Model Based Bulk Performance Metrics
draft-ietf-ippm-model-based-metrics-01
The information below is for an old version of the document.
| Document | Type | Active Internet-Draft (ippm WG) | |
|---|---|---|---|
| Authors | Matt Mathis , Al Morton | ||
| Last updated | 2013-11-06 (Latest revision 2013-10-21) | ||
| Replaces | draft-mathis-ippm-model-based-metrics | ||
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draft-ietf-ippm-model-based-metrics-01
IP Performance Working Group M. Mathis
Internet-Draft Google, Inc
Intended status: Experimental A. Morton
Expires: April 24, 2014 AT&T Labs
October 21, 2013
Model Based Bulk Performance Metrics
draft-ietf-ippm-model-based-metrics-01.txt
Abstract
We introduce a new class of model based metrics designed to determine
if a long network path can meet predefined end-to-end application
performance targets by applying a suite of IP diagnostic tests to
successive subpaths. The subpath at a time tests are designed to
exclude all known conditions which might prevent the full end-to-end
path from meeting the user's target application performance.
This approach makes it possible to to determine the IP performance
requirements needed to support the desired end-to-end TCP
performance. The IP metrics are based on traffic patterns that mimic
TCP or other transport protocol but are precomputed independently of
the actual behavior of the transport protocol over the subpath under
test. This makes the measurements open loop, eliminating nearly all
of the difficulties encountered by traditional bulk transport
metrics, which fundamentally depend on congestion control equilibrium
behavior.
A natural consequence of this methodology is verifiable network
measurement: measurements from any given vantage point can be
verified by repeating them from other vantage points.
Formatted: Mon Oct 21 15:42:35 PDT 2013
Status of this Memo
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material or to cite them other than as "work in progress."
This Internet-Draft will expire on April 24, 2014.
Copyright Notice
Copyright (c) 2013 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
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the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.1. TODO . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 6
3. New requirements relative to RFC 2330 . . . . . . . . . . . . 9
4. Background . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.1. TCP properties . . . . . . . . . . . . . . . . . . . . . . 12
5. Common Models and Parameters . . . . . . . . . . . . . . . . . 14
5.1. Target End-to-end parameters . . . . . . . . . . . . . . . 14
5.2. Common Model Calculations . . . . . . . . . . . . . . . . 15
5.3. Parameter Derating . . . . . . . . . . . . . . . . . . . . 16
6. Common testing procedures . . . . . . . . . . . . . . . . . . 16
6.1. Traffic generating techniques . . . . . . . . . . . . . . 16
6.1.1. Paced transmission . . . . . . . . . . . . . . . . . . 16
6.1.2. Constant window pseudo CBR . . . . . . . . . . . . . . 17
6.1.3. Scanned window pseudo CBR . . . . . . . . . . . . . . 18
6.1.4. Concurrent or channelized testing . . . . . . . . . . 18
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 . . . . 21
6.2.3. Reordering Tolerance . . . . . . . . . . . . . . . . . 23
6.3. Test Qualifications . . . . . . . . . . . . . . . . . . . 23
6.3.1. Verify the Traffic Generation Accuracy . . . . . . . . 23
6.3.2. Verify the absence of cross traffic . . . . . . . . . 24
6.3.3. Additional test preconditions . . . . . . . . . . . . 25
7. Diagnostic Tests . . . . . . . . . . . . . . . . . . . . . . . 25
7.1. Basic Data Rate and Run Length Tests . . . . . . . . . . . 25
7.1.1. Run Length at Paced Full Data Rate . . . . . . . . . . 26
7.1.2. run length at Full Data Windowed Rate . . . . . . . . 26
7.1.3. Background Run Length Tests . . . . . . . . . . . . . 26
7.2. Standing Queue tests . . . . . . . . . . . . . . . . . . . 26
7.2.1. Congestion Avoidance . . . . . . . . . . . . . . . . . 28
7.2.2. Bufferbloat . . . . . . . . . . . . . . . . . . . . . 28
7.2.3. Non excessive loss . . . . . . . . . . . . . . . . . . 28
7.2.4. Duplex Self Interference . . . . . . . . . . . . . . . 28
7.3. Slowstart tests . . . . . . . . . . . . . . . . . . . . . 29
7.3.1. Full Window slowstart test . . . . . . . . . . . . . . 29
7.3.2. Slowstart AQM test . . . . . . . . . . . . . . . . . . 29
7.4. Sender Rate Burst tests . . . . . . . . . . . . . . . . . 29
7.5. Combined Tests . . . . . . . . . . . . . . . . . . . . . . 30
7.5.1. Sustained burst test . . . . . . . . . . . . . . . . . 30
7.5.2. Live Streaming Media . . . . . . . . . . . . . . . . . 31
8. Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
