Internet Engineering Task Force (IETF) M. Mathis
Request for Comments: 8337 Google, Inc
Category: Experimental A. Morton
ISSN: 2070-1721 AT&T Labs
March 2018
Model-Based Metrics for Bulk Transport Capacity
Abstract
This document introduces a new class of Model-Based Metrics designed
to assess if a complete Internet path can be expected to meet a
predefined Target Transport Performance by applying a suite of IP
diagnostic tests to successive subpaths. The subpath-at-a-time tests
can be robustly applied to critical infrastructure, such as network
interconnections or even individual devices, to accurately detect if
any part of the infrastructure will prevent paths traversing it from
meeting the Target Transport Performance.
Model-Based Metrics rely on mathematical models to specify a Targeted
IP Diagnostic Suite, a set of IP diagnostic tests designed to assess
whether common transport protocols can be expected to meet a
predetermined Target Transport Performance over an Internet path.
For Bulk Transport Capacity, the IP diagnostics are built using test
streams and statistical criteria for evaluating the packet transfer
that mimic TCP over the complete path. The temporal structure of the
test stream (e.g., bursts) mimics TCP or other transport protocols
carrying bulk data over a long path. However, they are constructed
to be independent of the details of the subpath under test, end
systems, or applications. Likewise, the success criteria evaluates
the packet transfer statistics of the subpath against criteria
determined by protocol performance models applied to the Target
Transport Performance of the complete path. The success criteria
also does not depend on the details of the subpath, end systems, or
applications.
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Status of This Memo
This document is not an Internet Standards Track specification; it is
published for examination, experimental implementation, and
evaluation.
This document defines an Experimental Protocol for the Internet
community. This document is a product of the Internet Engineering
Task Force (IETF). It represents the consensus of the IETF
community. It has received public review and has been approved for
publication by the Internet Engineering Steering Group (IESG). Not
all documents approved by the IESG are candidates for any level of
Internet Standard; see Section 2 of RFC 7841.
Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
https://www.rfc-editor.org/info/rfc8337.
Copyright Notice
Copyright (c) 2018 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(
https://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents
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to this document. Code Components extracted from this document must
include Simplified BSD License text as described in Section 4.e of
the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
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Table of Contents
1. Introduction ....................................................4
2. Overview ........................................................5
3. Terminology .....................................................8
3.1. General Terminology ........................................8
3.2. Terminology about Paths ...................................10
3.3. Properties ................................................11
3.4. Basic Parameters ..........................................12
3.5. Ancillary Parameters ......................................13
3.6. Temporal Patterns for Test Streams ........................14
3.7. Tests .....................................................15
4. Background .....................................................16
4.1. TCP Properties ............................................18
4.2. Diagnostic Approach .......................................20
4.3. New Requirements Relative to RFC 2330 .....................21
5. Common Models and Parameters ...................................22
5.1. Target End-to-End Parameters ..............................22
5.2. Common Model Calculations .................................22
5.3. Parameter Derating ........................................23
5.4. Test Preconditions ........................................24
6. Generating Test Streams ........................................24
6.1. Mimicking Slowstart .......................................25
6.2. Constant Window Pseudo CBR ................................27
6.3. Scanned Window Pseudo CBR .................................28
6.4. Concurrent or Channelized Testing .........................28
7. Interpreting the Results .......................................29
7.1. Test Outcomes .............................................29
7.2. Statistical Criteria for Estimating run_length ............31
7.3. Reordering Tolerance ......................................33
8. IP Diagnostic Tests ............................................34
8.1. Basic Data Rate and Packet Transfer Tests .................34
8.1.1. Delivery Statistics at Paced Full Data Rate ........35
8.1.2. Delivery Statistics at Full Data Windowed Rate .....35
8.1.3. Background Packet Transfer Statistics Tests ........35
8.2. Standing Queue Tests ......................................36
8.2.1. Congestion Avoidance ...............................37
8.2.2. Bufferbloat ........................................37
8.2.3. Non-excessive Loss .................................38
8.2.4. Duplex Self-Interference ...........................38
8.3. Slowstart Tests ...........................................39
8.3.1. Full Window Slowstart Test .........................39
8.3.2. Slowstart AQM Test .................................39
8.4. Sender Rate Burst Tests ...................................40
8.5. Combined and Implicit Tests ...............................41
8.5.1. Sustained Full-Rate Bursts Test ....................41
8.5.2. Passive Measurements ...............................42
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9. Example ........................................................43
9.1. Observations about Applicability ..........................44
10. Validation ....................................................45
11. Security Considerations .......................................46
12. IANA Considerations ...........................................47
13. Informative References ........................................47
Appendix A. Model Derivations ....................................52
A.1. Queueless Reno ............................................52
Appendix B. The Effects of ACK Scheduling ........................53
Acknowledgments ...................................................55
Authors' Addresses ................................................55
1. Introduction
Model-Based Metrics (MBM) rely on peer-reviewed mathematical models
to specify a Targeted IP Diagnostic Suite (TIDS), a set of IP
diagnostic tests designed to assess whether common transport
protocols can be expected to meet a predetermined Target Transport
Performance over an Internet path. This document describes the
modeling framework to derive the test parameters for assessing an
Internet path's ability to support a predetermined Bulk Transport
Capacity.
Each test in TIDS measures some aspect of IP packet transfer needed
to meet the Target Transport Performance. For Bulk Transport
Capacity, the TIDS includes IP diagnostic tests to verify that there
is sufficient IP capacity (data rate), sufficient queue space at
bottlenecks to absorb and deliver typical transport bursts, low
enough background packet loss ratio to not interfere with congestion
control, and other properties described below. Unlike typical IP
Performance Metrics (IPPM) that yield measures of network properties,
Model-Based Metrics nominally yield pass/fail evaluations of the
ability of standard transport protocols to meet the specific
performance objective over some network path.
