Mathis & Morton Expires December 15, 2015 [Page 4]
Internet-Draft Model Based Metrics June 2015
1. Introduction
Model Based Metrics (MBM) rely on mathematical models to specify a
targeted diagnostic suite of IP diagnostic tests, designed to verify
that common transport protocols can meet a predetermined performance
target over an Internet path. Each diagnostic in the suite measures
some aspect of IP delivery that is required to meet the performance
target. For example a TDS may have separate diagnostic tests to
verify that there is sufficient data rate and sufficient queueing
buffer space to deliver typical transport bursts, and that the
background packet loss is small enough not to interfere with
congestion control. Unlike other metrics which yield measures of
network properties, Model Based Metrics nominally yield pass/fail
evaluations of the ability of transport protocols to meet a
performance objective as need by a user application over a particular
network path.
This note describes the modeling framework to derive the IP
diagnostic test parameters from the target performance specified for
TCP bulk transport capacity. In the future, other Model Based
Metrics may cover other applications and transports, such as VoIP
over RTP. In most cases the IP diagnostic tests can be implemented
by combining existing IPPM metrics with additional controls for
precomputed traffic patterns and statistical criteria for evaluating
packet delivery.
This approach, mapping transport performance targets to a targeted
diagnostic suite (TDS) of IP diagnostic tests, solves an intrinsic
problem with using TCP or other throughput maximizing protocols for
measurement. In particular all throughput maximizing protocols (and
TCP congestion control in particular) cause some level of congestion
in order to fill the network. This self inflicted congestion
obscures the network properties of interest and introduces non-linear
equilibrium behaviors that make any resulting measurements useless as
metrics because they have no predictive value for conditions or paths
different than the measurement itself. This problem is discussed in
Section 5.
A targeted suite of IP diagnostic tests do not have such
difficulties. They can be constructed to make strong statistical
statements about path properties that are independent of the
measurement details, such as vantage and choice of measurement
points. Model Based Metrics bridge the gap between empirical IP
measurements and expected TCP performance.
Mathis & Morton Expires December 15, 2015 [Page 5]
Internet-Draft Model Based Metrics June 2015
1.1. Version Control
RFC Editor: Please remove this entire subsection prior to
publication.
Please send comments about this draft to ippm@ietf.org. See
http://goo.gl/02tkD for more information including: interim drafts,
an up to date todo list and information on contributing.
Formatted: Sat Jun 13 16:25:01 PDT 2015
Changes since -04 draft:
o The introduction was heavily overhauled: split into a separate
introduction and overview.
o The new shorter introduction:
* Is a problem statement;
* This document provides a framework;
* That it replaces TCP measurement by IP tests;
* That the results are pass/fail.
o Added a diagram of the framework to the overview
o and introduces all of the elements of the framework.
o Renumbered sections, reducing the depth of some section numbers.
o Updated definitions to better agree with other documents:
* Reordered section 2
* Bulk [data] performance -> Bulk Transport Capacity, everywhere
including the title.
* loss rate and loss probability -> loss ratio
* end-to-end path -> complete path
* [end-to-end][target] performance -> target transport
performance
* load test -> capacity test
This interim draft is a partial update since the WGLC, to collect an
additional round of feedback on the Introduction, overview, and
terminology sections. Note that some of the prior WGLC comments are
still pending. Later sections (4 and beyond) have only been updated
to track changes in the terminology section. We intend to produce an
additional draft prior to the IETF, incorporating still pending
comments from the WGLC and any additional comments on the
introduction and overview.
2. Overview
This document describes a modeling framework for deriving Target
Diagnostic Suites to determine if an IP path can be expected to meet
a predetermined target performance. It relies on other standards
documents to define Important details such as packet type-p
Mathis & Morton Expires December 15, 2015 [Page 6]
Internet-Draft Model Based Metrics June 2015
selection, sampling techniques, vantage selection, etc. which are not
specified here. We imagine Fully Specified Targeted Diagnostic
Suites (FSTDS), that define all of these details. We use TDS to
refer to the subset of such a specification that is in scope for this
document.
Figure 1 shows the MBM modeling and measurement framework. (See
Section 3 for terminology used throughout this document). The target
transport performance is determined by the needs of the user or
application, outside the scope of this document. For bulk transport
capacity, the performance parameter of interest is the target data
rate. However, since TCP's ability to compensate for less than ideal
network conditions is fundamentally affected by the Round Trip Time
(RTT) and the Maximum Transmission Unit (MTU) of the complete path,
these parameters must also be specified in advance using knowledge
about the intended application setting. Section 6 describes the
common parameters and models used to derive a targeted diagnostic
suite.
The target transport performance may reflect a specific application
over real path through the Internet or an idealized application and
path representing a typical user community.
Mathis & Morton Expires December 15, 2015 [Page 7]
Internet-Draft Model Based Metrics June 2015
target transport performance
(target data rate, target RTT and target MTU)
|
________V_________
| mathematical |
| models |
| |
------------------
Traffic parameters | | Statistical criteria
| |
_______V____________V____Targeted_______
| | * * * | Diagnostic Suite |
_____|_______V____________V________________ |
__|____________V____________V______________ | |
| IP Diagnostic test | | |
| | | | | |
| _____________V__ __V____________ | | |
| | Traffic | | Delivery | | | |
| | Generation | | Evaluation | | | |
| | | | | | | |
| -------v-------- ------^-------- | | |
| | v Test Traffic via ^ | | |--
| | -->======================>-- | | |
| | subpath under test | |-
----V----------------------------------V--- |
| | | | | |
V V V V V V
fail/inconclusive pass/fail/inconclusive
Overall Modeling Framework
Figure 1
Section 5 describes some key aspects of TCP behavior and what they
imply about the requirements for IP packet delivery. Most of the IP
diagnostic tests needed to confirm that the path meets these
properties can be built on existing IPPM metrics, with the addition
of statistical criteria for evaluating packet delivery and in some
cases new mechanisms to implement precomputed traffic patterns. One
group of tests, the standing queue tests described in section
Section 10.2, don't correspond to existing IPPM metrics, but suitable
metrics can be patterned after existing tools.
Mathematical models are used to design traffic patterns that mimic
TCP or other bulk transport protocol operating at the target data
rate, MTU and RTT over a full range of conditions, including flows
that are bursty at multiple time scales. The traffic patterns are
generated based on the three target parameters of complete path and
Mathis & Morton Expires December 15, 2015 [Page 8]
Internet-Draft Model Based Metrics June 2015
independent of the properties of individual subpaths as described in
Section 7. As much as possible the measurement traffic is generated
deterministically to that minimize the extent to which test
methodology, measurement points, measurement vantage or path
partitioning affect the details of the measurement traffic.
Section 8 describes packet delivery statistics and methods test them
against the bounds provided by the mathematical models. Since these
statistics are typically aggregated from all subpaths of the complete
path, in situ testing requires that the end-to-end statistical bounds
be apportioned as a separate bound for each subpath. Links that are
expected to be bottlenecks are expected to contribute a larger
fraction of the total packet loss. In compensation, other links have
to be constrained to contribute less packet loss. The criteria for
passing each test of a TDS is an apportioned share of the total bound
determined by the mathematical model from the target transport
performance .
Section 10 describes the suite of individual tests needed to verify
all of required IP delivery properties. A subpath passes if and only
if all of the individual IP diagnostics tests pass. Any subpath that
fails any test indicates that some users are likely fail to attain
their target transport performance under some conditions. In
addition to passing or failing, a test can be deemed to be
inconclusive for a number of reasons including: the precomputed
traffic pattern was not accurately generated; the measurement results
were not statistically significant; and others such as failing to
meet some required test preconditions. If all test pass, except some
are inconclusive then the entire suite is deemed to be inconclusive.
Since there is some uncertainty in this process, Section 12,
describes a validation procedure to diagnose and minimize false
positive and false negative results.
In Section 11 we present an example TDS that might be representative
of HD video, and illustrate how Model Based Metrics can be used to
address difficult measurement situations, such as confirming that
intercarrier exchanges have sufficient performance and capacity to
deliver HD video between ISPs.
A TDS includes the target parameters, documentation of the models and
assumptions used to derive the IP diagnostic test parameters,
specifications for the traffic and delivery statistics for the tests
themselves, and a description of a test setup that can be used to
validate the tests and models.
Mathis & Morton Expires December 15, 2015 [Page 9]
Internet-Draft Model Based Metrics June 2015
3. Terminology
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in [RFC2119].
General Terminology:
Target: A general term for any parameter specified by or derived
from the user's application or transport performance requirements.
Complete Path: From RFC 5835
target transport performance: Application or transport performance
goals for the complete path. For bulk transport capacity defined
in this note the target transport performance includes the target
data rate, target RTT and target MTU as described below.
Target Data Rate: The specified application data rate required for
an application's proper operation. This is typically the
performance goal as needed by the ultimate user.
Target RTT (Round Trip Time): The baseline (minimum) RTT of the
longest complete path over which the application expects to be
able meet the target performance. TCP and other transport
protocol's ability to compensate for path problems is generally
proportional to the number of round trips per second. The Target
RTT determines both key parameters of the traffic patterns (e.g.
burst sizes) and the thresholds on acceptable traffic statistics.
The Target RTT must be specified considering authentic packets
sizes: MTU sized packets on the forward path, ACK sized packets
(typically header_overhead) on the return path.
Target MTU (Maximum Transmission Unit): The maximum MTU supported by
the complete path the over which the application expects to meet
the target performance. Assume 1500 Byte MTU unless otherwise
specified. If some subpath forces a smaller MTU, then it becomes
the target MTU, and all model calculations and subpath tests must
use the same smaller MTU.
Targeted Diagnostic Suite (TDS): A set of IP Diagnostics designed to
determine if an otherwise ideal complete path containing the
subpath under test can sustain flows at a specific
target_data_rate using target_MTU sized packets when the RTT of
the complete path is target_RTT.
Fully Specified Targeted Diagnostic Suite: A TDS together with
additional specification such as "type-p", etc which are out of
scope for this document, but need to be drawn from other standards
documents.
loss ratio: See "Packet Loss Ratio in [RFC2680bis]
Mathis & Morton Expires December 15, 2015 [Page 10]
Internet-Draft Model Based Metrics June 2015
apportioned: To divide and allocate, for example budgeting packet
loss ratio across multiple subpaths such that they will accumulate
to less than a specified end-to-end loss ratio.
open loop: A control theory term used to describe a class of
techniques where systems that naturally exhibit circular
dependencies can be analyzed by suppressing some of the
dependences, such that the resulting dependency graph is acyclic.