8.1. Near serving HD streaming video . . . . . . . . . . . . . 32
8.2. Far serving SD streaming video . . . . . . . . . . . . . . 32
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8.3. Bulk delivery of remote scientific data . . . . . . . . . 33
9. Validation . . . . . . . . . . . . . . . . . . . . . . . . . . 33
10. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 34
11. Informative References . . . . . . . . . . . . . . . . . . . . 35
Appendix A. Model Derivations . . . . . . . . . . . . . . . . . . 36
A.1. Aggregate Reno . . . . . . . . . . . . . . . . . . . . . . 37
A.2. CUBIC . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Appendix B. Version Control . . . . . . . . . . . . . . . . . . . 38
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 38
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1. Introduction
Model based bulk performance metrics evaluate an Internet path's
ability to carry bulk data. TCP models are used 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. A
targeted diagnostic suite is constructed such that independent tests
of the subpaths will accurately predict if the full end-to-end path
can deliver bulk data at the specified performance target,
independent of the measurement vantage points or other details of the
test procedures used to measure each subpath.
Each test in the TDS consists of a precomputed traffic pattern and
statistical criteria for evaluating packet delivery.
TCP models are used to design traffic patterns that mimic TCP or
other bulk transport protocol operating at the target performance 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 properties of the full end-to-end path and
independent of the properties of individual subpaths. As much as
possible the traffic is generated deterministically in ways that
minimizes the extent to which test methodology, measurement points,
measurement vantage or path partitioning effect the details of the
traffic.
Models are also used to compute the bounds on the packet delivery
statistics for acceptable the IP performance. The criteria for
passing each test are determined from the end-to-end target
performance and are independent of the subpath under test. In
addition to passing or failing, a test can be inconclusive if the
precomputed traffic pattern was not authentically generated, test
preconditions were not met or the measurement results were not
statistically significant.
TCP's ability to compensate for less than ideal network conditions is
fundamentally affected by the RTT and MTU of the end-to-end Internet
path that it traverses. The end-to-end path determines fixed bounds
on these parameters. The target values for these three parameters,
Data Rate, RTT and MTU, are determined by the application, its
intended use and the physical infrastructure over which it is
intended to traverse. These parameters are used to inform the models
used to design the TDS.
This document describes a framework for deriving the traffic 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 out
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of scope for this document. We imagine Fully Specified Targeted
Diagnostic Suites (FSTDS), that fully defines 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 specification for the
traffic and delivery statistics for the diagnostic tests themselves,
documentation of the models and any assumptions or derating used to
derive the test parameters and a description of the test setup used
to calibrate the models, as described in later sections.
Section 2 defines terminology used throughout this document.
It has been difficult to develop BTC 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 an
network designed to support high performance bulk download.
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 falls
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 document will define model based metrics for other traffic
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: Mon Oct 21 15:42:35 PDT 2013
2. Terminology
Terminology about paths, etc. See [RFC2330] and
[I-D.morton-ippm-lmap-path].
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[data] sender Host sending data and receiving ACKs, typically via
TCP.
[data] receiver Host receiving data and sending ACKs, typically via
TCP.
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, plus possibly
additional infrastructure between the measurement points and the
subpath.
[Dominant] Bottleneck The Bottleneck that determines a flow's self
clock. It generally determines the traffic statistics for the
entire path. 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 TCP/IP (or other protocol) headers and
retransmits.