In most cases, the IP diagnostic tests can be implemented by
combining existing IPPM metrics with additional controls for
generating test streams having a specified temporal structure (bursts
or standing queues caused by constant bit rate streams, etc.) and
statistical criteria for evaluating packet transfer. The temporal
structure of the test streams mimics transport protocol behavior over
the complete path; the statistical criteria models the transport
protocol's response to less-than-ideal IP packet transfer. In
control theory terms, the tests are "open loop". Note that running a
test requires the coordinated activity of sending and receiving
measurement points.
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This document addresses Bulk Transport Capacity. It describes an
alternative to the approach presented in "A Framework for Defining
Empirical Bulk Transfer Capacity Metrics" [RFC3148]. Other Model-
Based Metrics may cover other applications and transports, such as
Voice over IP (VoIP) over UDP, RTP, and new transport protocols.
This document assumes a traditional Reno TCP-style, self-clocked,
window-controlled transport protocol that uses packet loss and
Explicit Congestion Notification (ECN) Congestion Experienced (CE)
marks for congestion feedback. There are currently some experimental
protocols and congestion control algorithms that are rate based or
otherwise fall outside of these assumptions. In the future, these
new protocols and algorithms may call for revised models.
The MBM approach, i.e., mapping Target Transport Performance to a
Targeted IP Diagnostic Suite (TIDS) of IP tests, solves some
intrinsic problems with using TCP or other throughput-maximizing
protocols for measurement. In particular, all throughput-maximizing
protocols (especially TCP congestion control) cause some level of
congestion in order to detect when they have reached the available
capacity limitation of the network. This self-inflicted congestion
obscures the network properties of interest and introduces non-linear
dynamic equilibrium behaviors that make any resulting measurements
useless as metrics because they have no predictive value for
conditions or paths different from that of the measurement itself.
In order to prevent these effects, it is necessary to avoid the
effects of TCP congestion control in the measurement method. These
issues are discussed at length in Section 4. Readers who are
unfamiliar with basic properties of TCP and TCP-like congestion
control may find it easier to start at Section 4 or 4.1.
A Targeted IP Diagnostic Suite does not have such difficulties. IP
diagnostics can be constructed such that they make strong statistical
statements about path properties that are independent of measurement
details, such as vantage and choice of measurement points.
2. Overview
This document describes a modeling framework for deriving a Targeted
IP Diagnostic Suite from a predetermined Target Transport
Performance. It is not a complete specification and relies on other
standards documents to define important details such as packet type-P
selection, sampling techniques, vantage selection, etc. Fully
Specified Targeted IP Diagnostic Suites (FSTIDSs) define all of these
details. A Targeted IP Diagnostic Suite (TIDS) refers to the subset
of such a specification that is in scope for this document. This
terminology is further defined in Section 3.
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Section 4 describes some key aspects of TCP behavior and what they
imply about the requirements for IP packet transfer. Most of the IP
diagnostic tests needed to confirm that the path meets these
properties can be built on existing IPPM metrics, with the addition
of statistical criteria for evaluating packet transfer and, in a few
cases, new mechanisms to implement the required temporal structure.
(One group of tests, the standing queue tests described in
Section 8.2, don't correspond to existing IPPM metrics, but suitable
new IPPM metrics can be patterned after the existing definitions.)
Figure 1 shows the MBM modeling and measurement framework. The
Target Transport Performance at the top of the figure is determined
by the needs of the user or application, which are outside the scope
of this document. For Bulk Transport Capacity, the main performance
parameter of interest is the Target Data Rate. However, since TCP's
ability to compensate for less-than-ideal network conditions is
fundamentally affected by the Round-Trip Time (RTT) and the Maximum
Transmission Unit (MTU) of the complete path, these parameters must
also be specified in advance based on knowledge about the intended
application setting. They may reflect a specific application over a
real path through the Internet or an idealized application and
hypothetical path representing a typical user community. Section 5
describes the common parameters and models derived from the Target
Transport Performance.
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Target Transport Performance
(Target Data Rate, Target RTT, and Target MTU)
|
________V_________
| mathematical |
| models |
| |
------------------
Traffic parameters | | Statistical criteria
| |
_______V____________V____Targeted IP____
| | * * * | Diagnostic Suite |
_____|_______V____________V________________ |
__|____________V____________V______________ | |
| IP diagnostic tests | | |
| | | | | |
| _____________V__ __V____________ | | |
| | traffic | | Delivery | | | |
| | pattern | | Evaluation | | | |
| | generation | | | | | |
| -------v-------- ------^-------- | | |
| | v test stream via ^ | | |--
| | -->======================>-- | | |
| | subpath under test | |-
----V----------------------------------V--- |
| | | | | |
V V V V V V
fail/inconclusive pass/fail/inconclusive
(traffic generation status) (test result)
Figure 1: Overall Modeling Framework
Mathematical TCP models are used to determine traffic parameters and
subsequently to design traffic patterns that mimic TCP (which has
burst characteristics at multiple time scales) or other transport
protocols delivering bulk data and operating at the Target Data Rate,
MTU, and RTT over a full range of conditions. Using the techniques
described in Section 6, the traffic patterns are generated based on
the three Target parameters of the complete path (Target Data Rate,
Target RTT, and Target MTU), independent of the properties of
individual subpaths. As much as possible, the test streams are
generated deterministically (precomputed) to minimize the extent to
which test methodology, measurement points, measurement vantage, or
path partitioning affect the details of the measurement traffic.
