Model Based Bulk Performance Metrics
draft-ietf-ippm-model-based-metrics-02
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| Document | Type | Active Internet-Draft (ippm WG) | |
|---|---|---|---|
| Authors | Matt Mathis , Al Morton | ||
| Last updated | 2014-02-14 | ||
| Replaces | draft-mathis-ippm-model-based-metrics | ||
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draft-ietf-ippm-model-based-metrics-02
IP Performance Working Group M. Mathis
Internet-Draft Google, Inc
Intended status: Experimental A. Morton
Expires: August 18, 2014 AT&T Labs
February 14, 2014
Model Based Bulk Performance Metrics
draft-ietf-ippm-model-based-metrics-02.txt
Abstract
We introduce a new class of model based metrics designed to determine
if an end-to-end Internet path can meet predefined transport
performance targets by applying a suite of IP diagnostic tests to
successive subpaths. The subpath-at-a-time tests are designed to
accurately detect if any subpath will prevent the full end-to-end
path from meeting the specified target performance. Each IP
diagnostic test consists of a precomputed traffic pattern and a
statistical criteria for evaluating packet delivery.
The IP diagnostics tests are based on traffic patterns that are
precomputed to mimic TCP or other transport protocol over a long path
but are independent of the actual details of the subpath under test.
Likewise the success criteria depends on the target performance and
not the actual performance of the subpath. This makes the
measurements open loop, eliminating nearly all of the difficulties
encountered by traditional bulk transport metrics.
This document does not fully define diagnostic tests, but provides a
framework for designing suites of diagnostics tests that are tailored
the confirming the target performance.
By making the tests open loop, we eliminate standards congestion
control equilibrium behavior, which otherwise causes every measured
parameter to be sensitive to every component of the system. As an
open loop test, various measurable properties become independent, and
potentially subject to an algebra enabling several important new
uses.
Interim DRAFT Formatted: Fri Feb 14 14:07:33 PST 2014
Status of this Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
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Task Force (IETF). Note that other groups may also distribute
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Internet-Drafts are draft documents valid for a maximum of six months
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material or to cite them other than as "work in progress."
This Internet-Draft will expire on August 18, 2014.
Copyright Notice
Copyright (c) 2014 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
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described in the Simplified BSD License.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.1. TODO . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 7
3. New requirements relative to RFC 2330 . . . . . . . . . . . . 10
4. Background . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.1. TCP properties . . . . . . . . . . . . . . . . . . . . . . 12
4.2. Diagnostic Approach . . . . . . . . . . . . . . . . . . . 13
5. Common Models and Parameters . . . . . . . . . . . . . . . . . 15
5.1. Target End-to-end parameters . . . . . . . . . . . . . . . 15
5.2. Common Model Calculations . . . . . . . . . . . . . . . . 15
5.3. Parameter Derating . . . . . . . . . . . . . . . . . . . . 16
6. Common testing procedures . . . . . . . . . . . . . . . . . . 17
6.1. Traffic generating techniques . . . . . . . . . . . . . . 17
6.1.1. Paced transmission . . . . . . . . . . . . . . . . . . 17
6.1.2. Constant window pseudo CBR . . . . . . . . . . . . . . 18
6.1.3. Scanned window pseudo CBR . . . . . . . . . . . . . . 18
6.1.4. Concurrent or channelized testing . . . . . . . . . . 19
6.1.5. Intermittent Testing . . . . . . . . . . . . . . . . . 19
6.1.6. Intermittent Scatter Testing . . . . . . . . . . . . . 20
6.2. Interpreting the Results . . . . . . . . . . . . . . . . . 20
6.2.1. Test outcomes . . . . . . . . . . . . . . . . . . . . 20
6.2.2. Statistical criteria for measuring run_length . . . . 22
6.2.2.1. Alternate criteria for measuring run_length . . . 24
6.2.3. Reordering Tolerance . . . . . . . . . . . . . . . . . 25
6.3. Test Qualifications . . . . . . . . . . . . . . . . . . . 26
7. Diagnostic Tests . . . . . . . . . . . . . . . . . . . . . . . 27
7.1. Basic Data Rate and Run Length Tests . . . . . . . . . . . 27
7.1.1. Run Length at Paced Full Data Rate . . . . . . . . . . 27
7.1.2. Run Length at Full Data Windowed Rate . . . . . . . . 28
7.1.3. Background Run Length Tests . . . . . . . . . . . . . 28
7.2. Standing Queue tests . . . . . . . . . . . . . . . . . . . 28
7.2.1. Congestion Avoidance . . . . . . . . . . . . . . . . . 29
7.2.2. Bufferbloat . . . . . . . . . . . . . . . . . . . . . 30
7.2.3. Non excessive loss . . . . . . . . . . . . . . . . . . 30
7.2.4. Duplex Self Interference . . . . . . . . . . . . . . . 30
7.3. Slowstart tests . . . . . . . . . . . . . . . . . . . . . 30
7.3.1. Full Window slowstart test . . . . . . . . . . . . . . 31
7.3.2. Slowstart AQM test . . . . . . . . . . . . . . . . . . 31
7.4. Sender Rate Burst tests . . . . . . . . . . . . . . . . . 31
7.5. Combined Tests . . . . . . . . . . . . . . . . . . . . . . 32
7.5.1. Sustained burst test . . . . . . . . . . . . . . . . . 32
7.5.2. Live Streaming Media . . . . . . . . . . . . . . . . . 33
8. Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
8.1. Near serving HD streaming video . . . . . . . . . . . . . 34