Link Data Rate General term for the data rate as seen by the link or
lower layers. It 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 or ultimate user's performance
goal. When converted to link data rate, it must be slightly
smaller than the actual link data rate, otherwise there is no
margin for compensating for RTT or other path properties. These
test will be excessively brittle if the target data rate does not
include any built in headroom.
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Target RTT (Round Trip Time): The baseline (minimum) RTT of the
longest end-to-end path the over which the application expects to
meet the target performance. This must be specified considering
authentic packets sizes: MTU sized packets on the forward path,
header_overhead sized packets on the return (ACK) 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 Bytes per 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
that might be inferred from the ACK stream, by looking at how much
data the ACK stream reports was delivered per unit time. See
Section 4.1 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.
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 in 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 target data rate, RTT and MTU.
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.
target_run_length Required run length computed from the target data
rate, RTT and MTU.
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.
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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 (using appropriate packet sizes) between two
measurement points.
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.
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, they reflect parameters
that can transition from passing to failing as a consequence of
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 design to capture the most important aspects of
a capacity test, but without causing unreasonable ongoing load
themselves. As such they may miss some details of the network
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 results 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.
3. New requirements relative to RFC 2330
[Move this entire section to a future paper]
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Model Based Metrics are designed to fulfill some additional
requirement that were not recognized at the time RFC 2330 [RFC2330]
was written. These missing requirements may have significantly
contributed to policy difficulties in the IP measurement space. Some
additional requirements are:
o 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 (e.g., measurement points as
described in [I-D.morton-ippm-lmap-path]), including off path
measurement points. The only requirements on MP selection should
be that the portion of the path that is not under test is
effectively ideal (or is non ideal in calibratable ways) and the
RTT between MPs is below some reasonable bound.
o Metrics must be repeatable by multiple parties. 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
[Move to a future paper, abridge here, ]
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, 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
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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 gage the relative
magnitude of their effects on each other.
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 (losses, 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 network 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.
Models are used to determine the actual test parameters (burst size,
loss rate, etc) from the target parameters. The basic method is to
use models to estimate specific network properties required to
sustain a given transport flow (or set of flows), and using a suite
of metrics to confirm that the network meets the required properties.
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 raw packet 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.
The tests to verify these condition are described in Section 7.
A singleton [RFC2330] measurement is a pass/fail evaluation of a
given path or subpath at a given performance. Note that measurements
to confirm that a link passes at one particular performance might not
be be useful to predict if the link will pass at a different
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performance.
A TDS does have several valuable properties, such as natural ways to
define several different composition metrics [RFC5835].
[Add text on algebra on metrics (A-Frame from [RFC2330]) and
tomography.] The Spatial Composition of fundamental IPPM metrics has
been studied and standardized. For example, the algebra to combine
empirical assessments of loss ratio to estimate complete path
performance is described in section 5.1.5. of [RFC6049]. We intend
to use this and other composition metrics as necessary.
We are developing a tool that can perform many of the tests described
here[MBMSource].
4.1. TCP properties
[Move this entire section to a future paper]
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 most frequently responds by sending exactly the same quantity
of data back into the network. The 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 widow 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 (the window is exactly twice the pipe
size) so when the window is halved, the new window will be exactly
the pipe size.
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Another source of bursts are application pauses. If the application
pauses (stops reading or writing data) for some fraction of one RTT,
state-of-the-art TCP to "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.
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. Furthermore, although the
interface rate bursts caused by the application are likely to be
smaller than last burst of a slowstart, they are at a higher rate so
they can exercise queues at arbitrary points along the "front path"
from the data sender up to and including the queue at the bottleneck.
For many network technologies a simple queueing model does 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
or broadcast media.
Altering the ACK stream generally has two consequences: raising the
effective bottleneck data rate making slowstart burst at higher rates
(possibly as high as the sender's interface rate) and effectively
raising the RTT by the time that the ACKs were postponed. 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
class of behaviors is a half duplex channel that is never released
until the current end point has no pending traffic. Such
environments cause self clocked protocols 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.