Section 7 describes packet transfer statistics and methods to test
against the statistical criteria provided by the mathematical models.
Since the statistical criteria typically apply to the complete path
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(a composition of subpaths) [RFC6049], in situ testing requires that
the end-to-end statistical criteria be apportioned as separate
criteria for each subpath. Subpaths that are expected to be
bottlenecks would then be permitted to contribute a larger fraction
of the end-to-end packet loss budget. In compensation, subpaths that
are not expected to exhibit bottlenecks must be constrained to
contribute less packet loss. Thus, the statistical criteria for each
subpath in each test of a TIDS is an apportioned share of the end-to-
end statistical criteria for the complete path that was determined by
the mathematical model.
Section 8 describes the suite of individual tests needed to verify
all of the required IP delivery properties. A subpath passes if and
only if all of the individual IP diagnostic tests pass. Any subpath
that fails any test indicates that some users are likely to fail to
attain their Target Transport Performance under some conditions. In
addition to passing or failing, a test can be deemed inconclusive for
a number of reasons, including the following: the precomputed traffic
pattern was not accurately generated, the measurement results were
not statistically significant, the test failed to meet some required
test preconditions, etc. If all tests pass but some are
inconclusive, then the entire suite is deemed to be inconclusive.
In Section 9, we present an example TIDS that might be representative
of High Definition (HD) video and illustrate how Model-Based Metrics
can be used to address difficult measurement situations, such as
confirming that inter-carrier exchanges have sufficient performance
and capacity to deliver HD video between ISPs.
Since there is some uncertainty in the modeling process, Section 10
describes a validation procedure to diagnose and minimize false
positive and false negative results.
3. Terminology
Terms containing underscores (rather than spaces) appear in equations
and typically have algorithmic definitions.
3.1. General Terminology
Target: A general term for any parameter specified by or derived
from the user's application or transport performance requirements.
Target Transport Performance: Application or transport performance
target values for the complete path. For Bulk Transport Capacity
defined in this document, the Target Transport Performance
includes the Target Data Rate, Target RTT, and Target MTU as
described below.
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Target Data Rate: The specified application data rate required for
an application's proper operation. Conventional Bulk Transport
Capacity (BTC) metrics are focused on the Target Data Rate;
however, these metrics have little or no predictive value because
they do not consider the effects of the other two parameters of
the Target Transport Performance -- the RTT and MTU of the
complete paths.
Target RTT (Round-Trip Time): The specified baseline (minimum) RTT
of the longest complete path over which the user expects to be
able 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 IP packet transfer
statistics. The Target RTT must be specified considering
appropriate packets sizes: MTU-sized packets on the forward path
and ACK-sized packets (typically, header_overhead) on the return
path. Note that Target RTT is specified and not measured; MBM
measurements derived for a given target_RTT will be applicable to
any path with a smaller RTT.
Target MTU (Maximum Transmission Unit): The specified maximum MTU
supported by the complete path over which the application expects
to meet the target performance. In this document, we assume a
1500-byte MTU unless otherwise specified. If a subpath has a
smaller MTU, then it becomes the Target MTU for the complete path,
and all model calculations and subpath tests must use the same
smaller MTU.
Targeted IP Diagnostic Suite (TIDS): A set of IP diagnostic tests
designed to determine if an otherwise ideal complete path
containing the subpath under test can sustain flows at a specific
target_data_rate using packets with a size of target_MTU when the
RTT of the complete path is target_RTT.
Fully Specified Targeted IP Diagnostic Suite (FSTIDS): A TIDS
together with additional specifications such as measurement packet
type ("type-p" [RFC2330]) that are out of scope for this document
and need to be drawn from other standards documents.
Bulk Transport Capacity (BTC): Bulk Transport Capacity metrics
evaluate an Internet path's ability to carry bulk data, such as
large files, streaming (non-real-time) video, and, under some
conditions, web images and other content. Prior efforts to define
BTC metrics have been based on [RFC3148], which predates our
understanding of TCP and the requirements described in Section 4.
In general, "Bulk Transport" indicates that performance is
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determined by the interplay between the network, cross traffic,
and congestion control in the transport protocol. It excludes
situations where performance is dominated by the RTT alone (e.g.,
transactions) or bottlenecks elsewhere, such as in the application
itself.
IP diagnostic tests: Measurements or diagnostics to determine if
packet transfer statistics meet some precomputed target.
traffic patterns: The temporal patterns or burstiness of traffic
generated by applications over transport protocols such as TCP.
There are several mechanisms that cause bursts at various
timescales as described in Section 4.1. Our goal here is to mimic
the range of common patterns (burst sizes, rates, etc.), without
tying our applicability to specific applications, implementations,
or technologies, which are sure to become stale.
Explicit Congestion Notification (ECN): See [RFC3168].
packet transfer statistics: Raw, detailed, or summary statistics
about packet transfer properties of the IP layer including packet
losses, ECN Congestion Experienced (CE) marks, reordering, or any
other properties that may be germane to transport performance.
packet loss ratio: As defined in [RFC7680].
apportioned: To divide and allocate, for example, budgeting packet
loss across multiple subpaths such that the losses will accumulate
to less than a specified end-to-end loss ratio. Apportioning
metrics is essentially the inverse of the process described in
[RFC5835].
open loop: A control theory term used to describe a class of
techniques where systems that naturally exhibit circular
dependencies can be analyzed by suppressing some of the
dependencies, such that the resulting dependency graph is acyclic.
3.2. Terminology about Paths
See [RFC2330] and [RFC7398] for existing terms and definitions.
data sender: Host sending data and receiving ACKs.
data receiver: Host receiving data and sending ACKs.
complete path: The end-to-end path from the data sender to the data
receiver.