Bulk Transport Capacity: Bulk Transport Capacity Metrics evaluate an
Internet path's ability to carry bulk data, such as large files,
streaming (non-real time) video, and under some conditions, web
images and other content. Prior efforts to define BTC metrics
have been based on [RFC3148], which never succeeded due to some
overlooked requirements described in Section 4 and problems
described in The metrics presented in this document reflect an
entirely different approach to the problem outlined in [RFC3148].
traffic patterns: The temporal patterns or statistics of traffic
generated by applications over transport protocols such as TCP.
There are several mechanisms that cause bursts at various time
scales. Our goal here is to mimic the range of common patterns
(burst sizes and rates, etc), without tieing our applicability to
specific applications, implementations or technologies, which are
sure to become stale.
delivery Statistics: Raw or summary statistics about packet delivery
properties of the IP layer including packet losses, ECN marks,
reordering, or any other properties that may be germane to
transport performance.
IP performance tests: Measurements or diagnostic tests to determine
delivery statistics.
Terminology about paths, etc. See [RFC2330] and [RFC7398].
[data] sender: Host sending data and receiving ACKs.
[data] receiver: Host receiving data and sending ACKs.
subpath: A portion of the full path. Note that there is no
requirement that subpaths be non-overlapping.
Measurement Point: Measurement points as described in [RFC7398].
test path: A path between two measurement points that includes a
subpath of the complete path under test, and could include
infrastructure between the measurement points and the subpath.
[Dominant] Bottleneck: The Bottleneck that generally dominates
traffic statistics for the entire path. It typically determines a
flow's self clock timing, packet loss and ECN marking rate. See
Section 5.1.
front path: The subpath from the data sender to the dominant
bottleneck.
Mathis & Morton Expires December 15, 2015 [Page 11]
Internet-Draft Model Based Metrics June 2015
back path: The subpath from the dominant bottleneck to the receiver.
return path: The path taken by the ACKs from the data receiver to
the data sender.
cross traffic: Other, potentially interfering, traffic competing for
network resources (bandwidth and/or queue capacity).
Properties determined by the complet path and application. They are
described in more detail in Section 6.1.
Application Data Rate: General term for the data rate as seen by the
application above the transport layer. This is the payload data
rate, and explicitly excludes transport and lower level headers
(TCP/IP or other protocols), retransmissions and other overhead
that is not part to the total quantity of data delivered to the
application.
Link Data Rate: General term for the data rate as seen by the link
or lower layers. The link data rate includes transport and IP
headers, retransmissions and other transport layer overhead. This
document is agnostic as to whether the link data rate includes or
excludes framing, MAC, or other lower layer overheads, except that
they must be treated uniformly.
Effective Bottleneck Data Rate: This is the bottleneck data rate
implied by the returning ACKs, by looking at how much application
data the ACK stream reports delivered per unit time. If the path
is thinning ACKs or batching ACKs the effective bottleneck rate
can be much higher than the average link rate. See Section 5.1
and Appendix B for more details.
[sender | interface] rate: The burst data rate, constrained by the
data sender's interface. Today 1 or 10 Gb/s are typical.
Header_overhead: The IP and TCP header sizes, which are the portion
of each MTU not available for carrying application payload.
Without loss of generality this is assumed to be the size for
returning acknowledgements (ACKs). For TCP, the Maximum Segment
Size (MSS) is the Target MTU minus the header_overhead.
Basic parameters common to models and subpath tests are defined here
are described in more detail in Section 6.2. Note that these are
mixed between application transport performance (excludes headers)
and link IP performance (includes headers).
Window: The total quantity of data plus the data represented by ACKs
circulating in the network is referred to as the window. See
Section 5.1
pipe size: A general term for number of packets needed in flight
(the window size) to exactly fill some network path or subpath.
It corresponds to the window size which maximizes network power,
the observed data rate divided by the observed RTT. Often used
with additional qualifies to specify which path, etc.
Mathis & Morton Expires December 15, 2015 [Page 12]
Internet-Draft Model Based Metrics June 2015
target_pipe_size: The number of packets in flight (the window size)
needed to exactly meet the target rate, with a single stream and
no cross traffic for the specified application target data rate,
RTT, and MTU. It is the amount of circulating data required to
meet the target data rate, and implies the scale of the bursts
that the network might experience.
run length: A general term for the observed, measured, or specified
number of packets that are (to be) delivered between losses or ECN
marks. Nominally one over the sum of the loss and ECN marking
probabilities, if there are independently and identically
distributed.
target_run_length: The target_run_length is an estimate of the
minimum number of non-congestion marked packets needed between
losses or ECN marks necessary to attain the target_data_rate over
a path with the specified target_RTT and target_MTU, as computed
by a mathematical model of TCP congestion control. A reference
calculation is shown in Section 6.2 and alternatives in Appendix A
reference target_run_length: target_run_length computed precisely by
the method in Section 6.2. This is likely to be more slightly
conservative than required by modern TCP algorithms.
Ancillary parameters used for some tests
derating: Under some conditions the standard models are too
conservative. The modeling framework permits some latitude in
relaxing or "derating" some test parameters as described in
Section 6.3 in exchange for a more stringent TDS validation
procedures, described in Section 12.
subpath_data_rate: The maximum data rate supported by a subpath.
This typically includes TCP/IP overhead, including all headers and
retransmits, etc.
test_path_RTT: The RTT observed between two measurement points using
packet sizes that are consistent with the transport protocol.
Generally MTU sized packets of the forward path, header_overhead
sized packets on the return path.
test_path_pipe: The amount of data necessary to fill a test path.
Nominally the test path RTT times the subpath_data_rate.
test_window: The window necessary to meet the target_rate over a
subpath. Typically test_window=target_data_rate*test_RTT/
(target_MTU - header_overhead).
Tests can be grouped according to their applicability.
Capacity tests: determine if a network subpath has sufficient
capacity to deliver the target performance. As long as the test
traffic is within the proper envelope for the target performance,
the average packet losses or ECN marks must be below the threshold
computed by the model. As such, capacity tests reflect parameters
Mathis & Morton Expires December 15, 2015 [Page 13]
Internet-Draft Model Based Metrics June 2015
that can transition from passing to failing as a consequence of
cross traffic, additional presented load or the actions of other
network users. By definition, capacity tests also consume
significant network resources (data capacity and/or buffer space),
and the test schedules must be balanced by their cost.
Monitoring tests: are designed to capture the most important aspects
of a capacity test, but without presenting excessive ongoing load
themselves. As such they may miss some details of the network's
performance, but can serve as a useful reduced-cost proxy for a
capacity test, for example to support ongoing monitoring.
Engineering tests: evaluate how network algorithms (such as AQM and
channel allocation) interact with TCP-style self clocked protocols
and adaptive congestion control based on packet loss and ECN
marks. These tests are likely to have complicated interactions
with cross traffic and under some conditions can be inversely
sensitive to load. For example a test to verify that an AQM
algorithm causes ECN marks or packet drops early enough to limit
queue occupancy may experience a false pass result in the presence
of cross traffic. It is important that engineering tests be
performed under a wide range of conditions, including both in situ
and bench testing, and over a wide variety of load conditions.
Ongoing monitoring is less likely to be useful for engineering
tests, although sparse in situ testing might be appropriate.
4. New requirements relative to RFC 2330
Model Based Metrics are designed to fulfill some additional
requirement that were not recognized at the time RFC 2330 was written
[RFC2330]. These missing requirements may have significantly
contributed to policy difficulties in the IP measurement space. Some
additional requirements are:
o IP metrics must be actionable by the ISP - they have to be
interpreted in terms of behaviors or properties at the IP or lower
layers, that an ISP can test, repair and verify.
o Metrics should be spatially composable, such that measures of
concatenated paths should be predictable from subpaths.
o Metrics must be vantage point invariant over a significant range
of measurement point choices, including off path measurement
points. The only requirements on MP selection should be that the
portion of the test path that is not under test between the MP and
the part that is under test is effectively ideal, or is non ideal
in ways that can be calibrated out of the measurements and the
test RTT between the MPs is below some reasonable bound.
o Metric measurements must be repeatable by multiple parties with no
specialized access to MPs or diagnostic infrastructure. It must
be possible for different parties to make the same measurement and
observe the same results. In particular it is specifically
Mathis & Morton Expires December 15, 2015 [Page 14]
Internet-Draft Model Based Metrics June 2015
important that both a consumer (or their delegate) and ISP be able
to perform the same measurement and get the same result. Note
that vantage independence is key to this requirement.
5. Background
At the time the IPPM WG was chartered, sound Bulk Transport Capacity
measurement was known to be well beyond our capabilities. Even at
the time [RFC3148] was written we knew that we didn't fully
understand the problem. Now, by hindsight we understand why BTC is
such a hard problem:
o TCP is a control system with circular dependencies - everything
affects performance, including components that are explicitly not
part of the test.
o Congestion control is an equilibrium process, such that transport
protocols change the network (raise the loss ratio and/or RTT) to
conform to their behavior. By design TCP congestion control keep
raising the data rate until the network give some indication that
it is full by delaying, dropping or ECN marking packets.
o TCP's ability to compensate for network flaws is directly
proportional to the number of roundtrips per second (i.e.
inversely proportional to the RTT). As a consequence a flawed
link may pass a short RTT local test even though it fails when the
path is extended by a perfect network to some larger RTT.
o TCP has a meta Heisenberg problem - Measurement and cross traffic
interact in unknown and ill defined ways. The situation is
actually worse than the traditional physics problem where you can
at least estimate bounds on the relative momentum of the
measurement and measured particles. For network measurement you
can not in general determine the relative "mass" of the
measurement traffic and cross traffic, so you can not even gauge
the relative magnitude of their effects on each other.
These properties are a consequence of the equilibrium behavior
intrinsic to how all throughput optimizing protocols interact with
the Internet. The protocols rely on control systems based on
multiple network estimators to regulate the quantity of data traffic
sent into the network. The data traffic in turn alters network and
the properties observed by the estimators, such that there are
circular dependencies between every component and every property.