8.2. Far serving SD streaming video . . . . . . . . . . . . . . 34
8.3. Bulk delivery of remote scientific data . . . . . . . . . 35
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9. Validation . . . . . . . . . . . . . . . . . . . . . . . . . . 35
10. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 37
11. Informative References . . . . . . . . . . . . . . . . . . . . 37
Appendix A. Model Derivations . . . . . . . . . . . . . . . . . . 39
A.1. Queueless Reno . . . . . . . . . . . . . . . . . . . . . . 39
A.2. CUBIC . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Appendix B. Complex Queueing . . . . . . . . . . . . . . . . . . 41
Appendix C. Version Control . . . . . . . . . . . . . . . . . . . 42
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 42
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1. Introduction
Bulk performance metrics evaluate an Internet path's ability to carry
bulk data. Model based bulk performance metrics rely on mathematical
TCP models to design a targeted diagnostic suite (TDS) of IP
performance tests which can be applied independently to each subpath
of the full end-to-end path. These targeted diagnostic suites allow
independent tests of subpaths to accurately detect if any subpath
will prevent the full end-to-end path from delivering bulk data at
the specified performance target, independent of the measurement
vantage points or other details of the test procedures used for each
measurement.
The end-to-end target performance is determined by the needs of the
user or application, outside the scope of this document. For bulk
data transport, the primary 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
entire end-to-end path over which the data traverses, these
parameters must also be specified in advance. They may reflect a
specific real path through the Internet or an idealized path
representing a typical user community. The target values for these
three parameters, Data Rate, RTT and MTU, inform the mathematical
models used to design the TDS.
Each IP diagnostic test in a TDS consists of a precomputed traffic
pattern and statistical criteria for evaluating packet delivery.
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
computed in advance based on the three target parameters of the end-
to-end path and independent of the properties of individual subpaths.
As much as possible the measurement traffic is generated
deterministically in ways that minimize the extent to which test
methodology, measurement points, measurement vantage or path
partitioning affect the details of the measurement traffic.
Mathematical models are also used to compute the bounds on the packet
delivery statistics for acceptable IP performance. Since these
statistics, such as packet loss, are typically aggregated from all
subpaths of the end-to-end path, the end-to-end statistical bounds
need to be apportioned as a separate bound for each subpath. Note
that links that are expected to be bottlenecks are expected to
contribute more packet loss and/or delay. In compensation, other
links have to be constrained to contribute less packet loss and
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delay. The criteria for passing each test of a TDS is an apportioned
share of the total bound determined by the mathematical model from
the end-to-end target performance.
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, measurement results
were not statistically significant, and others such as failing to
meet some test preconditions.
This document describes a framework for deriving traffic patterns and
delivery statistics for model based metrics. It does not fully
specify any measurement techniques. Important details such as packet
type-p selection, sampling techniques, vantage selection, etc. 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. A TDS includes the target parameters, documentation of the
models and assumptions used to derive the 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.
Section 2 defines terminology used throughout this document.
It has been difficult to develop Bulk Transport Capacity [RFC3148]
metrics due to some overlooked requirements described in Section 3
and some intrinsic problems with using protocols for measurement,
described in Section 4.
In Section 5 we describe the models and common parameters used to
derive the targeted diagnostic suite. In Section 6 we describe
common testing procedures. Each subpath is evaluated using suite of
far simpler and more predictable diagnostic tests described in
Section 7. In Section 8 we present three example TDS', one that
might be representative of HD video, when served fairly close to the
user, a second that might be representative of standard video, served
from a greater distance, and a third that might be representative of
high performance bulk data delivered over a transcontinental path.
There exists a small risk that model based metric itself might yield
a false pass result, in the sense that every subpath of an end-to-end
path passes every IP diagnostic test and yet a real application fails
to attain the performance target over the end-to-end path. If this
happens, then the validation procedure described in Section 9 needs
to be used to prove and potentially revise the models.
Future documents will define model based metrics for other traffic
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classes and application types, such as real time streaming media.
1.1. TODO
Please send comments on 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: Fri Feb 14 14:07:33 PST 2014
2. Terminology
Terminology about paths, etc. See [RFC2330] and
[I-D.morton-ippm-lmap-path].