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subpath: A portion of the complete path. Note that there is no
requirement that subpaths be non-overlapping. A subpath can be as
small as a single device, link, or interface.
measurement point: Measurement points as described in [RFC7398].
test path: A path between two measurement points that includes a
subpath of the complete path under test. If the measurement
points are off path, the test path may include "test leads"
between the measurement points and the subpath.
dominant bottleneck: The bottleneck that generally determines most
packet transfer statistics for the entire path. It typically
determines a flow's self-clock timing, packet loss, and ECN CE
marking rate, with other potential bottlenecks having less effect
on the packet transfer statistics. See Section 4.1 on TCP
properties.
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
network resources (such as bandwidth and/or queue capacity).
3.3. Properties
The following properties are determined by the complete path and
application. These 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 in bytes per second. This
is the payload data rate and explicitly excludes transport-level
and lower-level headers (TCP/IP or other protocols),
retransmissions, and other overhead that is not part of the total
quantity of data delivered to the application.
IP rate: The actual number of IP-layer bytes delivered through a
subpath, per unit time, including TCP and IP headers, retransmits,
and other TCP/IP overhead. This is the same as IP-type-P Link
Usage in [RFC5136].
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IP capacity: The maximum number of IP-layer bytes that can be
transmitted through a subpath, per unit time, including TCP and IP
headers, retransmits, and other TCP/IP overhead. This is the same
as IP-type-P Link Capacity in [RFC5136].
bottleneck IP capacity: The IP capacity of the dominant bottleneck
in the forward path. All throughput-maximizing protocols estimate
this capacity by observing the IP rate delivered through the
bottleneck. Most protocols derive their self-clocks from the
timing of this data. See Section 4.1 and Appendix B for more
details.
implied bottleneck IP capacity: The bottleneck IP capacity implied
by the ACKs returning from the receiver. It is determined by
looking at how much application data the ACK stream at the sender
reports as delivered to the data receiver per unit time at various
timescales. If the return path is thinning, batching, or
otherwise altering the ACK timing, the implied bottleneck IP
capacity over short timescales might be substantially larger than
the bottleneck IP capacity averaged over a full RTT. Since TCP
derives its clock from the data delivered through the bottleneck,
the front path must have sufficient buffering to absorb any data
bursts at the dimensions (size and IP rate) implied by the ACK
stream, which are potentially doubled during slowstart. If the
return path is not altering the ACK stream, then the implied
bottleneck IP capacity will be the same as the bottleneck IP
capacity. See Section 4.1 and Appendix B for more details.
sender interface rate: The IP rate that corresponds to the IP
capacity of the data sender's interface. Due to sender efficiency
algorithms, including technologies such as TCP segmentation
offload (TSO), nearly all modern servers deliver data in bursts at
full interface link rate. 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 acknowledgments (ACKs). For TCP, the Maximum Segment
Size (MSS) is the Target MTU minus the header_overhead.
3.4. Basic Parameters
Basic parameters common to models and subpath tests are defined here.
Formulas for target_window_size and target_run_length appear in
Section 5.2. Note that these are mixed between application transport
performance (excludes headers) and IP performance (includes TCP
headers and retransmissions as part of the IP payload).
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Network power: The observed data rate divided by the observed RTT.
Network power indicates how effectively a transport protocol is
filling a network.
Window [size]: The total quantity of data carried by packets
in-flight plus the data represented by ACKs circulating in the
network is referred to as the window. See Section 4.1. Sometimes
used with other qualifiers (congestion window (cwnd) or receiver
window) to indicate which mechanism is controlling the window.
pipe size: A general term for the number of packets needed in flight
(the window size) to exactly fill a network path or subpath. It
corresponds to the window size, which maximizes network power. It
is often used with additional qualifiers to specify which path,
under what conditions, etc.
target_window_size: The average number of packets in flight (the
window size) needed to meet the Target Data Rate for the specified
Target RTT and Target MTU. It 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 (expected to be) delivered between
losses or ECN CE marks. Nominally, it is one over the sum of the
loss and ECN CE marking probabilities, if they are independently
and identically distributed.
target_run_length: The target_run_length is an estimate of the
minimum number of non-congestion marked packets needed between
losses or ECN CE marks necessary to attain the target_data_rate
over a path with the specified target_RTT and target_MTU, as
computed by a mathematical model of TCP congestion control. A
reference calculation is shown in Section 5.2 and alternatives in
Appendix A.
reference target_run_length: target_run_length computed precisely by
the method in Section 5.2. This is likely to be slightly more
conservative than required by modern TCP implementations.
3.5. Ancillary Parameters
The following ancillary parameters are 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 TIDS validation
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procedures, described in Section 10. Models can be derated by
including a multiplicative derating factor to make tests less
stringent.
subpath_IP_capacity: The IP capacity of a specific subpath.
test path: A subpath of a complete path under test.
test_path_RTT: The RTT observed between two measurement points using
packet sizes that are consistent with the transport protocol.
This is generally MTU-sized packets of the forward path and
packets with a size of header_overhead on the return path.
test_path_pipe: The pipe size of a test path. Nominally, it is the
test_path_RTT times the test path IP_capacity.
test_window: The smallest window sufficient to meet or exceed the
target_rate when operating with a pure self-clock over a test
path. The test_window is typically calculated as follows (but see
the discussion in Appendix B about the effects of channel
scheduling on RTT):
ceiling(target_data_rate * test_path_RTT / (target_MTU -
header_overhead))
On some test paths, the test_window may need to be adjusted
slightly to compensate for the RTT being inflated by the devices
that schedule packets.