Since some of these properties are nonlinear, the entire system is
nonlinear, and any change anywhere causes difficult to predict
changes in every parameter.
Model Based Metrics overcome these problems by forcing the
measurement system to be open loop: the delivery statistics (akin to
the network estimators) do not affect the traffic or traffic patterns
Mathis & Morton Expires December 15, 2015 [Page 15]
Internet-Draft Model Based Metrics June 2015
(bursts), which computed on the basis of the target performance. In
order for a network to pass, the resulting delivery statistics and
corresponding network estimators have to be such that they would not
cause the control systems slow the traffic below the target rate.
5.1. TCP properties
TCP and SCTP are self clocked protocols. The dominant steady state
behavior is to have an approximately fixed quantity of data and
acknowledgements (ACKs) circulating in the network. The receiver
reports arriving data by returning ACKs to the data sender, the data
sender typically responds by sending exactly the same quantity of
data back into the network. The total quantity of data plus the data
represented by ACKs circulating in the network is referred to as the
window. The mandatory congestion control algorithms incrementally
adjust the window by sending slightly more or less data in response
to each ACK. The fundamentally important property of this systems is
that it is entirely self clocked: The data transmissions are a
reflection of the ACKs that were delivered by the network, the ACKs
are a reflection of the data arriving from the network.
A number of phenomena can cause bursts of data, even in idealized
networks that are modeled as simple queueing systems.
During slowstart the data rate is doubled on each RTT by sending
twice as much data as was delivered to the receiver on the prior RTT.
For slowstart to be able to fill such a network the network must be
able to tolerate slowstart bursts up to the full pipe size inflated
by the anticipated window reduction on the first loss or ECN mark.
For example, with classic Reno congestion control, an optimal
slowstart has to end with a burst that is twice the bottleneck rate
for exactly one RTT in duration. This burst causes a queue which is
exactly equal to the pipe size (i.e. the window is exactly twice the
pipe size) so when the window is halved in response to the first
loss, the new window will be exactly the pipe size.
Note that if the bottleneck data rate is significantly slower than
the rest of the path, the slowstart bursts will not cause significant
queues anywhere else along the path; they primarily exercise the
queue at the dominant bottleneck.
Other sources of bursts include application pauses and channel
allocation mechanisms. Appendix B describes the treatment of channel
allocation systems. If the application pauses (stops reading or
writing data) for some fraction of one RTT, state-of-the-art TCP
catches up to the earlier window size by sending a burst of data at
the full sender interface rate. To fill such a network with a
realistic application, the network has to be able to tolerate
Mathis & Morton Expires December 15, 2015 [Page 16]
Internet-Draft Model Based Metrics June 2015
interface rate bursts from the data sender large enough to cover
application pauses.
Although the interface rate bursts are typically smaller than last
burst of a slowstart, they are at a higher data rate so they
potentially exercise queues at arbitrary points along the front path
from the data sender up to and including the queue at the dominant
bottleneck. There is no model for how frequent or what sizes of
sender rate bursts should be tolerated.
To verify that a path can meet a performance target, it is necessary
to independently confirm that the path can tolerate bursts in the
dimensions that can be caused by these mechanisms. Three cases are
likely to be sufficient:
o Slowstart bursts sufficient to get connections started properly.
o Frequent sender interface rate bursts that are small enough where
they can be assumed not to significantly affect delivery
statistics. (Implicitly derated by selecting the burst size).
o Infrequent sender interface rate full target_pipe_size bursts that
do affect the delivery statistics. (Target_run_length may be
derated).
5.2. Diagnostic Approach
The MBM approach is to open loop TCP by precomputing traffic patterns
that are typically generated by TCP operating at the given target
parameters, and evaluating delivery statistics (packet loss, ECN
marks and delay). In this approach the measurement software
explicitly controls the data rate, transmission pattern or cwnd
(TCP's primary congestion control state variables) to create
repeatable traffic patterns that mimic TCP behavior but are
independent of the actual behavior of the subpath under test. These
patterns are manipulated to probe the network to verify that it can
deliver all of the traffic patterns that a transport protocol is
likely to generate under normal operation at the target rate and RTT.
By opening the protocol control loops, we remove most sources of
temporal and spatial correlation in the traffic delivery statistics,
such that each subpath's contribution to the end-to-end delivery
statistics can be assumed to be independent and stationary (The
delivery statistics depend on the fine structure of the data
transmissions, but not on long time scale state imbedded in the
sender, receiver or other network components.) Therefore each
subpath's contribution to the end-to-end delivery statistics can be
assumed to be independent, and spatial composition techniques such as
[RFC5835] and [RFC6049] apply.
Mathis & Morton Expires December 15, 2015 [Page 17]
Internet-Draft Model Based Metrics June 2015
In typical networks, the dominant bottleneck contributes the majority
of the packet loss and ECN marks. Often the rest of the path makes
insignificant contribution to these properties. A TDS should
apportion the end-to-end budget for the specified parameters
(primarily packet loss and ECN marks) to each subpath or group of
subpaths. For example the dominant bottleneck may be permitted to
contribute 90% of the loss budget, while the rest of the path is only
permitted to contribute 10%.
A TDS or FSTDS MUST apportion all relevant packet delivery statistics
between successive subpaths, such that the spatial composition of the
apportioned metrics will yield end-to-end delivery statistics which
are within the bounds determined by the models.
A network is expected to be able to sustain a Bulk TCP flow of a
given data rate, MTU and RTT when all of the following conditions are
met:
1. The raw link rate is higher than the target data rate. See
Section 10.1 or any number of data rate tests outside of MBM.
2. The observed packet delivery statistics are better than required
by a suitable TCP performance model (e.g. fewer losses or ECN
marks). See Section 10.1 or any number of low rate packet loss
tests outside of MBM.
3. There is sufficient buffering at the dominant bottleneck to
absorb a slowstart rate burst large enough to get the flow out of
slowstart at a suitable window size. See Section 10.3.
4. There is sufficient buffering in the front path to absorb and
smooth sender interface rate bursts at all scales that are likely
to be generated by the application, any channel arbitration in
the ACK path or any other mechanisms. See Section 10.4.
5. When there is a standing queue at a bottleneck for a shared media
subpath (e.g. half duplex), there are suitable bounds on how the
data and ACKs interact, for example due to the channel
arbitration mechanism. See Section 10.2.4.
6. When there is a slowly rising standing queue at the bottleneck
the onset of packet loss has to be at an appropriate point (time
or queue depth) and progressive. See Section 10.2.
Note that conditions 1 through 4 require capacity tests for
confirmation, and thus need to be monitored on an ongoing basis.
Conditions 5 and 6 require engineering tests. They won't generally
fail due to load, but may fail in the field due to configuration
errors, etc. and should be spot checked.
We are developing a tool that can perform many of the tests described
here[MBMSource].
Mathis & Morton Expires December 15, 2015 [Page 18]
Internet-Draft Model Based Metrics June 2015
6. Common Models and Parameters
6.1. Target End-to-end parameters
The target end-to-end parameters are the target data rate, target RTT
and target MTU as defined in Section 3. These parameters are
determined by the needs of the application or the ultimate end user
and the complete Internet path over which the application is expected
to operate. The target parameters are in units that make sense to
upper layers: payload bytes delivered to the application, above TCP.
They exclude overheads associated with TCP and IP headers,
retransmits and other protocols (e.g. DNS).
Other end-to-end parameters defined in Section 3 include the
effective bottleneck data rate, the sender interface data rate and
the TCP/IP header sizes (overhead).
The target data rate must be smaller than all link data rates by
enough headroom to carry the transport protocol overhead, explicitly
including retransmissions and an allowance for fluctuations in the
actual data rate, needed to meet the specified average rate.
Specifying a target rate with insufficient headroom is likely to
result in brittle measurements having little predictive value.
Note that the target parameters can be specified for a hypothetical
path, for example to construct TDS designed for bench testing in the
absence of a real application, or for a real physical test for in
situ testing of production infrastructure.
The number of concurrent connections is explicitly not a parameter to
this model. If a subpath requires multiple connections in order to
meet the specified performance, that must be stated explicitly and
the procedure described in Section 7.4 applies.
6.2. Common Model Calculations
The target transport performance is used to derive the
target_pipe_size and the reference target_run_length.
The target_pipe_size, is the average window size in packets needed to
meet the target rate, for the specified target RTT and MTU. It is
given by:
target_pipe_size = ceiling( target_rate * target_RTT / ( target_MTU -
header_overhead ) )
Target_run_length is an estimate of the minimum required number of
unmarked packets that must be delivered between losses or ECN marks,
Mathis & Morton Expires December 15, 2015 [Page 19]
Internet-Draft Model Based Metrics June 2015
as computed by a mathematical model of TCP congestion control. The
derivation here follows [MSMO97], and by design is quite
conservative. The alternate models described in Appendix A generally
yield smaller run_lengths (higher acceptable loss or ECN marking
rates), but may not apply in all situations. A FSTDS that uses an
alternate model MUST compare it to the reference target_run_length
computed here.
Reference target_run_length is derived as follows: assume the
subpath_data_rate is infinitesimally larger than the target_data_rate
plus the required header_overhead. Then target_pipe_size also
predicts the onset of queueing. A larger window will cause a
standing queue at the bottleneck.
Assume the transport protocol is using standard Reno style Additive
Increase, Multiplicative Decrease (AIMD) congestion control [RFC5681]
(but not Appropriate Byte Counting [RFC3465]) and the receiver is
using standard delayed ACKs. Reno increases the window by one packet
every pipe_size worth of ACKs. With delayed ACKs this takes 2 Round
Trip Times per increase. To exactly fill the pipe, losses must be no
closer than when the peak of the AIMD sawtooth reached exactly twice
the target_pipe_size otherwise the multiplicative window reduction
triggered by the loss would cause the network to be underfilled.
Following [MSMO97] the number of packets between losses must be the
area under the AIMD sawtooth. They must be no more frequent than
every 1 in ((3/2)*target_pipe_size)*(2*target_pipe_size) packets,
which simplifies to:
target_run_length = 3*(target_pipe_size^2)
Note that this calculation is very conservative and is based on a
number of assumptions that may not apply. Appendix A discusses these
assumptions and provides some alternative models. If a different
model is used, a fully specified TDS or FSTDS MUST document the
actual method for computing target_run_length and ratio between
alternate target_run_length and the reference target_run_length
calculated above, along with a discussion of the rationale for the
underlying assumptions.