[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
[I-D.morton-ippm-lmap-path].
test path A path between two measurement points that includes a
subpath of the end-to-end 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 4.1.
front path The subpath from the data sender to the dominant
bottleneck.
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
resources (network and/or queue capacity).
Properties determined by the end-to-end path and application. They
are described in more detail in Section 5.1.
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 excludes transport and lower level headers(TCP/IP or
other protocols) and as well as retransmissions and other data
that does not contribute to the total quantity of data delivered
to the application.
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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, retransmits 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.
end-to-end target parameters: Application or transport performance
goals for the end-to-end path. They include the target data rate,
RTT and MTU described below.
Target Data Rate: The application data rate, typically the ultimate
user's performance goal.
Target RTT (Round Trip Time): The baseline (minimum) RTT of the
longest end-to-end path over which the application expects to 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 the header_overhead) on the return path.
Target MTU (Maximum Transmission Unit): The maximum MTU supported by
the end-to-end path the over which the application expects to meet
the target performance. Assume 1500 Byte packet 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.
Effective Bottleneck Data Rate: This is the bottleneck data rate
inferred from the ACK stream, by looking at how much data the ACK
stream reports delivered per unit time. If the path is thinning
ACKs or batching packets the effective bottleneck rate can be much
higher than the average link rate. See Section 4.1 and Appendix B
for more details.
[sender | interface] rate: The burst data rate, constrained by the
data sender's interfaces. 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. They are
described in more detail in Section 5.2. Note that these are mixed
between application transport performance (excludes headers) and link
IP performance (includes headers).
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pipe size A general term for number of packets needed in flight (the
window size) to exactly fill some network path or subpath. This
is the window size which is normally the onset of queueing.
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 loss or ECN marking probability, if
there are independently and identically distributed.
target_run_length The target_run_length is an estimate of the
minimum required headway 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 show in
Section 5.2 and alternatives in Appendix A
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 5.3 in exchange for a more stringent TDS validation
procedures, described in Section 9.
subpath_data_rate The maximum IP data rate supported by a subpath.
This typically includes TCP/IP overhead, including headers,
retransmits, etc.
test_path_RTT The RTT between two measurement points using
appropriate data and ACK packet sizes.
test_path_pipe The amount of data necessary to fill a test path.
Nominally the test path RTT times the subpath_data_rate (which
should be part of the end-to-end subpath).
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 classified into groups 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 end-to-end
performance, the average packet losses or ECN must be below the
threshold computed by the model. As such, capacity tests reflect
parameters that can transition from passing to failing as a
consequence of cross traffic, additional presented load or the
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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.
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 other 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 bursty 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.
General Terminology:
Targeted Diagnostic Test (TDS) A set of IP Diagnostics designed to
determine if a subpath can sustain flows at a specific
target_data_rate over a path that has a target_RTT using
target_MTU sided packets.
Fully Specified Targeted Diagnostic Test 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.
apportioned To divide and allocate, as in budgeting packet loss
rates across multiple subpaths to accumulate below a specified
end-to-end loss rate.
open loop A control theory term used to describe a class of
techniques where systems that exhibit circular dependencies can be
analyzed by suppressing some of the dependences, such that the
resulting dependency graph is acyclic.
3. 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
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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 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 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 Metrics 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 important that
both a consumer (or their delegate) and ISP be able to perform the
same measurement and get the same result.
NB: All of the metric requirements in RFC 2330 should be reviewed and
potentially revised. If such a document is opened soon enough, this
entire section should be dropped.
4. Background
At the time the IPPM WG was chartered, sound Bulk Transport Capacity
measurement was known to be beyond our capabilities. By hindsight it
is now clear why it 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 loss probability and/or RTT)
to conform to their behavior.
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 the relative momentum of the measurement and
measured particles. For network measurement you can not in
general determine the relative "elasticity" of the measurement
traffic and cross traffic, so you can not even gauge the relative
magnitude of their effects on each other.
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These properties are a consequence of the equilibrium behavior
intrinsic to how all throughput optimizing protocols interact with
the network. The protocols rely on control systems based on multiple
network estimators to regulate the quantity of data sent into the
network. The data 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 estimators
are non-linear, 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. The traffic and
traffic patterns (bursts) are 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.
4.1. 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
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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
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 is
derated).
4.2. 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
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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 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] apply.
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 different subpaths, such that the spatial composition of the
metrics yields end-to-end statics 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 the following conditions are met:
o The raw link rate is higher than the target data rate.
o The observed run length is larger than required by a suitable TCP
performance model
o 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.
o 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 other mechanisms.
o When there is a standing queue at a bottleneck for a shared media
subpath, there are suitable bounds on how the data and ACKs
interact, for example due to the channel arbitration mechanism.
o 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. This typically requires some form
of Automatic Queue Management [RFC2309].
We are developing a tool that can perform many of the tests described
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here[MBMSource].