To verify that a path can meet the performance target, it is
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necessary to independently confirm that the entire path can tolerate
bursts in the dimensions that are likely to be induced by the
application and any data or ACK scheduling anywhere in the path. Two
common cases are the most important: slowstart bursts at twice the
effective bottleneck data rate; and somewhat smaller sender interface
rate bursts.
The slowstart rate bursts must be at least as least as large
target_pipe_size packets and should be twice as large (so the peak
queue occupancy at the dominant bottleneck would be approximately
target_pipe_size).
There is no general model for how well the network needs to tolerate
sender interface rate bursts. All existing TCP implementations send
full sized full rate bursts under some typically uncommon conditions,
such as application pauses that approximately match the RTT, or when
ACKs are lost or thinned. Strawman: partial window bursts (some
fraction of target_pipe_size) should be tolerated without
significantly raising the loss probability. Full target_pipe_size
bursts may slightly increase the loss probability. Interface rate
bursts as large as twice target_pipe_size should not cause
deterministic packet drops.
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 the upper layer: payload bytes delivered to the application,
above TCP. They exclude overheads associated with TCP and IP
headers, retransmitts and other protocols (e.g. DNS). In addition,
other end-to-end parameters include the effective bottleneck data
rate, the sender interface data rate and the TCP/IP header sizes
(overhead).
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 [unlike earlier drafts]. If a subpath requires multiple
connections in order to meet the specified performance, that must be
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stated explicitly and the procedure described in Section 6.1.4
applies.
5.2. Common Model Calculations
The most important derived parameter is target_pipe_size (in
packets), which is the window size --- the number of packets needed
exactly meet the target rate, with no cross traffic for the specified
target RTT and MTU. It is given by:
target_pipe_size = target_rate * target_RTT / ( target_MTU -
header_overhead )
If the transport protocol (e.g. TCP) average window size is smaller
than this, it will not meet the target rate.
The reference target_run_length, is a very conservative model for the
minimum required spacing between losses or ECN marks. The reference
target_run_length can derived as follows: assume the
subpath_data_rate is infinitesimally larger than the target_data_rate
plus the required header overheads. Then target_pipe_size also
predicts the onset of queueing. If the transport protocol (e.g.
TCP) has a window size that is larger than the target_pipe_size, the
excess packets will raise the RTT, typically by forming a standing
queue at the bottleneck.
Assume the transport protocol is using standard Reno style Additive
Increase, Multiplicative Decrease congestion control [RFC5681] and
the receiver is using standard delayed ACKs. With delayed ACKs there
must be 2*target_pipe_size roundtrips between losses. Otherwise the
multiplicative window reduction triggered by a loss would cause the
network to be underfilled. We derive the number of packets between
losses from the area under the AIMD sawtooth following [MSMO97].
They must be no more frequent than every 1 in
(3/2)*target_pipe_size*(2*target_pipe_size) packets. This 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.
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These two parameters, target_pipe_size and target_run_length,
directly imply most of the individual parameters for the tests below.
Target_pipe_size is the window size, the amount of circulating data
required to meet the target data rate, and implies the scale of the
bursts that the network might experience. Target_run_length is the
amount of data required between losses or ECN marks standard for
standard congestion control.
The individual parameters are for each diagnostic test is described
below. In a few case there are not well established models for what
is considered correct network operation. In many of these cases the
problems might either be partially mitigated by future improvements
to TCP implementations.
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
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 infinitessimally 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 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.
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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 [ABC] is present or delayed ack is disabled.
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 time scale.
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 the target data rate.
Note that if the effective bottleneck link rate is more than half of
the sender interface rate, slowstart bursts become sender interface
rate bursts.
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 data rates, the test MUST NOT be considered passing. If
there is a signature of a network problem (e.g. the run length is too
small) then the test can be considered to fail. Since packet loss
and ECN marks are required to reduce the data rate for standard
transport protocols, the test specification must include suitable
allowances in the prescribed data rates. If there is not sufficient
signature of a network problem, then failing to make the prescribed
data rate must be considered inconclusive. Otherwise there are some
cases where tester failures might cause false negative test results.