These two parameters, target_pipe_size and target_run_length,
directly imply most of the individual parameters for the tests in
Section 10.
6.3. Parameter Derating
Since some aspects of the models are very conservative, the MBM
framework permits some latitude in derating test parameters. Rather
than trying to formalize more complicated models we permit some test
Mathis & Morton Expires December 15, 2015 [Page 20]
Internet-Draft Model Based Metrics June 2015
parameters to be relaxed as long as they meet some additional
procedural constraints:
o The TDS or FSTDS MUST document and justify the actual method used
to compute the derated metric parameters.
o The validation procedures described in Section 12 must be used to
demonstrate the feasibility of meeting the performance targets
with infrastructure that infinitesimally passes the derated tests.
o The validation process itself must be documented is such a way
that other researchers can duplicate the validation experiments.
Except as noted, all tests below assume no derating. Tests where
there is not currently a well established model for the required
parameters explicitly include derating as a way to indicate
flexibility in the parameters.
7. Traffic generating techniques
7.1. Paced transmission
Paced (burst) transmissions: send bursts of data on a timer to meet a
particular target rate and pattern. In all cases the specified data
rate can either be the application or link rates. Header overheads
must be included in the calculations as appropriate.
Packet Headway: Time interval between packets, specified from the
start of one to the start of the next. e.g. If packets are sent
with a 1 mS headway, there will be exactly 1000 packets per
second.
Burst Headway: Time interval between bursts, specified from the
start of the first packet one burst to the start of the first
packet of the next burst. e.g. If 4 packet bursts are sent with a
1 mS headway, there will be exactly 4000 packets per second.
Paced single packets: Send individual packets at the specified rate
or packet headway. [@@@@ Site RFC 3432, update definition?]
Paced Bursts: Send sender interface rate bursts on a timer. Specify
any 3 of: average rate, packet size, burst size (number of
packets) and burst headway (burst start to start). The packet
headway within a burst is typically assumed to be the minimum
supported by the tester's interface. i.e. Bursts are normally
sent as back-to-back packets. The packet headway within the
bursts can be explicitly specified.
Slowstart bursts: Send 4 packet paced bursts at an average data rate
equal to twice effective bottleneck link rate (but not more than
the sender interface rate). This corresponds to the average rate
during a TCP slowstart when Appropriate Byte Counting [RFC3465] is
present or delayed ack is disabled. Note that if the effective
bottleneck link rate is more than half of the sender interface
rate, slowstart rate bursts become sender interface rate bursts.
Mathis & Morton Expires December 15, 2015 [Page 21]
Internet-Draft Model Based Metrics June 2015
[@@@@ Add figure --MM].
Repeated Slowstart bursts: Slowstart bursts are typically part of
larger scale pattern of repeated bursts, such as sending
target_pipe_size packets as slowstart bursts on a target_RTT
headway (burst start to burst start). Such a stream has three
different average rates, depending on the averaging interval. At
the finest time scale the average rate is the same as the sender
interface rate, at a medium scale the average rate is twice the
effective bottleneck link rate and at the longest time scales the
average rate is equal to the target data rate.
Note that in conventional measurement theory, exponential
distributions are often used to eliminate many sorts of correlations.
For the procedures above, the correlations are created by the network
elements and accurately reflect their behavior. At some point in the
future, it will be desirable to introduce noise sources into the
above pacing models, but they are not warranted at this time.
7.2. Constant window pseudo CBR
Implement pseudo constant bit rate by running a standard protocol
such as TCP with a fixed window size, such that it is self clocked.
Data packets arriving at the receiver trigger acknowledgements (ACKs)
which travel back to the sender where they trigger additional
transmissions. The window size is computed from the target_data_rate
and the actual RTT of the test path. The rate is only maintained in
average over each RTT, and is subject to limitations of the transport
protocol.
Since the window size is constrained to be an integer number of
packets, for small RTTs or low data rates there may not be
sufficiently precise control over the data rate. Rounding the window
size up (the default) is likely to be result in data rates that are
higher than the target rate, but reducing the window by one packet
may result in data rates that are too small. Also cross traffic
potentially raises the RTT, implicitly reducing the rate. Cross
traffic that raises the RTT nearly always makes the test more
strenuous. A FSTDS specifying a constant window CBR tests MUST
explicitly indicate under what conditions errors in the data cause
tests to inconclusive. See the discussion of test outcomes in
Section 8.1.
Since constant window pseudo CBR testing is sensitive to RTT
fluctuations it can not accurately control the data rate in
environments with fluctuating delays.
Mathis & Morton Expires December 15, 2015 [Page 22]
Internet-Draft Model Based Metrics June 2015
7.3. Scanned window pseudo CBR
Scanned window pseudo CBR is similar to the constant window CBR
described above, except the window is scanned across a range of sizes
designed to include two key events, the onset of queueing and the
onset of packet loss or ECN marks. The window is scanned by
incrementing it by one packet every 2*target_pipe_size delivered
packets. This mimics the additive increase phase of standard TCP
congestion avoidance when delayed ACKs are in effect. It normally
separates the the window increases by approximately twice the
target_RTT.
There are two ways to implement this test: one built by applying a
window clamp to standard congestion control in a standard protocol
such as TCP and the other built by stiffening a non-standard
transport protocol. When standard congestion control is in effect,
any losses or ECN marks cause the transport to revert to a window
smaller than the clamp such that the scanning clamp loses control the
window size. The NPAD pathdiag tool is an example of this class of
algorithms [Pathdiag].
Alternatively a non-standard congestion control algorithm can respond
to losses by transmitting extra data, such that it maintains the
specified window size independent of losses or ECN marks. Such a
stiffened transport explicitly violates mandatory Internet congestion
control and is not suitable for in situ testing. [RFC5681] It is
only appropriate for engineering testing under laboratory conditions.
The Windowed Ping tool implements such a test [WPING]. The tool
described in the paper has been updated.[mpingSource]
The test procedures in Section 10.2 describe how to the partition the
scans into regions and how to interpret the results.
7.4. Concurrent or channelized testing
The procedures described in this document are only directly
applicable to single stream performance measurement, e.g. one TCP
connection. In an ideal world, we would disallow all performance
claims based multiple concurrent streams, but this is not practical
due to at least two different issues. First, many very high rate
link technologies are channelized and pin individual flows to
specific channels to minimize reordering or other problems and
second, TCP itself has scaling limits. Although the former problem
might be overcome through different design decisions, the later
problem is more deeply rooted.
All congestion control algorithms that are philosophically aligned
with the standard [RFC5681] (e.g. claim some level of TCP
Mathis & Morton Expires December 15, 2015 [Page 23]
Internet-Draft Model Based Metrics June 2015
friendliness) have scaling limits, in the sense that as a long fast
network (LFN) with a fixed RTT and MTU gets faster, these congestion
control algorithms get less accurate and as a consequence have
difficulty filling the network[CCscaling]. These properties are a
consequence of the original Reno AIMD congestion control design and
the requirement in [RFC5681] that all transport protocols have
uniform response to congestion.
There are a number of reasons to want to specify performance in term
of multiple concurrent flows, however this approach is not
recommended for data rates below several megabits per second, which
can be attained with run lengths under 10000 packets. Since the
required run length goes as the square of the data rate, at higher
rates the run lengths can be unreasonably large, and multiple
connection might be the only feasible approach.
If multiple connections are deemed necessary to meet aggregate
performance targets then this MUST be stated both the design of the
TDS and in any claims about network performance. The tests MUST be
performed concurrently with the specified number of connections. For
the the tests that use bursty traffic, the bursts should be
synchronized across flows.
8. Interpreting the Results
8.1. Test outcomes
To perform an exhaustive test of a complete network path, each test
of the TDS is applied to each subpath of the complete path. If any
subpath fails any test then an application running over the complete
path can also be expected to fail to attain the target performance
under some conditions.
In addition to passing or failing, a test can be deemed to be
inconclusive for a number of reasons. Proper instrumentation and
treatment of inconclusive outcomes is critical to the accuracy and
robustness of Model Based Metrics. Tests can be inconclusive if the
precomputed traffic pattern or data rates were not accurately
generated; the measurement results were not statistically
significant; and others causes such as failing to meet some required
preconditions for the test.
For example consider a test that implements Constant Window Pseudo
CBR (Section 7.2) by adding rate controls and detailed traffic
instrumentation to TCP (e.g. [RFC4898]). TCP includes built in
control systems which might interfere with the sending data rate. If
such a test meets the required delivery statistics (e.g. run length)
Mathis & Morton Expires December 15, 2015 [Page 24]
Internet-Draft Model Based Metrics June 2015
while failing to attain the specified data rate it must be treated as
an inconclusive result, because we can not a priori determine if the
reduced data rate was caused by a TCP problem or a network problem,
or if the reduced data rate had a material effect on the observed
delivery statistics.
Note that for capacity tests, if the observed delivery statistics
fail to meet the targets, the test can can be considered to have
failed because it doesn't really matter that the test didn't attain
the required data rate.
The really important new properties of MBM, such as vantage
independence, are a direct consequence of opening the control loops
in the protocols, such that the test traffic does not depend on
network conditions or traffic received. Any mechanism that
introduces feedback between the paths measurements and the traffic
generation is at risk of introducing nonlinearities that spoil these
properties. Any exceptional event that indicates that such feedback
has happened should cause the test to be considered inconclusive.
One way to view inconclusive tests is that they reflect situations
where a test outcome is ambiguous between limitations of the network
and some unknown limitation of the diagnostic test itself, which may
have been caused by some uncontrolled feedback from the network.
Note that procedures that attempt to sweep the target parameter space
to find the limits on some parameter such as target_data_rate are at
risk of breaking the location independent properties of Model Based
Metrics, if the boundary between passing and inconclusive is at all
sensitive to RTT.
One of the goals for evolving TDS designs will be to keep sharpening
distinction between inconclusive, passing and failing tests. The
criteria for for passing, failing and inconclusive tests MUST be
explicitly stated for every test in the TDS or FSTDS.