5. Common Models and Parameters
5.1. 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 2. These parameters are
determined by the needs of the application or the ultimate end user
and the end-to-end 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 2 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 fluctuations in the actual
data rate, needed to meet the specified average rate. Specifying a
target rate with insufficient headroom are 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 6.1.4 applies.
5.2. Common Model Calculations
The end-to-end target parameters are 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 = target_rate * target_RTT / ( target_MTU -
header_overhead )
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Target_run_length is an estimate of the minimum required headway
between losses or ECN marks, 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 loss rates),
but may not apply in all situations. In any case alternate models
should be compared 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 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 less
conservative model is used, a fully specified TDS or FSTDS MUST
document the actual method for computing target_run_length along with
the rationale for the underlying assumptions and the ratio of chosen
target_run_length to the reference target_run_length calculated
above.
These two parameters, target_pipe_size and target_run_length,
directly imply most of the individual parameters for the tests in
Section 7.
5.3. Parameter Derating
Since some aspects of the models are very conservative, this
framework permits some latitude in derating test parameters. Rather
than trying to formalize more complicated models we permit some test
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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
compute the derated metric parameters.
o The validation procedures described in Section 9 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.
6. Common testing procedures
6.1. Traffic generating techniques
6.1.1. 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.
Paced single packets: Send individual packets at the specified rate
or headway.
Burst: 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). These bursts are typically
sent as back-to-back packets at the testers interface rate.
Slowstart bursts: Send 4 packet sender interface rate 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 bursts become sender
interface rate bursts.
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
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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 may be desirable to introduce noise sources into the above
pacing models, but the are not warranted at this time.
6.1.2. Constant window pseudo CBR
Implement pseudo constant bit rate by running a standard protocol
such as TCP with a fixed bound on the window size. The rate is only
maintained in average over each RTT, and is subject to limitations of
the transport protocol.
The bound on the window size is computed from the target_data_rate
and the actual RTT of the test path.
If the transport protocol fails to maintain the test rate within
prescribed limits the test would typically be considered inconclusive
or failing, depending depending on what mechanism caused the reduced
rate. See the discussion of test outcomes in Section 6.2.1.
6.1.3. Scanned window pseudo CBR
Same as the 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 for every 2*target_pipe_size delivered
packets. This mimics the additive increase phase of standard
congestion avoidance and normally separates the the window increases
by approximately twice the target_RTT.
There are two versions of this test: one built by applying a window
clamp to standard congestion control and one one 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. It is only
appropriate for engineering testing under laboratory conditions. The
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Windowed Ping tools implemented such a test [WPING]. This tool has
been updated and is under test.[mpingSource]
The test procedures in Section 7.2 describe how to the partition the
scans into regions and how to interpret the results.
6.1.4. Concurrent or channelized testing
The procedures described in his 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 standard [RFC5681] and de facto standard congestion control
algorithms [CUBIC] have scaling limits, in the sense that as a long
fast network (LFN) with a fixed RTT and MTU gets faster, all
congestion control algorithms get less accurate and as a consequence
have difficulty filling the network [SLowScaling]. These properties
are a consequence of the original Reno AIMD congestion control design
and the requirement in RFC 5681 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 Mb/s, which can be attained
with run lengths under 10000 packets. Since run length goes as the
square of the data rate, at higher rates the run lengths can be
unfeasibly large, and multiple connection might be the only feasible
approach. For an example of this problem see Section 8.3.
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 using bursty traffic, the bursts should be
synchronized across flows.
6.1.5. Intermittent Testing
Any test which does not depend on queueing (e.g. the CBR tests) or
experiences periodic zero outstanding data during normal operation
(e.g. between bursts for the various burst tests), can be formulated
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as an intermittent test, to reduce the perceived impact on other
traffic. The approach is to insert periodic pauses in the test at
any point when there is no expected queue occupancy.
Intermittent testing can be used for ongoing monitoring for changes
in subpath quality with minimal disruption users. However it is not
suitable in environments where there are reactive links[REACTIVE].
6.1.6. Intermittent Scatter Testing
Intermittent scatter testing is a technique for non-disruptively
evaluating the front path from a sender to a subscriber aggregation
point within an ISP at full load by intermittently testing across a
pool of subscriber access links, such that each subscriber sees
tolerable test traffic loads. The load on the front path should be
limited to be no more than that which would be caused by a single
test to an known to otherwise be idle subscriber. This test in
aggregate mimics a full load test from a content provider to the
aggregation point.
Intermittent scatter testing can be used to reduce the measurement
noise introduced by unknown traffic on customer access links.
6.2. Interpreting the Results
6.2.1. Test outcomes
To perform an exhaustive test of an end-to-end network path, each
test of the TDS is applied to each subpath of an end-to-end path. If
any subpath fails any test then an application running over the end-
to-end 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 inclusive outcomes is critical to the accuracy and
robustness of Model Based Metrics. Tests can be inconclusive if the
precomputed traffic pattern was 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 6.1.2) 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 the run length specification while failing to
attain the specified data rate it must be treated as an inconclusive
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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 run length measurement
itself.