3.6. Temporal Patterns for Test Streams
The terminology below is used to define temporal patterns for test
streams. These patterns are designed to mimic TCP behavior, as
described in Section 4.1.
packet headway: Time interval between packets, specified from the
start of one to the start of the next. For example, if packets
are sent with a 1 ms headway, there will be exactly 1000 packets
per second.
burst headway: Time interval between bursts, specified from the
start of the first packet of one burst to the start of the first
packet of the next burst. For example, if 4 packet bursts are
sent with a 1 ms burst headway, there will be exactly 4000 packets
per second.
paced single packets: Individual packets sent at the specified rate
or packet headway.
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paced bursts: Bursts on a timer. Specify any 3 of the following:
average data rate, packet size, burst size (number of packets),
and burst headway (burst start to start). By default, the bursts
are assumed to occur at full sender interface rate, such that the
packet headway within each burst is the minimum supported by the
sender's interface. Under some conditions, it is useful to
explicitly specify the packet headway within each burst.
slowstart rate: Paced bursts of four packets each at an average data
rate equal to twice the implied bottleneck IP capacity (but not
more than the sender interface rate). This mimics TCP slowstart.
This is a two-level burst pattern described in more detail in
Section 6.1. If the implied bottleneck IP capacity is more than
half of the sender interface rate, the slowstart rate becomes the
sender interface rate.
slowstart burst: A specified number of packets in a two-level burst
pattern that resembles slowstart. This mimics one round of TCP
slowstart.
repeated slowstart bursts: Slowstart bursts repeated once per
target_RTT. For TCP, each burst would be twice as large as the
prior burst, and the sequence would end at the first ECN CE mark
or lost packet. For measurement, all slowstart bursts would be
the same size (nominally, target_window_size but other sizes might
be specified), and the ECN CE marks and lost packets are counted.
3.7. Tests
The tests described in this document can be grouped according to
their applicability.
Capacity tests: Capacity tests determine if a network subpath has
sufficient capacity to deliver the Target Transport Performance.
As long as the test stream is within the proper envelope for the
Target Transport Performance, the average packet losses or ECN CE
marks must be below the statistical criteria 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 actions of other
network users. By definition, capacity tests also consume
significant network resources (data capacity and/or queue buffer
space), and the test schedules must be balanced by their cost.
Monitoring tests: Monitoring tests are designed to capture the most
important aspects of a capacity test without presenting excessive
ongoing load themselves. As such, they may miss some details of
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the network's performance but can serve as a useful reduced-cost
proxy for a capacity test, for example, to support continuous
production network monitoring.
Engineering tests: Engineering tests evaluate how network algorithms
(such as Active Queue Management (AQM) and channel allocation)
interact with TCP-style self-clocked protocols and adaptive
congestion control based on packet loss and ECN CE marks. These
tests are likely to have complicated interactions with cross
traffic and, under some conditions, can be inversely sensitive to
load. For example, a test to verify that an AQM algorithm causes
ECN CE marks or packet drops early enough to limit queue occupancy
may experience a false pass result in the presence of cross
traffic. It is important that engineering tests be performed
under a wide range of conditions, including both in situ and bench
testing, and over a wide variety of load conditions. Ongoing
monitoring is less likely to be useful for engineering tests,
although sparse in situ testing might be appropriate.
4. Background
When "Framework for IP Performance Metrics" [RFC2330] was published
in 1998, sound Bulk Transport Capacity (BTC) measurement was known to
be well beyond our capabilities. Even when "A Framework for Defining
Empirical Bulk Transfer Capacity Metrics" [RFC3148] was published, we
knew that we didn't really understand the problem. Now, in
hindsight, we understand why assessing BTC is such a difficult
problem:
o TCP is a control system with circular dependencies -- everything
affects performance, including components that are explicitly not
part of the test (for example, the host processing power is not
in-scope of path performance tests).
o Congestion control is a dynamic equilibrium process, similar to
processes observed in chemistry and other fields. The network and
transport protocols find an operating point that balances opposing
forces: the transport protocol pushing harder (raising the data
rate and/or window) while the network pushes back (raising packet
loss ratio, RTT, and/or ECN CE marks). By design, TCP congestion
control keeps raising the data rate until the network gives some
indication that its capacity has been exceeded by dropping packets
or adding ECN CE marks. If a TCP sender accurately fills a path
to its IP capacity (e.g., the bottleneck is 100% utilized), then
packet losses and ECN CE marks are mostly determined by the TCP
sender and how aggressively it seeks additional capacity; they are
not determined by the network itself, because the network must
send exactly the signals that TCP needs to set its rate.
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o TCP's ability to compensate for network impairments (such as loss,
delay, and delay variation, outside of those caused by TCP itself)
is directly proportional to the number of send-ACK round-trip
exchanges per second (i.e., inversely proportional to the RTT).
As a consequence, an impaired subpath may pass a short RTT local
test even though it fails when the subpath is extended by an
effectively perfect network to some larger RTT.
o TCP has an extreme form of the Observer Effect (colloquially known
as the "Heisenberg Effect"). Measurement and cross traffic
interact in unknown and ill-defined ways. The situation is
actually worse than the traditional physics problem where you can
at least estimate bounds on the relative momentum of the
measurement and measured particles. In general, for network
measurement, you cannot determine even the order of magnitude of
the effect. It is possible to construct measurement scenarios
where the measurement traffic starves real user traffic, yielding
an overly inflated measurement. The inverse is also possible: the
user traffic can fill the network, such that the measurement
traffic detects only minimal available capacity. In general, you
cannot determine which scenario might be in effect, so you cannot
gauge the relative magnitude of the uncertainty introduced by
interactions with other network traffic.
o As a consequence of the properties listed above, it is difficult,
if not impossible, for two independent implementations (hardware
or software) of TCP congestion control to produce equivalent
performance results [RFC6576] under the same network conditions.