One of the goals of evolving the testing process, procedures, tools
and measurement point selection should be to minimize the number of
inconclusive tests.
It may be useful to keep raw data delivery statistics for deeper
study of the behavior of the network path and to measure the tools
themselves. Raw delivery statistics can help to drive tool
evolution. Under some conditions it might be possible to reevaluate
the raw data for satisfying alternate performance targets. However
it is important to guard against sampling bias and other implicit
feedback which can cause false results and exhibit measurement point
vantage sensitivity.
Mathis & Morton Expires December 15, 2015 [Page 25]
Internet-Draft Model Based Metrics June 2015
8.2. Statistical criteria for estimating run_length
When evaluating the observed run_length, we need to determine
appropriate packet stream sizes and acceptable error levels for
efficient measurement. In practice, can we compare the empirically
estimated packet loss and ECN marking ratios with the targets as the
sample size grows? How large a sample is needed to say that the
measurements of packet transfer indicate a particular run length is
present?
The generalized measurement can be described as recursive testing:
send packets (individually or in patterns) and observe the packet
delivery performance (loss ratio or other metric, any marking we
define).
As each packet is sent and measured, we have an ongoing estimate of
the performance in terms of the ratio of packet loss or ECN mark to
total packets (i.e. an empirical probability). We continue to send
until conditions support a conclusion or a maximum sending limit has
been reached.
We have a target_mark_probability, 1 mark per target_run_length,
where a "mark" is defined as a lost packet, a packet with ECN mark,
or other signal. This constitutes the null Hypothesis:
H0: no more than one mark in target_run_length =
3*(target_pipe_size)^2 packets
and we can stop sending packets if on-going measurements support
accepting H0 with the specified Type I error = alpha (= 0.05 for
example).
We also have an alternative Hypothesis to evaluate: if performance is
significantly lower than the target_mark_probability. Based on
analysis of typical values and practical limits on measurement
duration, we choose four times the H0 probability:
H1: one or more marks in (target_run_length/4) packets
and we can stop sending packets if measurements support rejecting H0
with the specified Type II error = beta (= 0.05 for example), thus
preferring the alternate hypothesis H1.
H0 and H1 constitute the Success and Failure outcomes described
elsewhere in the memo, and while the ongoing measurements do not
support either hypothesis the current status of measurements is
inconclusive.
Mathis & Morton Expires December 15, 2015 [Page 26]
Internet-Draft Model Based Metrics June 2015
The problem above is formulated to match the Sequential Probability
Ratio Test (SPRT) [StatQC]. Note that as originally framed the
events under consideration were all manufacturing defects. In
networking, ECN marks and lost packets are not defects but signals,
indicating that the transport protocol should slow down.
The Sequential Probability Ratio Test also starts with a pair of
hypothesis specified as above:
H0: p0 = one defect in target_run_length
H1: p1 = one defect in target_run_length/4
As packets are sent and measurements collected, the tester evaluates
the cumulative defect count against two boundaries representing H0
Acceptance or Rejection (and acceptance of H1):
Acceptance line: Xa = -h1 + s*n
Rejection line: Xr = h2 + s*n
where n increases linearly for each packet sent and
h1 = { log((1-alpha)/beta) }/k
h2 = { log((1-beta)/alpha) }/k
k = log{ (p1(1-p0)) / (p0(1-p1)) }
s = [ log{ (1-p0)/(1-p1) } ]/k
for p0 and p1 as defined in the null and alternative Hypotheses
statements above, and alpha and beta as the Type I and Type II
errors.
The SPRT specifies simple stopping rules:
o Xa < defect_count(n) < Xb: continue testing
o defect_count(n) <= Xa: Accept H0
o defect_count(n) >= Xb: Accept H1
The calculations above are implemented in the R-tool for Statistical
Analysis [Rtool] , in the add-on package for Cross-Validation via
Sequential Testing (CVST) [CVST] .
Using the equations above, we can calculate the minimum number of
packets (n) needed to accept H0 when x defects are observed. For
example, when x = 0:
Xa = 0 = -h1 + s*n
and n = h1 / s
8.3. Reordering Tolerance
All tests must be instrumented for packet level reordering [RFC4737].
However, there is no consensus for how much reordering should be
Mathis & Morton Expires December 15, 2015 [Page 27]
Internet-Draft Model Based Metrics June 2015
acceptable. Over the last two decades the general trend has been to
make protocols and applications more tolerant to reordering (see for
example [RFC4015]), in response to the gradual increase in reordering
in the network. This increase has been due to the deployment of
technologies such as multi threaded routing lookups and Equal Cost
MultiPath (ECMP) routing. These techniques increase parallelism in
network and are critical to enabling overall Internet growth to
exceed Moore's Law.
Note that transport retransmission strategies can trade off
reordering tolerance vs how quickly they can repair losses vs
overhead from spurious retransmissions. In advance of new
retransmission strategies we propose the following strawman:
Transport protocols should be able to adapt to reordering as long as
the reordering extent is no more than the maximum of one quarter
window or 1 mS, whichever is larger. Within this limit on reorder
extent, there should be no bound on reordering density.
By implication, recording which is less than these bounds should not
be treated as a network impairment. However [RFC4737] still applies:
reordering should be instrumented and the maximum reordering that can
be properly characterized by the test (e.g. bound on history buffers)
should be recorded with the measurement results.
Reordering tolerance and diagnostic limitations, such as history
buffer size, MUST be specified in a FSTDS.
9. Test Preconditions
Many tests have preconditions which are required to assure their
validity. For example the presence or nonpresence of cross traffic
on specific subpaths, or appropriate preloading to put reactive
network elements into the proper states[RFC7312]). If preconditions
are not properly satisfied for some reason, the tests should be
considered to be inconclusive. In general it is useful to preserve
diagnostic information about why the preconditions were not met, and
any test data that was collected even if it is not useful for the
intended test. Such diagnostic information and partial test data may
be useful for improving the test in the future.
It is important to preserve the record that a test was scheduled,
because otherwise precondition enforcement mechanisms can introduce
sampling bias. For example, canceling tests due to cross traffic on
subscriber access links might introduce sampling bias of tests of the
rest of the network by reducing the number of tests during peak
network load.
Mathis & Morton Expires December 15, 2015 [Page 28]
Internet-Draft Model Based Metrics June 2015
Test preconditions and failure actions MUST be specified in a FSTDS.
10. Diagnostic Tests
The diagnostic tests below are organized by traffic pattern: basic
data rate and delivery statistics, standing queues, slowstart bursts,
and sender rate bursts. We also introduce some combined tests which
are more efficient when networks are expected to pass, but conflate
diagnostic signatures when they fail.
There are a number of test details which are not fully defined here.
They must be fully specified in a FSTDS. From a standardization
perspective, this lack of specificity will weaken this version of
Model Based Metrics, however it is anticipated that this it be more
than offset by the extent to which MBM suppresses the problems caused
by using transport protocols for measurement. e.g. non-specific MBM
metrics are likely to have better repeatability than many existing
BTC like metrics. Once we have good field experience, the missing
details can be fully specified.
10.1. Basic Data Rate and Delivery Statistics Tests
We propose several versions of the basic data rate and delivery
statistics test. All measure the number of packets delivered between
losses or ECN marks, using a data stream that is rate controlled at
or below the target_data_rate.
The tests below differ in how the data rate is controlled. The data
can be paced on a timer, or window controlled at full target data
rate. The first two tests implicitly confirm that sub_path has
sufficient raw capacity to carry the target_data_rate. They are
recommend for relatively infrequent testing, such as an installation
or periodic auditing process. The third, background delivery
statistics, is a low rate test designed for ongoing monitoring for
changes in subpath quality.
All rely on the receiver accumulating packet delivery statistics as
described in Section 8.2 to score the outcome:
Pass: it is statistically significant that the observed interval
between losses or ECN marks is larger than the target_run_length.
Fail: it is statistically significant that the observed interval
between losses or ECN marks is smaller than the target_run_length.
A test is considered to be inconclusive if it failed to meet the data
rate as specified below, meet the qualifications defined in Section 9
Mathis & Morton Expires December 15, 2015 [Page 29]
Internet-Draft Model Based Metrics June 2015
or neither run length statistical hypothesis was confirmed in the
allotted test duration.
10.1.1. Delivery Statistics at Paced Full Data Rate
Confirm that the observed run length is at least the
target_run_length while relying on timer to send data at the
target_rate using the procedure described in in Section 7.1 with a
burst size of 1 (single packets) or 2 (packet pairs).
The test is considered to be inconclusive if the packet transmission
can not be accurately controlled for any reason.
RFC 6673 [RFC6673] is appropriate for measuring delivery statistics
at full data rate.
10.1.2. Delivery Statistics at Full Data Windowed Rate
Confirm that the observed run length is at least the
target_run_length while sending at an average rate approximately
equal to the target_data_rate, by controlling (or clamping) the
window size of a conventional transport protocol to a fixed value
computed from the properties of the test path, typically
test_window=target_data_rate*test_RTT/target_MTU. Note that if there
is any interaction between the forward and return path, test_window
may need to be adjusted slightly to compensate for the resulting
inflated RTT.
Since losses and ECN marks generally cause transport protocols to at
least temporarily reduce their data rates, this test is expected to
be less precise about controlling its data rate. It should not be
considered inconclusive as long as at least some of the round trips
reached the full target_data_rate without incurring losses or ECN
marks. To pass this test the network MUST deliver target_pipe_size
packets in target_RTT time without any losses or ECN marks at least
once per two target_pipe_size round trips, in addition to meeting the
run length statistical test.
10.1.3. Background Delivery Statistics Tests
The background run length is a low rate version of the target target
rate test above, designed for ongoing lightweight monitoring for
changes in the observed subpath run length without disrupting users.
It should be used in conjunction with one of the above full rate
tests because it does not confirm that the subpath can support raw
data rate.
RFC 6673 [RFC6673] is appropriate for measuring background delivery
Mathis & Morton Expires December 15, 2015 [Page 30]
Internet-Draft Model Based Metrics June 2015
statistics.
10.2. Standing Queue Tests
These engineering tests confirm that the bottleneck is well behaved
across the onset of packet loss, which typically follows after the
onset of queueing. Well behaved generally means lossless for
transient queues, but once the queue has been sustained for a
sufficient period of time (or reaches a sufficient queue depth) there
should be a small number of losses to signal to the transport
protocol that it should reduce its window. Losses that are too early
can prevent the transport from averaging at the target_data_rate.