Note that for load tests such as this example, an observed run length
that is too small can be considered to have failed the test 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 traffic 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 was
presumably caused by some uncontrolled feedback from the network.
Note that procedures that attempt to sweep the target parameter space
to find the bounds on some parameter (for example to find the highest
data rate for a subpath) are likely to break the location independent
properties of Model Based Metrics, because the boundary between
passing and inconclusive is sensitive to the RTT because TCP's
ability to compensate for problems scales with the number of round
trips per second. Repeating the same procedure from another vantage
point with a different RTT is likely get a different result, because
TCP will get lower performance on the path with the longer 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 inclusive 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.
This can help to drive tool evolution. Under some conditions it
might be possible to reevaluate the raw data for satisfying alternate
performance targets. However such procedures are likely to introduce
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sampling bias and other implicit feedback which can cause false
results and exhibit MP vantage sensitivity.
6.2.2. Statistical criteria for measuring 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 probabilities 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
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support either hypothesis the current status of measurements is
inconclusive.
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 + sn
Rejection line: Xr = h2 + sn
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 error.
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 + sn
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and n = h1 / s
6.2.2.1. Alternate criteria for measuring run_length
An alternate calculation, contributed by Alex Gilgur (Google).
The probability of failure within an interval whose length is
target_run_length is given by an exponential distribution with rate =
1 / target_run_length (a memoryless process). The implication of
this is that it will be different, depending on the total count of
packets that have been through the pipe, the formula being:
P(t1 < T < t2) = R(t1) - R(t2),
where
T = number of packets at which a failure will occur with probability P;
t = number of packets:
t1 = number of packets (e.g., when failure last occurred)
t2 = t1 + target_run_length
R = failure rate:
R(t1) = exp (-t1/target_run_length)
R(t2) = exp (-t2/target_run_length)
The algorithm:
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initialize the packet.counter = 0
initialize the failed.packet.counter = 0
start the loop
if paket_response = ACK:
increment the packet.counter
else:
### The packet failed
increment the packet.counter
increment the failed.packet.counter
P_fail_observed = failed.packet.counter/packet.counter
upper_bound = packet.counter + target.run.length / 2
lower_bound = packet.counter - target.run.length / 2
R1 = exp( -upper_bound / target.run.length)
R0 = R(max(0, lower_bound)/ target.run.length)
P_fail_predicted = R1-R0
Compare P_fail_observed vs. P_fail_predicted
end-if
continue the loop
This algorithm allows accurate comparison of the observed failure
probability with the corresponding values predicted based on a fixed
target_failure_rate, which is equal to 1.0 / target_run_length.
6.2.3. Reordering Tolerance
All tests must be instrumented for packet level reordering [RFC4737].
However, there is no consensus for how much reordering should be
acceptable. Over the last two decades the general trend has been to
make protocols and applications more tolerant to reordering, in
response to the gradual increase in reordering in the network. This
increase has been due to the gradual deployment of parallelism in the
network, as a consequence of such technologies as multithreaded route
lookups and Equal Cost Multipath (ECMP) routing. These techniques to
increase network parallelism are critical to enabling overall
Internet growth to exceed Moore's Law.
Section 5 of [RFC4737] proposed a metric that may be sufficient to
designate isolated reordered packets as effectively lost, because
TCP's retransmission response would be the same.
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TCP should be able to adapt to reordering as long as the reordering
extent is no more than the maximum of one half window or 1 mS,
whichever is larger. Note that there is a fundamental tradeoff
between tolerance to reordering and how quickly algorithms such as
fast retransmit can repair losses. Within this limit on reorder
extent, there should be no bound on reordering density.
NB: Traditional TCP implementations were not compatible with this
metric, however newer implementations still need to be evaluated
Parameters:
Reordering displacement: the maximum of one half of target_pipe_size
or 1 mS.
6.3. Test Qualifications
This entire section need to be completely overhauled. @@@@ It might
be summarized as "needs to be specified in a FSTDS".
Send pre-load traffic as needed to activate radios with a sleep mode,
or other "reactive network" elements (term defined in
[draft-morton-ippm-2330-update-01]).
In general failing to accurately generate the test traffic has to be
treated as an inconclusive test, since it must be presumed that the
error in traffic generation might have affected the test outcome. To
the extent that the network itself had an effect on the the traffic
generation (e.g. in the standing queue tests) the possibility exists
that allowing too large of error margin in the traffic generation
might introduce feedback loops that comprise the vantage independents
properties of these tests.