These properties are a consequence of the dynamic equilibrium
behavior intrinsic to how all throughput-maximizing protocols
interact with the Internet. These protocols rely on control systems
based on estimated network metrics to regulate the quantity of data
to send into the network. The packet-sending characteristics in turn
alter the network properties estimated by the control system metrics,
such that there are circular dependencies between every transmission
characteristic and every estimated metric. Since some of these
dependencies are nonlinear, the entire system is nonlinear, and any
change anywhere causes a difficult-to-predict response in network
metrics. As a consequence, Bulk Transport Capacity metrics have not
fulfilled the analytic framework envisioned in [RFC2330].
Model-Based Metrics overcome these problems by making the measurement
system open loop: the packet transfer statistics (akin to the network
estimators) do not affect the traffic or traffic patterns (bursts),
which are computed on the basis of the Target Transport Performance.
A path or subpath meeting the Target Transfer Performance
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requirements would exhibit packet transfer statistics and estimated
metrics that would not cause the control system to slow the traffic
below the Target Data Rate.
4.1. TCP Properties
TCP and other self-clocked protocols (e.g., the Stream Control
Transmission Protocol (SCTP)) carry the vast majority of all Internet
data. Their dominant bulk data transport behavior is to have an
approximately fixed quantity of data and acknowledgments (ACKs)
circulating in the network. The data receiver reports arriving data
by returning ACKs to the data sender, and the data sender typically
responds by sending approximately 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 system is that it is
self-clocked: the data transmissions are a reflection of the ACKs
that were delivered by the network, and the ACKs are a reflection of
the data arriving from the network.
A number of protocol features cause bursts of data, even in idealized
networks that can be modeled as simple queuing systems.
During slowstart, the IP rate is doubled on each RTT by sending twice
as much data as was delivered to the receiver during the prior RTT.
Each returning ACK causes the sender to transmit twice the data the
ACK reported arriving at the receiver. For slowstart to be able to
fill the pipe, 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 CE mark. For example, with classic Reno
congestion control, an optimal slowstart has to end with a burst that
is twice the bottleneck rate for one RTT in duration. This burst
causes a queue that is equal to the pipe size (i.e., the window is
twice the pipe size), so when the window is halved in response to the
first packet loss, the new window will be the pipe size.
Note that if the bottleneck IP rate is less than half of the capacity
of the front path (which is almost always the case), the slowstart
bursts will not by themselves cause significant queues anywhere else
along the front path; they primarily exercise the queue at the
dominant bottleneck.
Several common efficiency algorithms also cause bursts. The self-
clock is typically applied to groups of packets: the receiver's
delayed ACK algorithm generally sends only one ACK per two data
segments. Furthermore, modern senders use TCP segmentation offload
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(TSO) to reduce CPU overhead. The sender's software stack builds
super-sized TCP segments that the TSO hardware splits into MTU-sized
segments on the wire. The net effect of TSO, delayed ACK, and other
efficiency algorithms is to send bursts of segments at full sender
interface rate.
Note that these efficiency algorithms are almost always in effect,
including during slowstart, such that slowstart typically has a two-
level burst structure. Section 6.1 describes slowstart in more
detail.
Additional sources of bursts include TCP's initial window [RFC6928],
application pauses, channel allocation mechanisms, and network
devices that schedule ACKs. Appendix B describes these last two
items. If the application pauses (e.g., stops reading or writing
data) for some fraction of an RTT, many TCP implementations catch up
to their earlier window size by sending a burst of data at the full
sender interface rate. To fill a network with a realistic
application, the network has to be able to tolerate sender interface
rate bursts large enough to restore the prior window following
application pauses.
Although the sender interface rate bursts are typically smaller than
the last burst of a slowstart, they are at a higher IP 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. It is known that these bursts can hurt network
performance, especially in conjunction with other queue pressure;
however, we are not aware of any models for estimating the impact or
prescribing limits on the size or frequency of sender rate bursts.
In conclusion, to verify that a path can meet a Target Transport
Performance, it is necessary to independently confirm that the path
can tolerate bursts at the scales that can be caused by the above
mechanisms. Three cases are believed to be sufficient:
o Two-level slowstart bursts sufficient to get connections started
properly.
o Ubiquitous sender interface rate bursts caused by efficiency
algorithms. We assume four packet bursts to be the most common
case, since it matches the effects of delayed ACK during
slowstart. These bursts should be assumed not to significantly
affect packet transfer statistics.
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o Infrequent sender interface rate bursts that are the maximum of
the full target_window_size and the initial window size (10
segments in [RFC6928]). The target_run_length may be derated for
these large fast bursts.
If a subpath can meet the required packet loss ratio for bursts at
all of these scales, then it has sufficient buffering at all
potential bottlenecks to tolerate any of the bursts that are likely
introduced by TCP or other transport protocols.
4.2. Diagnostic Approach
A complete path is expected to be able to attain a specified Bulk
Transport Capacity if the path's RTT is equal to or smaller than the
Target RTT, the path's MTU is equal to or larger than the Target MTU,
and all of the following conditions are met:
1. The IP capacity is above the Target Data Rate by a sufficient
margin to cover all TCP/IP overheads. This can be confirmed by
the tests described in Section 8.1 or any number of IP capacity
tests adapted to implement MBM.