Losses that are too late indicate that the queue might be subject to
bufferbloat [wikiBloat] and inflict excess queuing delays on all
flows sharing the bottleneck queue. Excess losses (more than half of
the window) at the onset of congestion make loss recovery problematic
for the transport protocol. Non-linear, erratic or excessive RTT
increases suggest poor interactions between the channel acquisition
algorithms and the transport self clock. All of the tests in this
section use the same basic scanning algorithm, described here, but
score the link on the basis of how well it avoids each of these
problems.
For some technologies the data might not be subject to increasing
delays, in which case the data rate will vary with the window size
all the way up to the onset of load induced losses or ECN marks. For
theses technologies, the discussion of queueing does not apply, but
it is still required that the onset of losses or ECN marks be at an
appropriate point and progressive.
Use the procedure in Section 7.3 to sweep the window across the onset
of queueing and the onset of loss. The tests below all assume that
the scan emulates standard additive increase and delayed ACK by
incrementing the window by one packet for every 2*target_pipe_size
packets delivered. A scan can typically be divided into three
regions: below the onset of queueing, a standing queue, and at or
beyond the onset of loss.
Below the onset of queueing the RTT is typically fairly constant, and
the data rate varies in proportion to the window size. Once the data
rate reaches the link rate, the data rate becomes fairly constant,
and the RTT increases in proportion to the increase in window size.
The precise transition across the start of queueing can be identified
by the maximum network power, defined to be the ratio data rate over
the RTT. The network power can be computed at each window size, and
the window with the maximum are taken as the start of the queueing
region.
Mathis & Morton Expires December 15, 2015 [Page 31]
Internet-Draft Model Based Metrics June 2015
For technologies that do not have conventional queues, start the scan
at a window equal to the test_window=target_data_rate*test_RTT/
target_MTU, i.e. starting at the target rate, instead of the power
point.
If there is random background loss (e.g. bit errors, etc), precise
determination of the onset of queue induced packet loss may require
multiple scans. Above the onset of queuing loss, all transport
protocols are expected to experience periodic losses determined by
the interaction between the congestion control and AQM algorithms.
For standard congestion control algorithms the periodic losses are
likely to be relatively widely spaced and the details are typically
dominated by the behavior of the transport protocol itself. For the
stiffened transport protocols case (with non-standard, aggressive
congestion control algorithms) the details of periodic losses will be
dominated by how the the window increase function responds to loss.
10.2.1. Congestion Avoidance
A link passes the congestion avoidance standing queue test if more
than target_run_length packets are delivered between the onset of
queueing (as determined by the window with the maximum network power)
and the first loss or ECN mark. If this test is implemented using a
standards congestion control algorithm with a clamp, it can be
performed in situ in the production internet as a capacity test. For
an example of such a test see [Pathdiag].
For technologies that do not have conventional queues, use the
test_window inplace of the onset of queueing. i.e. A link passes the
congestion avoidance standing queue test if more than
target_run_length packets are delivered between start of the scan at
test_window and the first loss or ECN mark.
10.2.2. Bufferbloat
This test confirms that there is some mechanism to limit buffer
occupancy (e.g. that prevents bufferbloat). Note that this is not
strictly a requirement for single stream bulk performance, however if
there is no mechanism to limit buffer queue occupancy then a single
stream with sufficient data to deliver is likely to cause the
problems described in [RFC2309], [I-D.ietf-aqm-recommendation] and
[wikiBloat]. This may cause only minor symptoms for the dominant
flow, but has the potential to make the link unusable for other flows
and applications.
Pass if the onset of loss occurs before a standing queue has
introduced more delay than than twice target_RTT, or other well
defined and specified limit. Note that there is not yet a model for
Mathis & Morton Expires December 15, 2015 [Page 32]
Internet-Draft Model Based Metrics June 2015
how much standing queue is acceptable. The factor of two chosen here
reflects a rule of thumb. In conjunction with the previous test,
this test implies that the first loss should occur at a queueing
delay which is between one and two times the target_RTT.
Specified RTT limits that are larger than twice the target_RTT must
be fully justified in the FSTDS.
10.2.3. Non excessive loss
This test confirm that the onset of loss is not excessive. Pass if
losses are equal or less than the increase in the cross traffic plus
the test traffic window increase on the previous RTT. This could be
restated as non-decreasing link throughput at the onset of loss,
which is easy to meet as long as discarding packets in not more
expensive than delivering them. (Note when there is a transient drop
in link throughput, outside of a standing queue test, a link that
passes other queue tests in this document will have sufficient queue
space to hold one RTT worth of data).
Note that conventional Internet traffic policers will not pass this
test, which is correct. TCP often fails to come into equilibrium at
more than a small fraction of the available capacity, if the capacity
is enforced by a policer. [Citation Pending].
10.2.4. Duplex Self Interference
This engineering test confirms a bound on the interactions between
the forward data path and the ACK return path.
Some historical half duplex technologies had the property that each
direction held the channel until it completely drains its queue.
When a self clocked transport protocol, such as TCP, has data and
acks passing in opposite directions through such a link, the behavior
often reverts to stop-and-wait. Each additional packet added to the
window raises the observed RTT by two forward path packet times, once
as it passes through the data path, and once for the additional delay
incurred by the ACK waiting on the return path.
The duplex self interference test fails if the RTT rises by more than
some fixed bound above the expected queueing time computed from trom
the excess window divided by the link data rate. This bound must be
smaller than target_RTT/2 to avoid reverting to stop and wait
behavior. (e.g. Packets have to be released at least twice per RTT,
to avoid stop and wait behavior.)
Mathis & Morton Expires December 15, 2015 [Page 33]
Internet-Draft Model Based Metrics June 2015
10.3. Slowstart tests
These tests mimic slowstart: data is sent at twice the effective
bottleneck rate to exercise the queue at the dominant bottleneck.
In general they are deemed inconclusive if the elapsed time to send
the data burst is not less than half of the time to receive the ACKs.
(i.e. sending data too fast is ok, but sending it slower than twice
the actual bottleneck rate as indicated by the ACKs is deemed
inconclusive). Space the bursts such that the average data rate is
equal to the target_data_rate.
10.3.1. Full Window slowstart test
This is a capacity test to confirm that slowstart is not likely to
exit prematurely. Send slowstart bursts that are target_pipe_size
total packets.
Accumulate packet delivery statistics as described in Section 8.2 to
score the outcome. Pass if it is statistically significant that the
observed number of good packets delivered between losses or ECN marks
is larger than the target_run_length. Fail if it is statistically
significant that the observed interval between losses or ECN marks is
smaller than the target_run_length.
Note that these are the same parameters as the Sender Full Window
burst test, except the burst rate is at slowestart rate, rather than
sender interface rate.
10.3.2. Slowstart AQM test
Do a continuous slowstart (send data continuously at slowstart_rate),
until the first loss, stop, allow the network to drain and repeat,
gathering statistics on the last packet delivered before the loss,
the loss pattern, maximum observed RTT and window size. Justify the
results. There is not currently sufficient theory justifying
requiring any particular result, however design decisions that affect
the outcome of this tests also affect how the network balances
between long and short flows (the "mice and elephants" problem). The
queue at the time of the first loss should be at least one half of
the target_RTT.
This is an engineering test: It would be best performed on a
quiescent network or testbed, since cross traffic has the potential
to change the results.
Mathis & Morton Expires December 15, 2015 [Page 34]
Internet-Draft Model Based Metrics June 2015
10.4. Sender Rate Burst tests
These tests determine how well the network can deliver bursts sent at
sender's interface rate. Note that this test most heavily exercises
the front path, and is likely to include infrastructure may be out of
scope for an access ISP, even though the bursts might be caused by
ACK compression, thinning or channel arbitration in the access ISP.
See Appendix B.
Also, there are a several details that are not precisely defined.
For starters there is not a standard server interface rate. 1 Gb/s
and 10 Gb/s are very common today, but higher rates will become cost
effective and can be expected to be dominant some time in the future.
Current standards permit TCP to send a full window bursts following
an application pause. (Congestion Window Validation [RFC2861], is
not required, but even if was, it does not take effect until an
application pause is longer than an RTO.) Since full window bursts
are consistent with standard behavior, it is desirable that the
network be able to deliver such bursts, otherwise application pauses
will cause unwarranted losses. Note that the AIMD sawtooth requires
a peak window that is twice target_pipe_size, so the worst case burst
may be 2*target_pipe_size.
It is also understood in the application and serving community that
interface rate bursts have a cost to the network that has to be
balanced against other costs in the servers themselves. For example
TCP Segmentation Offload (TSO) reduces server CPU in exchange for
larger network bursts, which increase the stress on network buffer
memory.
There is not yet theory to unify these costs or to provide a
framework for trying to optimize global efficiency. We do not yet
have a model for how much the network should tolerate server rate
bursts. Some bursts must be tolerated by the network, but it is
probably unreasonable to expect the network to be able to efficiently
deliver all data as a series of bursts.
For this reason, this is the only test for which we encourage
derating. A TDS could include a table of pairs of derating
parameters: what burst size to use as a fraction of the
target_pipe_size, and how much each burst size is permitted to reduce
the run length, relative to to the target_run_length.
10.5. Combined and Implicit Tests
Combined tests efficiently confirm multiple network properties in a
single test, possibly as a side effect of normal content delivery.
Mathis & Morton Expires December 15, 2015 [Page 35]
Internet-Draft Model Based Metrics June 2015
They require less measurement traffic than other testing strategies
at the cost of conflating diagnostic signatures when they fail.
These are by far the most efficient for monitoring networks that are
nominally expected to pass all tests.
10.5.1. Sustained Bursts Test
The sustained burst test implements a combined worst case version of
all of the capacity tests above. It is simply:
Send target_pipe_size bursts of packets at server interface rate with
target_RTT burst headway (burst start to burst start). Verify that
the observed delivery statistics meets the target_run_length.
Key observations:
o The subpath under test is expected to go idle for some fraction of
the time: (subpath_data_rate-target_rate)/subpath_data_rate.