The proper treatment of cross traffic is different for different
subpaths. In general when testing infrastructure which is associated
with only one subscriber, the test should be treated as inconclusive
it that subscriber is active on the network. However, for shared
infrastructure managed by an ISP, the question at hand is likely to
be testing if ISP has sufficient total capacity. In such cases the
presence of cross traffic due to other subscribers is explicitly part
of the network conditions and its effects are explicitly part of the
test.
These two cases do not cover all subpaths. For example, WiFI which
itself shares unmanaged channel space with other devices is unlikely
to be unsuitable for any prescriptive measurement.
Note that canceling tests due to load on subscriber lines may
introduce sampling bias for testing other parts of the
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infrastructure. For this reason tests that are scheduled but not run
due to load should be treated as a special case of "inconclusive".
7. Diagnostic Tests
The diagnostic tests below are organized by traffic pattern: basic
data rate and run length, standing queues, slowstart bursts, and
sender rate bursts. We also introduce some combined tests which are
more efficient the expense of conflating the signatures of different
failures.
7.1. Basic Data Rate and Run Length Tests
We propose several versions of the basic data rate and run length
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 auditing process. The third, background run length, 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 6.2.2 to score the outcome:
Pass: it is statistically significant that the observed run length is
larger than the target_run_length.
Fail: it is statistically significant that the observed run length 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 6.3 or neither run length statistical hypothesis was
confirmed in the allotted test duration.
7.1.1. Run Length 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 6.1.1 with a
burst size of 1 (single packets).
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The test is considered to be inconclusive if the packet transmission
can not be accurately controlled for any reason.
7.1.2. Run Length at Full Data Windowed Rate
Confirm that the observed run length is at least the
target_run_length while sending at an average rate 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.
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. 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.
7.1.3. Background Run Length 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.
Existing loss metrics such as [RFC6673] might be appropriate for
measuring background run length.
7.2. Standing Queue tests
These test 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
[Bufferbloat] and inflict excess queuing delays on all flows sharing
the bottleneck queue. Excess losses make loss recovery problematic
for the transport protocol. Non-linear or erratic RTT fluctuations
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suggest poor interactions between the channel acquisition systems and
the transport self clock. All of the tests in this section use the
same basic scanning algorithm 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 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 6.1.3 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 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 the window size. The
precise transition from one region to the other can be identified by
the maximum network power, defined to be the ratio data rate over the
RTT[POWER].
For technologies that do not have conventional queues, start the scan
at a window equal to the test_window, 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 packet loss may require multiple scans.
Above the onset of loss, all transport protocols are expected to
experience periodic losses. For the stiffened transport case they
will be determined by the AQM algorithm in the network or the details
of how the the window increase function responds to loss. For the
standard transport case the details of periodic losses are typically
dominated by the behavior of the transport protocol itself.
7.2.1. Congestion Avoidance
A link passes the congestion avoidance standing queue test if more
than target_run_length packets are delivered between the power point
(or test_window) and the first loss or ECN mark. If this test is
implemented using a standards congestion control algorithm with a
clamp, it can be used in situ in the production internet as a
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capacity test. For an example of such a test see [NPAD].
7.2.2. 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 occupancy then a single stream
with sufficient data to deliver is likely to cause the problems
described in [RFC2309] and [Bufferbloat]. 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 is before a standing queue has introduced
more delay than than twice target_RTT, or other well defined limit.
Note that there is not yet a model for how much standing queue is
acceptable. The factor of two chosen here reflects a rule of thumb.
Note that 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.
7.2.3. Non excessive loss
This test confirm that the onset of loss is not excessive. Pass if
losses are bound by the the fluctuations in the cross traffic, such
that transient load (bursts) do not cause dips in aggregate raw
throughput. e.g. pass as long as the losses are no more bursty than
are expected from a simple drop tail queue. Although this test could
be made more precise it is really included here for pedantic
completeness.
7.2.4. Duplex Self Interference
This engineering test confirms a bound on the interactions between
the forward data path and the ACK return path. Fail 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.
7.3. Slowstart tests
These tests mimic slowstart: data is sent at twice the effective
bottleneck rate to exercise the queue at the dominant bottleneck.
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
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equal to the target_data_rate.
7.3.1. 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 6.2.2
to score the outcome. Pass if it is statistically significant that
the observed run length is larger than the target_run_length. Fail
if it is statistically significant that the observed run length 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.
7.3.2. 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).
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.
7.4. 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 a subscriber ISP.
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
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application pause is longer than an RTO. Since this is standard
behavior, it is desirable that the network be able to deliver such
bursts, otherwise application pauses will cause unwarranted losses.
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 explicitly
encourage detrateing. A TDS should 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.
7.5. Combined Tests
These tests are more efficient from a deployment/operational
perspective, but may not be possible to diagnose if they fail.
7.5.1. Sustained burst test
Send target_pipe_size*derate sender interface rate bursts every
target_RTT*derate, for derate between 0 and 1. Verify that the
observed run length meets target_run_length. Key observations:
o This test is subpath RTT invariant, as long as the tester can
generate the required pattern.