2. The observed packet transfer statistics are better than required
by a suitable TCP performance model (e.g., fewer packet losses or
ECN CE marks). See Section 8.1 or any number of low- or fixed-
rate packet loss tests outside of MBM.
3. There is sufficient buffering at the dominant bottleneck to
absorb a slowstart burst large enough to get the flow out of
slowstart at a suitable window size. See Section 8.3.
4. There is sufficient buffering in the front path to absorb and
smooth sender interface rate bursts at all scales that are likely
to be generated by the application, any channel arbitration in
the ACK path, or any other mechanisms. See Section 8.4.
5. When there is a slowly rising standing queue at the bottleneck,
then the onset of packet loss has to be at an appropriate point
(in time or in queue depth) and has to be progressive, for
example, by use of Active Queue Management [RFC7567]. See
Section 8.2.
6. When there is a standing queue at a bottleneck for a shared media
subpath (e.g., a half-duplex link), there must be a suitable
bound on the interaction between ACKs and data, for example, due
to the channel arbitration mechanism. See Section 8.2.4.
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Note that conditions 1 through 4 require capacity tests for
validation and thus may need to be monitored on an ongoing basis.
Conditions 5 and 6 require engineering tests, which are best
performed in controlled environments (e.g., bench tests). They won't
generally fail due to load but may fail in the field (e.g., due to
configuration errors, etc.) and thus should be spot checked.
A tool that can perform many of the tests is available from
[MBMSource].
4.3. New Requirements Relative to RFC 2330
Model-Based Metrics are designed to fulfill some additional
requirements that were not recognized at the time RFC 2330 [RFC2330]
was published. These missing requirements may have significantly
contributed to policy difficulties in the IP measurement space. Some
additional requirements are:
o IP metrics must be actionable by the ISP -- they have to be
interpreted in terms of behaviors or properties at the IP or lower
layers that an ISP can test, repair, and verify.
o Metrics should be spatially composable, such that measures of
concatenated paths should be predictable from subpaths.
o Metrics must be vantage point invariant over a significant range
of measurement point choices, including off-path measurement
points. The only requirements for Measurement Point (MP)
selection should be that the RTT between the MPs is below some
reasonable bound and that the effects of the "test leads"
connecting MPs to the subpath under test can be calibrated out of
the measurements. The latter might be accomplished if the test
leads are effectively ideal or their properties can be deducted
from the measurements between the MPs. While many tests require
that the test leads have at least as much IP capacity as the
subpath under test, some do not, for example, the Background
Packet Transfer Statistics Tests described in Section 8.1.3.
o Metric measurements should be repeatable by multiple parties with
no specialized access to MPs or diagnostic infrastructure. It
should be possible for different parties to make the same
measurement and observe the same results. In particular, it is
important that both a consumer (or the consumer's delegate) and
ISP be able to perform the same measurement and get the same
result. Note that vantage independence is key to meeting this
requirement.
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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 3. These parameters are
determined by the needs of the application or the ultimate end user
and the complete Internet path over which the application is expected
to operate. The target parameters are in units that make sense to
layers above the TCP layer: payload bytes delivered to the
application. They exclude overheads associated with TCP and IP
headers, retransmits and other protocols (e.g., DNS). Note that
IP-based network services include TCP headers and retransmissions as
part of delivered payload; this difference (header_overhead) is
recognized in calculations below.
Other end-to-end parameters defined in Section 3 include the
effective bottleneck data rate, the sender interface data rate, and
the TCP and IP header sizes.
The target_data_rate must be smaller than all subpath IP capacities
by enough headroom to carry the transport protocol overhead,
explicitly including retransmissions and an allowance for
fluctuations in TCP's actual data rate. Specifying a
target_data_rate with insufficient headroom is likely to result in
brittle measurements that have little predictive value.
Note that the target parameters can be specified for a hypothetical
path (for example, to construct TIDS designed for bench testing in
the absence of a real application) or for a live in situ test of
production infrastructure.
The number of concurrent connections is explicitly not a parameter in
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 6.4 applies.
5.2. Common Model Calculations
The Target Transport Performance is used to derive the
target_window_size and the reference target_run_length.
The target_window_size is the average window size in packets needed
to meet the target_rate, for the specified target_RTT and target_MTU.
To calculate target_window_size:
target_window_size = ceiling(target_rate * target_RTT / (target_MTU -
header_overhead))
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The target_run_length is an estimate of the minimum required number
of unmarked packets that must be delivered between losses or ECN CE
marks, as computed by a mathematical model of TCP congestion control.
The derivation here is parallel to the derivation in [MSMO97] and, by
design, is quite conservative.
The reference target_run_length is derived as follows. Assume the
subpath_IP_capacity is infinitesimally larger than the
target_data_rate plus the required header_overhead. Then,
target_window_size also predicts the onset of queuing. A larger
window will cause a standing queue at the bottleneck.
Assume the transport protocol is using standard Reno-style Additive
Increase Multiplicative Decrease (AIMD) congestion control [RFC5681]
(but not Appropriate Byte Counting [RFC3465]) and the receiver is
using standard delayed ACKs. Reno increases the window by one packet
every pipe size worth of ACKs. With delayed ACKs, this takes two
RTTs per increase. To exactly fill the pipe, the spacing of losses
must be no closer than when the peak of the AIMD sawtooth reached
exactly twice the target_window_size. Otherwise, the multiplicative
window reduction triggered by the loss would cause the network to be
underfilled. Per [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_window_size)*(2*target_window_size)
packets, which simplifies to:
target_run_length = 3*(target_window_size^2)
Note that this calculation is very conservative and is based on a
number of assumptions that may not apply. Appendix A discusses these
assumptions and provides some alternative models. If a different
model is used, an FSTIDS must document the actual method for
computing target_run_length and the ratio between alternate
target_run_length and the reference target_run_length calculated
above, along with a discussion of the rationale for the underlying
assumptions.