Failing to do so indicates a problem with the procedure and an
inconclusive test result.
o The burst sensitivity can be derated by sending smaller bursts
more frequently. E.g. send target_pipe_size*derate packet bursts
every target_RTT*derate.
o When not derated, this test is the most strenuous capacity test.
o A link that passes this test is likely to be able to sustain
higher rates (close to subpath_data_rate) for paths with RTTs
significantly smaller than the target_RTT.
o This test can be implemented with instrumented TCP [RFC4898],
using a specialized measurement application at one end [MBMSource]
and a minimal service at the other end [RFC0863] [RFC0864].
o This test is efficient to implement, since it does not require
per-packet timers, and can make use of TSO in modern NIC hardware.
o This test by itself is not sufficient: the standing window
engineering tests are also needed to ensure that the link is well
behaved at and beyond the onset of congestion.
o Assuming the link passes relevant standing window engineering
tests (particularly that it has a progressive onset of loss at an
appropriate queue depth) the passing sustained burst test is
(believed to be) a sufficient verify that the subpath will not
impair stream at the target performance under all conditions.
Proving this statement will be subject of ongoing research.
Note that this test is clearly independent of the subpath RTT, or
other details of the measurement infrastructure, as long as the
measurement infrastructure can accurately and reliably deliver the
required bursts to the subpath under test.
Mathis & Morton Expires December 15, 2015 [Page 36]
Internet-Draft Model Based Metrics June 2015
10.5.2. Streaming Media
Model Based Metrics can be implicitly implemented as a side effect of
serving any non-throughput maximizing traffic, such as streaming
media, with some additional controls and instrumentation in the
servers. The essential requirement is that the traffic be
constrained such that even with arbitrary application pauses, bursts
and data rate fluctuations, the traffic stays within the envelope
defined by the individual tests described above.
If the application's serving_data_rate is less than or equal to the
target_data_rate and the serving_RTT (the RTT between the sender and
client) is less than the target_RTT, this constraint is most easily
implemented by clamping the transport window size to be no larger
than:
serving_window_clamp=target_data_rate*serving_RTT/
(target_MTU-header_overhead)
Under the above constraints the serving_window_clamp will limit the
both the serving data rate and burst sizes to be no larger than the
procedures in Section 10.1.2 and Section 10.4 or Section 10.5.1.
Since the serving RTT is smaller than the target_RTT, the worst case
bursts that might be generated under these conditions will be smaller
than called for by Section 10.4 and the sender rate burst sizes are
implicitly derated by the serving_window_clamp divided by the
target_pipe_size at the very least. (Depending on the application
behavior, the data traffic might be significantly smoother than
specified by any of the burst tests.)
Note that it is important that the target_data_rate be above the
actual average rate needed by the application so it can recover after
transient pauses caused by congestion or the application itself.
In an alternative implementation the data rate and bursts might be
explicitly controlled by a host shaper or pacing at the sender. This
would provide better control over transmissions but it is
substantially more complicated to implement and would be likely to
have a higher CPU overhead.
Note that these techniques can be applied to any content delivery
that can be subjected to a reduced data rate in order to inhibit TCP
equilibrium behavior.
11. An Example
In this section a we illustrate a TDS designed to confirm that an
Mathis & Morton Expires December 15, 2015 [Page 37]
Internet-Draft Model Based Metrics June 2015
access ISP can reliably deliver HD video from multiple content
providers to all of their customers. With modern codecs, minimal HD
video (720p) generally fits in 2.5 Mb/s. Due to their geographical
size, network topology and modem designs the ISP determines that most
content is within a 50 mS RTT from their users (This is a sufficient
to cover continental Europe or either US coast from a single serving
site.)
2.5 Mb/s over a 50 ms path
+----------------------+-------+---------+
| End-to-End Parameter | value | units |
+----------------------+-------+---------+
| target_rate | 2.5 | Mb/s |
| target_RTT | 50 | ms |
| target_MTU | 1500 | bytes |
| header_overhead | 64 | bytes |
| target_pipe_size | 11 | packets |
| target_run_length | 363 | packets |
+----------------------+-------+---------+
Table 1
Table 1 shows the default TCP model with no derating, and as such is
quite conservative. The simplest TDS would be to use the sustained
burst test, described in Section 10.5.1. Such a test would send 11
packet bursts every 50mS, and confirming that there was no more than
1 packet loss per 33 bursts (363 total packets in 1.650 seconds).
Since this number represents is the entire end-to-end loss budget,
independent subpath tests could be implemented by apportioning the
loss ratio across subpaths. For example 50% of the losses might be
allocated to the access or last mile link to the user, 40% to the
interconnects with other ISPs and 1% to each internal hop (assuming
no more than 10 internal hops). Then all of the subpaths can be
tested independently, and the spatial composition of passing subpaths
would be expected to be within the end-to-end loss budget.
Testing interconnects has generally been problematic: conventional
performance tests run between Measurement Points adjacent to either
side of the interconnect, are not generally useful. Unconstrained
TCP tests, such as iperf [iperf] are usually overly aggressive
because the RTT is so small (often less than 1 mS). With a short RTT
these tools are likely to report inflated numbers because for short
RTTs these tools can tolerate very high loss ratio and can push other
cross traffic off of the network. As a consequence they are useless
for predicting actual user performance, and may themselves be quite
disruptive. Model Based Metrics solves this problem. The same test
Mathis & Morton Expires December 15, 2015 [Page 38]
Internet-Draft Model Based Metrics June 2015
pattern as used on other links can be applied to the interconnect.
For our example, when apportioned 40% of the losses, 11 packet bursts
sent every 50mS should have fewer than one loss per 82 bursts (902
packets).
12. Validation
Since some aspects of the models are likely to be too conservative,
Section 6.2 permits alternate protocol models and Section 6.3 permits
test parameter derating. If either of these techniques are used, we
require demonstrations that such a TDS can robustly detect links that
will prevent authentic applications using state-of-the-art protocol
implementations from meeting the specified performance targets. This
correctness criteria is potentially difficult to prove, because it
implicitly requires validating a TDS against all possible links and
subpaths. The procedures described here are still experimental.
We suggest two approaches, both of which should be applied: first,
publish a fully open description of the TDS, including what
assumptions were used and and how it was derived, such that the
research community can evaluate the design decisions, test them and
comment on their applicability; and second, demonstrate that an
applications running over an infinitessimally passing testbed do meet
the performance targets.
An infinitessimally passing testbed resembles a epsilon-delta proof
in calculus. Construct a test network such that all of the
individual tests of the TDS pass by only small (infinitesimal)
margins, and demonstrate that a variety of authentic applications
running over real TCP implementations (or other protocol as
appropriate) meets the target transport performance over such a
network. The workloads should include multiple types of streaming
media and transaction oriented short flows (e.g. synthetic web
traffic ).
For example, for the HD streaming video TDS described in Section 11,
the link layer bottleneck data rate should be exactly the header
overhead above 2.5 Mb/s, the per packet random background loss ratio
should be 1/363, for a run length of 363 packets, the bottleneck
queue should be 11 packets and the front path should have just enough
buffering to withstand 11 packet interface rate bursts. We want
every one of the TDS tests to fail if we slightly increase the
relevant test parameter, so for example sending a 12 packet bursts
should cause excess (possibly deterministic) packet drops at the
dominant queue at the bottleneck. On this infinitessimally passing
network it should be possible for a real application using a stock
TCP implementation in the vendor's default configuration to attain
Mathis & Morton Expires December 15, 2015 [Page 39]
Internet-Draft Model Based Metrics June 2015
2.5 Mb/s over an 50 mS path.
The most difficult part of setting up such a testbed is arranging for
it to infinitesimally pass the individual tests. Two approaches:
constraining the network devices not to use all available resources
(e.g. by limiting available buffer space or data rate); and
preloading subpaths with cross traffic. Note that is it important
that a single environment be constructed which infinitessimally
passes all tests at the same time, otherwise there is a chance that
TCP can exploit extra latitude in some parameters (such as data rate)
to partially compensate for constraints in other parameters (queue
space, or viceversa).
To the extent that a TDS is used to inform public dialog it should be
fully publicly documented, including the details of the tests, what
assumptions were used and how it was derived. All of the details of
the validation experiment should also be published with sufficient
detail for the experiments to be replicated by other researchers.
All components should either be open source of fully described
proprietary implementations that are available to the research
community.
13. Security Considerations
Measurement is often used to inform business and policy decisions,
and as a consequence is potentially subject to manipulation for
illicit gains. Model Based Metrics are expected to be a huge step
forward because equivalent measurements can be performed from
multiple vantage points, such that performance claims can be
independently validated by multiple parties.
Much of the acrimony in the Net Neutrality debate is due by the
historical lack of any effective vantage independent tools to
characterize network performance. Traditional methods for measuring
Bulk Transport Capacity are sensitive to RTT and as a consequence
often yield very different results local to an ISP and when run over
a customer's complete path. Neither the ISP nor customer can repeat
the other's measurements, leading to high levels of distrust and
acrimony. Model Based Metrics are expected to greatly improve this
situation.
This document only describes a framework for designing Fully
Specified Targeted Diagnostic Suite. Each FSTDS MUST include its own
security section.
Mathis & Morton Expires December 15, 2015 [Page 40]
Internet-Draft Model Based Metrics June 2015
14. Acknowledgements
Ganga Maguluri suggested the statistical test for measuring loss
probability in the target run length. Alex Gilgur for helping with
the statistics.
Meredith Whittaker for improving the clarity of the communications.
This work was inspired by Measurement Lab: open tools running on an
open platform, using open tools to collect open data. See
http://www.measurementlab.net/
15. IANA Considerations
This document has no actions for IANA.
16. References
16.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
16.2. Informative References
[RFC0863] Postel, J., "Discard Protocol", STD 21, RFC 863, May 1983.
[RFC0864] Postel, J., "Character Generator Protocol", STD 22,
RFC 864, May 1983.
[RFC2309] Braden, B., Clark, D., Crowcroft, J., Davie, B., Deering,
S., Estrin, D., Floyd, S., Jacobson, V., Minshall, G.,
Partridge, C., Peterson, L., Ramakrishnan, K., Shenker,
S., Wroclawski, J., and L. Zhang, "Recommendations on
Queue Management and Congestion Avoidance in the
Internet", RFC 2309, April 1998.