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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
looses 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 (attempts) to
maintain 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 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 stream 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 solve 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 [RFC 5681] and de facto standard [CUBIC] congestion
control algorithms have scaling limits, in the sense that as a
network over a fixed RTT and MTU gets faster all congestion control
algorithms get less accurate. In general their noise immunity drops
(a single packet drop should have less effect as individual packets
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become smaller relative to the window size) and the control frequency
of the AIMD sawtooth also drops, meaning that as TCP is using more
total capacity it gets less information about the state of the
network and other traffic. These properties are a direct consequence
of the original Reno design and are implicitly required by the
requirement that all transport protocols be "TCP friendly"
[Guidelines] There are a number of reason to want to specify
performance in term of multiple concurrent flows. Although there are
a number of downsides to @@@@
The use of multiple connections in the Internet has been very
controversial since the beginning of the World-Wide-Web[first
complaint]. Modern browsers open many connections [BScope]. Experts
associated with IETF transport area have frequently spoken against
this practice [long list]. It is not inappropriate to assume some
small number of concurrent connections (e.g. 4 or 6), to compensate
for limitation in TCP. However, choosing too large a number is at
risk of being interpreted as a signal by the web browser community
that this practice has been embraced by the Internet service provider
community. It may not be desirable to send such a signal.
Note that the current proposal for httpbis [SPDY] is specifically
designed to work best with a single TCP connection per client server
pair, because it uses adaptive compression which requires sending
separate compression dictionaries per connection. As long as TCP can
use IW10 and some of the transport parameter can be cached, multiple
connections provide a negative gain, due to the replicated
compression overhead.
The specification to use multiple connections is not recommended for
data rates below several Mb/s, which can be attained with run lengths
under 10000. Since run length goes as the square of the data rates,
at higher rates (see Section 8.3) the run lengths can be unfeasibly
large, and multiple connection might be the only feasible approach.
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
as an intermittent test.
The Intermittent testing can be used for ongoing monitoring for
changes in subpath quality with minimal disruption users. It should
be used in conjunction with the full rate test because this method
assesses an average_run_length over a long time interval w.r.t. user
sessions. It may false fail due to other legitimate congestion
causing traffic or may false pass changes in underlying link
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properties (e.g. a modem retraining to an out of contract lower
rate).
[Need text about bias (false pass) in the shadow of loss caused by
excessive bursts]
6.1.6. Intermittent Scatter Testing
Intermittent scatter testing: when testing the network path to or
from an ISP subscriber aggregation point (CMTS, DSLAM, etc),
intermittent tests can be spread across a pool of users such that no
one users experiences the full impact of the testing, even though the
traffic to or from the ISP subscriber aggregation point is sustained
at full rate.
6.2. Interpreting the Results
6.2.1. Test outcomes
A singleton is a pass/fail measurement of a subpath. If any subpath
fails any test then the end-to-end path is also expected to fail to
attain the target performance under some conditions.
In addition we use "inconclusive outcome" to indicate that a test
failed to attain the required test conditions. A test is
inconclusive if the precomputed traffic pattern was not authentically
generated, test preconditions were not met or the measurement results
were not statistically significantly.
This is important to the extent that the diagnostic tests use
protocols which themselves include built in control systems which
might interfere with some aspect of the test. For example consider a
test that is implemented by adding rate controls and loss
instrumentation to TCP: meeting the run length specification while
failing to attain the specified data rate 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 run length
measurement. (Note that if the measured run length was too small,
the test can be considered to have failed because it doesn't really
matter that the test didn't attain the required data rate).
The vantage independence properties of Model Based Metrics depends on
the accuracy of the distinction between conclusive (pass or fail) and
inconclusive tests. One way to view inconclusive tests is that they
reflect situations where the signature is ambiguous between problems
with the the subpath and problems with the diagnostic test itself.
One of the goals for evolving diagnostic test designs will be to keep
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sharpening this distinction.
One of the goals of evolving the testing process, procedures and
measurement point selection should be to minimize the number of
inconclusive tests.
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 extremely likely to be RTT sensitive,
because TCP's ability to compensate for problems scales with the
number of round trips per second.
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 methods of measurement. In practice, can we compare the
empirically estimated loss 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
transfer performance (loss ratio or other metric, any defect we
define).