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 suggests a problem with the procedure and an
inconclusive test result.
o This test is more strenuous than the slowstart tests: they are not
needed if the link passes this test with derate=1.
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
smaller than the target_RTT. Offsetting this performance
underestimation is part of the rationale behind permitting
derating in general.
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o This test can be implemented with standard instrumented
TCP[RFC4898], using a specialized measurement application at one
end and a minimal service at the other end [RFC 863, RFC 864]. It
may require tweaks to the TCP implementation. [MBMSource]
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 is not totally sufficient: the standing window
engineering tests are also needed to be sure that the link is well
behaved at and beyond the onset of congestion.
o This one test can be proven to be the one capacity test to
supplant them all.
7.5.2. Live Streaming Media
Model Based Metrics can be 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, for a specific TDS.
If the 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:
serving_window_clamp=target_data_rate*serving_RTT/
(target_MTU-header_overhead)
The serving_window_clamp will limit the both the serving data rate
and burst sizes to be no larger than the procedures in Section 7.1.2
and Section 7.4 or Section 7.5.1. 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 7.4
and the sender rate burst sizes are implicitly derated by the
serving_window_clamp divided by the target_pipe_size at the very
least. (The traffic might be smoother than specified by the sender
interface rate bursts test.)
Note that if the application tolerates fluctuations in its actual
data rate (say by use of a playout buffer) 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.
Alternatively the sender data rate and bursts might be explicitly
controlled by a host shaper or pacing at the sender. This would
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provide better control and work for serving_RTTs that are larger than
the target_RTT, but it is substantially more complicated to
implement. With this technique, any traffic might be used for
measurement.
* Note that this technique might be applied to any content, if users
are willing to tolerate reduced data rate to inhibit TCP equilibrium
behavior.
8. Examples
In this section we present TDS for a couple of performance
specifications.
Tentatively: 5 Mb/s*50 ms, 1 Mb/s*50ms, 250kbp*100mS
8.1. Near serving HD streaming video
Today the best quality HD video requires slightly less than 5 Mb/s
[HDvideo]. Since it is desirable to serve such content locally, we
assume that the content will be within 50 mS, which is enough to
cover continental Europe or either US coast from a single site.
5 Mb/s over a 50 ms path
+----------------------+-------+---------+
| End to End Parameter | Value | units |
+----------------------+-------+---------+
| target_rate | 5 | Mb/s |
| target_RTT | 50 | ms |
| traget_MTU | 1500 | bytes |
| target_pipe_size | 22 | packets |
| target_run_length | 1452 | packets |
+----------------------+-------+---------+
Table 1
This example uses the most conservative TCP model and no derating.
8.2. Far serving SD streaming video
Standard Quality video typically fits in 1 Mb/s [SDvideo]. This can
be reasonably delivered via longer paths with larger. We assume
100mS.
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1 Mb/s over a 100 ms path
+----------------------+-------+---------+
| End to End Parameter | Value | units |
+----------------------+-------+---------+
| target_rate | 1 | Mb/s |
| target_RTT | 100 | ms |
| traget_MTU | 1500 | bytes |
| target_pipe_size | 9 | packets |
| target_run_length | 243 | packets |
+----------------------+-------+---------+
Table 2
This example uses the most conservative TCP model and no derating.
8.3. Bulk delivery of remote scientific data
This example corresponds to 100 Mb/s bulk scientific data over a
moderately long RTT. Note that the target_run_length is infeasible
for most networks.
100 Mb/s over a 200 ms path
+----------------------+---------+---------+
| End to End Parameter | Value | units |
+----------------------+---------+---------+
| target_rate | 100 | Mb/s |
| target_RTT | 200 | ms |
| traget_MTU | 1500 | bytes |
| target_pipe_size | 1741 | packets |
| target_run_length | 9093243 | packets |
+----------------------+---------+---------+
Table 3
9. Validation
Since some aspects of the models are likely to be too conservative,
Section 5.2 and Section 5.3 permit alternate protocol models and test
parameter derating. In exchange for this latitude in the modelling
process, 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.
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We suggest two strategies, 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 these decisions, test them and
comment on there 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 only pass by small (infinitesimal)
margins, and demonstrate that a variety of authentic applications
running over real TCP implementations (or other protocol as
appropriate) meets the end-to-end target parameters 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 using our example in our HD streaming video TDS described
in Section 8.1, the bottleneck data rate should be 5 Mb/s, the per
packet random background loss probability should be 1/1453, for a run
length of 1452 packets, the bottleneck queue should be 22 packets and
the front path should have just enough buffering to withstand 22
packet line 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 23 packet slowstart 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 ral application using a stock TCP implementation in the vendor's
default configuration to attain 5 Mb/s over an 50 mS path.