[RFC2330] Paxson, V., Almes, G., Mahdavi, J., and M. Mathis,
"Framework for IP Performance Metrics", RFC 2330,
May 1998.
[RFC2861] Handley, M., Padhye, J., and S. Floyd, "TCP Congestion
Window Validation", RFC 2861, June 2000.
[RFC3148] Mathis, M. and M. Allman, "A Framework for Defining
Empirical Bulk Transfer Capacity Metrics", RFC 3148,
Mathis & Morton Expires December 15, 2015 [Page 41]
Internet-Draft Model Based Metrics June 2015
July 2001.
[RFC3465] Allman, M., "TCP Congestion Control with Appropriate Byte
Counting (ABC)", RFC 3465, February 2003.
[RFC4015] Ludwig, R. and A. Gurtov, "The Eifel Response Algorithm
for TCP", RFC 4015, February 2005.
[RFC4737] Morton, A., Ciavattone, L., Ramachandran, G., Shalunov,
S., and J. Perser, "Packet Reordering Metrics", RFC 4737,
November 2006.
[RFC4898] Mathis, M., Heffner, J., and R. Raghunarayan, "TCP
Extended Statistics MIB", RFC 4898, May 2007.
[RFC5681] Allman, M., Paxson, V., and E. Blanton, "TCP Congestion
Control", RFC 5681, September 2009.
[RFC5835] Morton, A. and S. Van den Berghe, "Framework for Metric
Composition", RFC 5835, April 2010.
[RFC6049] Morton, A. and E. Stephan, "Spatial Composition of
Metrics", RFC 6049, January 2011.
[RFC6673] Morton, A., "Round-Trip Packet Loss Metrics", RFC 6673,
August 2012.
[RFC7312] Fabini, J. and A. Morton, "Advanced Stream and Sampling
Framework for IP Performance Metrics (IPPM)", RFC 7312,
August 2014.
[RFC7398] Bagnulo, M., Burbridge, T., Crawford, S., Eardley, P., and
A. Morton, "A Reference Path and Measurement Points for
Large-Scale Measurement of Broadband Performance",
RFC 7398, February 2015.
[I-D.ietf-aqm-recommendation]
Baker, F. and G. Fairhurst, "IETF Recommendations
Regarding Active Queue Management",
draft-ietf-aqm-recommendation-11 (work in progress),
February 2015.
[MSMO97] Mathis, M., Semke, J., Mahdavi, J., and T. Ott, "The
Macroscopic Behavior of the TCP Congestion Avoidance
Algorithm", Computer Communications Review volume 27,
number3, July 1997.
[WPING] Mathis, M., "Windowed Ping: An IP Level Performance
Mathis & Morton Expires December 15, 2015 [Page 42]
Internet-Draft Model Based Metrics June 2015
Diagnostic", INET 94, June 1994.
[mpingSource]
Fan, X., Mathis, M., and D. Hamon, "Git Repository for
mping: An IP Level Performance Diagnostic", Sept 2013,
<https://github.com/m-lab/mping>.
[MBMSource]
Hamon, D., Stuart, S., and H. Chen, "Git Repository for
Model Based Metrics", Sept 2013,
<https://github.com/m-lab/MBM>.
[Pathdiag]
Mathis, M., Heffner, J., O'Neil, P., and P. Siemsen,
"Pathdiag: Automated TCP Diagnosis", Passive and Active
Measurement , June 2008.
[iperf] Wikipedia Contributors, "iPerf", Wikipedia, The Free
Encyclopedia , cited March 2015, <http://en.wikipedia.org/
w/index.php?title=Iperf&oldid=649720021>.
[StatQC] Montgomery, D., "Introduction to Statistical Quality
Control - 2nd ed.", ISBN 0-471-51988-X, 1990.
[Rtool] R Development Core Team, "R: A language and environment
for statistical computing. R Foundation for Statistical
Computing, Vienna, Austria. ISBN 3-900051-07-0, URL
http://www.R-project.org/", , 2011.
[CVST] Krueger, T. and M. Braun, "R package: Fast Cross-
Validation via Sequential Testing", version 0.1, 11 2012.
[AFD] Pan, R., Breslau, L., Prabhakar, B., and S. Shenker,
"Approximate fairness through differential dropping",
SIGCOMM Comput. Commun. Rev. 33, 2, April 2003.
[wikiBloat]
Wikipedia, "Bufferbloat", http://en.wikipedia.org/w/
index.php?title=Bufferbloat&oldid=608805474, March 2015.
[CCscaling]
Fernando, F., Doyle, J., and S. Steven, "Scalable laws for
stable network congestion control", Proceedings of
Conference on Decision and
Control, http://www.ee.ucla.edu/~paganini, December 2001.
Mathis & Morton Expires December 15, 2015 [Page 43]
Internet-Draft Model Based Metrics June 2015
Appendix A. Model Derivations
The reference target_run_length described in Section 6.2 is based on
very conservative assumptions: that all window above target_pipe_size
contributes to a standing queue that raises the RTT, and that classic
Reno congestion control with delayed ACKs are in effect. In this
section we provide two alternative calculations using different
assumptions.
It may seem out of place to allow such latitude in a measurement
standard, but this section provides offsetting requirements.
The estimates provided by these models make the most sense if network
performance is viewed logarithmically. In the operational Internet,
data rates span more than 8 orders of magnitude, RTT spans more than
3 orders of magnitude, and loss ratio spans at least 8 orders of
magnitude. When viewed logarithmically (as in decibels), these
correspond to 80 dB of dynamic range. On an 80 db scale, a 3 dB
error is less than 4% of the scale, even though it might represent a
factor of 2 in untransformed parameter.
This document gives a lot of latitude for calculating
target_run_length, however people designing a TDS should consider the
effect of their choices on the ongoing tussle about the relevance of
"TCP friendliness" as an appropriate model for Internet capacity
allocation. Choosing a target_run_length that is substantially
smaller than the reference target_run_length specified in Section 6.2
strengthens the argument that it may be appropriate to abandon "TCP
friendliness" as the Internet fairness model. This gives developers
incentive and permission to develop even more aggressive applications
and protocols, for example by increasing the number of connections
that they open concurrently.
A.1. Queueless Reno
In Section 6.2 it was assumed that the link rate matches the target
rate plus overhead, such that the excess window needed for the AIMD
sawtooth causes a fluctuating queue at the bottleneck.
An alternate situation would be bottleneck where there is no
significant queue and losses are caused by some mechanism that does
not involve extra delay, for example by the use of a virtual queue as
in Approximate Fair Dropping[AFD]. A flow controlled by such a
bottleneck would have a constant RTT and a data rate that fluctuates
in a sawtooth due to AIMD congestion control. Assume the losses are
being controlled to make the average data rate meet some goal which
is equal or greater than the target_rate. The necessary run length
can be computed as follows:
Mathis & Morton Expires December 15, 2015 [Page 44]
Internet-Draft Model Based Metrics June 2015
For some value of Wmin, the window will sweep from Wmin packets to
2*Wmin packets in 2*Wmin RTT (due to delayed ACK). Unlike the
queueing case where Wmin = Target_pipe_size, we want the average of
Wmin and 2*Wmin to be the target_pipe_size, so the average rate is
the target rate. Thus we want Wmin = (2/3)*target_pipe_size.
Between losses each sawtooth delivers (1/2)(Wmin+2*Wmin)(2Wmin)
packets in 2*Wmin round trip times.
Substituting these together we get:
target_run_length = (4/3)(target_pipe_size^2)
Note that this is 44% of the reference_run_length computed earlier.
This makes sense because under the assumptions in Section 6.2 the
AMID sawtooth caused a queue at the bottleneck, which raised the
effective RTT by 50%.
Appendix B. Complex Queueing
For many network technologies simple queueing models don't apply: the
network schedules, thins or otherwise alters the timing of ACKs and
data, generally to raise the efficiency of the channel allocation
when confronted with relatively widely spaced small ACKs. These
efficiency strategies are ubiquitous for half duplex, wireless and
broadcast media.
Altering the ACK stream generally has two consequences: it raises the
effective bottleneck data rate, making slowstart burst at higher
rates (possibly as high as the sender's interface rate) and it
effectively raises the RTT by the average time that the ACKs and data
were delayed. The first effect can be partially mitigated by
reclocking ACKs once they are beyond the bottleneck on the return
path to the sender, however this further raises the effective RTT.
The most extreme example of this sort of behavior would be a half
duplex channel that is not released as long as end point currently
holding the channel has more traffic (data or ACKs) to send. Such
environments cause self clocked protocols under full load to revert
to extremely inefficient stop and wait behavior, where they send an
entire window of data as a single burst of the forward path, followed
by the entire window of ACKs on the return path. It is important to
note that due to self clocking, ill conceived channel allocation
mechanisms can increase the stress on upstream links in a long path:
they cause large and faster bursts.
If a particular return path contains a link or device that alters the
Mathis & Morton Expires December 15, 2015 [Page 45]
Internet-Draft Model Based Metrics June 2015
ACK stream, then the entire path from the sender up to the bottleneck
must be tested at the burst parameters implied by the ACK scheduling
algorithm. The most important parameter is the Effective Bottleneck
Data Rate, which is the average rate at which the ACKs advance
snd.una. Note that thinning the ACKs (relying on the cumulative
nature of seg.ack to permit discarding some ACKs) is implies an
effectively infinite bottleneck data rate.
Holding data or ACKs for channel allocation or other reasons (such as
forward error correction) always raises the effective RTT relative to
the minimum delay for the path. Therefore it may be necessary to
replace target_RTT in the calculation in Section 6.2 by an
effective_RTT, which includes the target_RTT plus a term to account
for the extra delays introduced by these mechanisms.
Appendix C. Version Control
This section to be removed prior to publication.
Formatted: Sat Jun 13 16:25:01 PDT 2015
Authors' Addresses
Matt Mathis
Google, Inc
1600 Amphitheater Parkway
Mountain View, California 94043
USA
Email: mattmathis@google.com
Al Morton
AT&T Labs
200 Laurel Avenue South
Middletown, NJ 07748
USA
Phone: +1 732 420 1571
Email: acmorton@att.com
URI: http://home.comcast.net/~acmacm/
Mathis & Morton Expires December 15, 2015 [Page 46]