As each packet is sent and measured, we have an ongoing estimate of
the performance in terms of defect to total packet ratio (or an
empirical probability). We continue to send until conditions support
a conclusion or a maximum sending limit has been reached.
We have a target_defect_probability, 1 defect per target_run_length,
where a "defect" is defined as a lost packet, a packet with ECN mark,
or other impairment. This constitutes the null Hypothesis:
H0: no more than one defect 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_defect_probability. Based on
analysis of typical values and practical limits on measurement
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duration, we choose four times the H0 probability:
H1: one or more defects 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.
The problem above is formulated to match the Sequential Probability
Ratio Test (SPRT) [StatQC], which 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, in the add-on package for Cross-Validation via Sequential
Testing (CVST) [http://www.r-project.org/] [Rtool] [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:
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Xa = 0 = -h1 + sn
and n = h1 / s
6.2.3. Reordering Tolerance
All tests must be instrumented for reordering [RFC4737].
NB: there is no global consensus for how much reordering tolerance is
appropriate or reasonable. ("None" is absolutely unreasonable.)
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.
[As a strawman, we propose the following:] 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 might be summarized as "needs to be specified in
a FSTDS"
Things to monitor before, during and after a test.
6.3.1. Verify the Traffic Generation Accuracy
[Excess detail for this doc. To be summarized]
for most tests, failing to accurately generate the test traffic
indicates an inconclusive tests, since it has to 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.
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Parameters:
Maximum Data Rate Error The permitted amount that the test traffic
can be different than specified for the current test. This is a
symmetrical bound.
Maximum Data Rate Overage The permitted amount that the test traffic
can be above than specified for the current test.
Maximum Data Rate Underage The permitted amount that the test
traffic can be less than specified for the current test.
6.3.2. Verify the absence of cross traffic
[Excess detail for this doc. To be summarized]
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, the question at hand is likely to be testing if
provider 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.
@@@@ Need to distinguish between ISP managed sharing and unmanaged
sharing. e.g. WiFi
Note that canceling tests due to load on subscriber lines may
introduce sampling errors for testing other parts of the
infrastructure. For this reason tests that are scheduled but not run
due to load should be treated as a special case of "inconclusive".
Use a passive packet or SNMP monitoring to verify that the traffic
volume on the subpath agrees with the traffic generated by a test.
Ideally this should be performed before, during and after each test.
The goal is provide quality assurance on the overall measurement
process, and specifically to detect the following measurement
failure: a user observes unexpectedly poor application performance,
the ISP observes that the access link is running at the rated
capacity. Both fail to observe that the user's computer has been
infected by a virus which is spewing traffic as fast as it can.
Parameters:
Maximum Cross Traffic Data Rate The amount of excess traffic
permitted. Note that this will be different for different tests.
One possible method is an adaptation of: www-didc.lbl.gov/papers/
SCNM-PAM03.pdf D Agarwal etal. "An Infrastructure for Passive
Network Monitoring of Application Data Streams". Use the same
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technique as that paper to trigger the capture of SNMP statistics for
the link.
6.3.3. Additional test preconditions
[Excess detail for this doc. To be summarized]
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]).
Use the procedure above to confirm that the pre-test background
traffic is low enough.
7. Diagnostic Tests
The diagnostic tests are organized by which properties are being
tested: run length, standing queues; slowstart bursts; sender rate
bursts; and combined tests. The combined tests reduce overhead at
the expense of conflating the signatures of multiple 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
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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).
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 [RFC 6673] 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
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queueing. Well behaved generally means lossless for transient
queues, but once the queue has been sustained for a sufficient period
of time (or 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.
Excess losses make loss recovery problematic for the transport
protocol. Non-linear or erratic RTT fluctuations 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
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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
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. 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 [RFC 2309] and [Bufferbloat]. This may cause only minor
symptoms for the dominant flow, but has the potential to make the
link unusable for all 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.
@@@@ This needs further testing.
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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
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 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 nominally out
of scope.
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Also, there are a several details that are not precisely defined.