The most difficult part of setting up such a testbed is arranging to
infinitesimally pass the individual tests. We suggest two
approaches: constraining the network devices not to use all available
resources (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 public with sufficient
detail for the experiments to be replicated by other researchers.
All components should either be open source of fully described
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proprietary implementations that are available to the research
community.
This work here is inspired by open tools running on an open platform,
using open techniques to collect open data. See Measurement Lab
[http://www.measurementlab.net/]
10. Acknowledgements
Ganga Maguluri suggested the statistical test for measuring loss
probability in the target run length. Alex Gilgur for helping with
the statistics and contributing and alternate model.
Meredith Whittaker for improving the clarity of the communications.
11. Informative References
[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,
July 2001.
[RFC3465] Allman, M., "TCP Congestion Control with Appropriate Byte
Counting (ABC)", RFC 3465, February 2003.
[RFC4898] Mathis, M., Heffner, J., and R. Raghunarayan, "TCP
Extended Statistics MIB", RFC 4898, May 2007.
[RFC4737] Morton, A., Ciavattone, L., Ramachandran, G., Shalunov,
S., and J. Perser, "Packet Reordering Metrics", RFC 4737,
November 2006.
[RFC5681] Allman, M., Paxson, V., and E. Blanton, "TCP Congestion
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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.
[I-D.morton-ippm-lmap-path]
Bagnulo, M., Burbridge, T., Crawford, S., Eardley, P., and
A. Morton, "A Reference Path and Measurement Points for
LMAP", draft-morton-ippm-lmap-path-00 (work in progress),
January 2013.
[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
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., "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.
[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-
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Validation via Sequential Testing", version 0.1, 11 2012.
[LMCUBIC] Ledesma Goyzueta, R. and Y. Chen, "A Deterministic Loss
Model Based Analysis of CUBIC, IEEE International
Conference on Computing, Networking and Communications
(ICNC), E-ISBN : 978-1-4673-5286-4", January 2013.
Appendix A. Model Derivations
The reference target_run_length described in Section 5.2 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 the 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 probability 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 5.2
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 5.2 it is assumed that the target rate is the same as the
link rate, and any excess window causes a standing queue at the
bottleneck. This might be representative of a non-shared access
link. An alternative situation would be a heavily aggregated subpath
where individual flows do not significantly contribute to the
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queueing delay, and losses are determined monitoring the average data
rate, for example by the use of a virtual queue as in [AFD]. In such
a scheme the RTT is constant and TCP's AIMD congestion control causes
the data rate to fluctuate in a sawtooth. If the traffic is being
controlled in a manner that is consistent with the metrics here, goal
would be to make the actual average rate equal to the
target_data_rate.
We can derive a model for Reno TCP and delayed ACK under the above
set of assumptions: for some value of Wmin, the window will sweep
from Wmin to 2*Wmin in 2*Wmin RTT. 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. This makes sense
because under the assumptions in Section 5.2 the AMID sawtooth caused
a queue at the bottleneck, which raised the effective RTT by 50%.
A.2. CUBIC
CUBIC has three operating regions. The model for the expected value
of window size derived in [LMCUBIC] assumes operation in the
"concave" region only, which is a non-TCP friendly region for long-
lived flows. The authors make the following assumptions: packet loss
probability, p, is independent and periodic, losses occur one at a
time, and they are true losses due to tail drop or corruption. This
definition of p aligns very well with our definition of
target_run_length and the requirement for progressive loss (AQM).
Although CUBIC window increase depends on continuous time, the
authors transform the time to reach the maximum Window size in terms
of RTT and a parameter for the multiplicative rate decrease on
observing loss, beta (whose default value is 0.2 in CUBIC). The
expected value of Window size, E[W], is also dependent on C, a
parameter of CUBIC that determines its window-growth aggressiveness
(values from 0.01 to 4).
E[W] = ( C*(RTT/p)^3 * ((4-beta)/beta) )^-4
and, further assuming Poisson arrival, the mean throughput, x, is
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x = E[W]/RTT
We note that under these conditions (deterministic single losses),
the value of E[W] is always greater than 0.8 of the maximum window
size ~= reference_run_length. (as far as I can tell)
Appendix B. Complex Queueing
For many network technologies simple queueing models do not apply:
the network schedules, thins or otherwise alters the timing of ACKs
and data, generally to raise the efficiency of the channel allocation
process 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 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 pending traffic. 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, followed by the entire window of ACKs on
the return path.
If a particular end-to-end path contains a link or device that alters
the 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. It is
important to note that due to the self clock, ill conceived channel
allocation mechanisms can increase the stress on upstream links in a
long path.
Holding data or ACKs for channel allocation or other reasons (such as
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 5.2 by an effective_RTT,
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which includes the target_RTT reflecting the fixed part of the path
plus a term to account for the extra delays introduced by these
mechanisms.
Appendix C. Version Control
Formatted: Fri Feb 14 14:07:33 PST 2014
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 August 18, 2014 [Page 42]