For starters there is not a standard server interface rate. 1 Gb/s is
very common today, but higher rates (e.g. 10 Gb/s) are becoming 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 [RFC 2861], is
not required, but even if was it does not take effect until an
application pause is longer than an RTO. Since this is standard
behavior, it is desirable that the network be able to deliver it,
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 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. @@@@ Needs more
work and experimentation.
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.
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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.
o This test can be implemented with standard instrumented TCP[RFC
4898], 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.
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, by
applying some additional controls to the traffic. 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 determined by all of the individual
tests described above, for a specific TDS.
If the serving RTT is less than the target_RTT, this constraint is
most easily implemented by clamping the transport window size to
test_window=target_data_rate*serving_RTT/target_MTU. This
test_window size 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, assuming burst size derating equal to the serving_RTT
divided by the target_RTT.
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. Since the serving RTT is
smaller than the target_RTT, the worst case bursts that might be
generated under these conditions are smaller than called for by
Section 7.4
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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.
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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5 Mb/s over a 50 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
This document permits alternate models and parameter derating, as
described in Section 5.2 and Section 5.3. In exchange for this
latitude in the modelling process it requires the ability to
demonstrate authentic applications and protocol implementations
meeting the target end-to-end performance goals over infrastructure
that infinitessimally passes the TDS.
The validation process relies on constructing a test network such
that all of the individual load tests pass only infinitessimally, and
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proving that an authentic application running over a real TCP
implementation (or other protocol as appropriate) can be expected to
meet the end-to-end target parameters on such a network.
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.
@@@@ Need to better specify the workload: both short and long flows.
The difficult part of this process is arranging for each subpath to
infinitesimally pass the individual tests. We suggest two
approaches: constraining resources in devices by configuring them not
to use all available buffer space or data rate; and preloading
subpaths with cross traffic. Note that is it important that a single
environment is constructed that infinitessimally passes all tests,
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.
If a TDS validated according to these procedures is used to inform
public dialog, the validation experiment itself should also be public
with sufficient precision for the experiment to be replicated by
other researchers. All components should either be open source of
fully specified proprietary implementations that are available to the
research community.
TODO: paper proving the validation process.
10. Acknowledgements
Ganga Maguluri suggested the statistical test for measuring loss
probability in the target run length.
Meredith Whittaker for improving the clarity of the communications.
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11. Informative References
[RFC2330] Paxson, V., Almes, G., Mahdavi, J., and M. Mathis,
"Framework for IP Performance Metrics", RFC 2330,
May 1998.
[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
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.
[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.
[BScope] Broswerscope, "Browserscope Network tests", Sept 2012,
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<http://www.browserscope.org/?category=network>.
[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.
[StatQC] Montgomery, D., "Introduction to Statistical Quality
Control - 2nd ed.", ISBN 0-471-51988-X, 1990.
[CVST] Krueger, T. and M. Braun, "R package: Fast Cross-
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 is 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.
These models provide estimates that 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 raw parameter.
Although this document gives a lot of latitude for calculating
target_run_length, people designing suites of tests need to consider
the effect of their choices on the ongoing conversation and tussle
about the relevance of "TCP friendliness" as an appropriate model for
capacity allocation. Choosing a target_run_length that is
substantially smaller than the reference target_run_length specified
in Section 5.2 is equivalent to saying that it is appropriate for the
transport research community to abandon "TCP friendliness" as a
fairness model and to develop more aggressive Internet transport
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protocols, and for applications to continue (or even increase) the
number of connections that they open concurrently.
A.1. Aggregate 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
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. Between losses each sawtooth
delivers (1/2)(Wmin+2*Wmin)(2Wmin) packets in 2*Wmin round trip
times. However, 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.
(@@@@ something is wrong above) Substituting these together we get:
target_run_length = (8/3)(target_pipe_size^2)
Note that this is always 88% of the reference run length.
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
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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
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)
Commentary on the consequence of the choice.
Appendix B. Version Control
Formatted: Mon Oct 21 15:42:35 PDT 2013
Authors' Addresses
Matt Mathis
Google, Inc
1600 Amphitheater Parkway
Mountain View, California 93117
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/
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