Most of the individual parameters for the tests in Section 8 are
derived from target_window_size and target_run_length.
5.3. Parameter Derating
Since some aspects of the models are very conservative, the MBM
framework permits some latitude in derating test parameters. Rather
than trying to formalize more complicated models, we permit some test
parameters to be relaxed as long as they meet some additional
procedural constraints:
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o The FSTIDS must document and justify the actual method used to
compute the derated metric parameters.
o The validation procedures described in Section 10 must be used to
demonstrate the feasibility of meeting the Target Transport
Performance with infrastructure that just barely passes the
derated tests.
o The validation process for an FSTIDS itself must be documented in
such a way that other researchers can duplicate the validation
experiments.
Except as noted, all tests below assume no derating. Tests for which
there is not currently a well-established model for the required
parameters explicitly include derating as a way to indicate
flexibility in the parameters.
5.4. Test Preconditions
Many tests have preconditions that are required to assure their
validity. Examples include the presence or non-presence of cross
traffic on specific subpaths; negotiating ECN; and a test stream
preamble of appropriate length to achieve stable access to network
resources in the presence of reactive network elements (as defined in
Section 1.1 of [RFC7312]). If preconditions are not properly
satisfied for some reason, the tests should be considered to be
inconclusive. In general, it is useful to preserve diagnostic
information as to why the preconditions were not met and any test
data that was collected even if it is not useful for the intended
test. Such diagnostic information and partial test data may be
useful for improving the test or test procedures themselves.
It is important to preserve the record that a test was scheduled;
otherwise, precondition enforcement mechanisms can introduce sampling
bias. For example, canceling tests due to cross traffic on
subscriber access links might introduce sampling bias in tests of the
rest of the network by reducing the number of tests during peak
network load.
Test preconditions and failure actions must be specified in an
FSTIDS.
6. Generating Test Streams
Many important properties of Model-Based Metrics, such as vantage
independence, are a consequence of using test streams that have
temporal structures that mimic TCP or other transport protocols
running over a complete path. As described in Section 4.1, self-
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clocked protocols naturally have burst structures related to the RTT
and pipe size of the complete path. These bursts naturally get
larger (contain more packets) as either the Target RTT or Target Data
Rate get larger or the Target MTU gets smaller. An implication of
these relationships is that test streams generated by running self-
clocked protocols over short subpaths may not adequately exercise the
queuing at any bottleneck to determine if the subpath can support the
full Target Transport Performance over the complete path.
Failing to authentically mimic TCP's temporal structure is part of
the reason why simple performance tools such as iPerf, netperf, nc,
etc., have the reputation for yielding false pass results over short
test paths, even when a subpath has a flaw.
The definitions in Section 3 are sufficient for most test streams.
We describe the slowstart and standing queue test streams in more
detail.
In conventional measurement practice, stochastic processes are used
to eliminate many unintended correlations and sample biases.
However, MBM tests are designed to explicitly mimic temporal
correlations caused by network or protocol elements themselves. Some
portions of these systems, such as traffic arrival (e.g., test
scheduling), are naturally stochastic. Other behaviors, such as
back-to-back packet transmissions, are dominated by implementation-
specific deterministic effects. Although these behaviors always
contain non-deterministic elements and might be modeled
stochastically, these details typically do not contribute
significantly to the overall system behavior. Furthermore, it is
known that real protocols are subject to failures caused by network
property estimators suffering from bias due to correlation in their
own traffic. For example, TCP's RTT estimator used to determine the
Retransmit Timeout (RTO), can be fooled by periodic cross traffic or
start-stop applications. For these reasons, many details of the test
streams are specified deterministically.
It may prove useful to introduce fine-grained noise sources into the
models used for generating test streams in an update of Model-Based
Metrics, but the complexity is not warranted at the time this
document was written.
6.1. Mimicking Slowstart
TCP slowstart has a two-level burst structure as shown in Figure 2.
The fine time structure is caused by efficiency algorithms that
deliberately batch work (CPU, channel allocation, etc.) to better
amortize certain network and host overheads. ACKs passing through
the return path typically cause the sender to transmit small bursts
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of data at the full sender interface rate. For example, TCP
Segmentation Offload (TSO) and Delayed Acknowledgment both contribute
to this effect. During slowstart, these bursts are at the same
headway as the returning ACKs but are typically twice as large (e.g.,
have twice as much data) as the ACK reported was delivered to the
receiver. Due to variations in delayed ACK and algorithms such as
Appropriate Byte Counting [RFC3465], different pairs of senders and
receivers produce slightly different burst patterns. Without loss of
generality, we assume each ACK causes four packet sender interface
rate bursts at an average headway equal to the ACK headway; this
corresponds to sending at an average rate equal to twice the
effective bottleneck IP rate. Each slowstart burst consists of a
series of four packet sender interface rate bursts such that the
total number of packets is the current window size (as of the last
packet in the burst).
The coarse time structure is due to each RTT being a reflection of
the prior RTT. For real transport protocols, each slowstart burst is
twice as large (twice the window) as the previous burst but is spread
out in time by the network bottleneck, such that each successive RTT
exhibits the same effective bottleneck IP rate. The slowstart phase
ends on the first lost packet or ECN mark, which is intended to
happen after successive slowstart bursts merge in time: the next
burst starts before the bottleneck queue is fully drained and the
prior burst is complete.
For the diagnostic tests described below, we preserve the fine time
structure but manipulate the coarse structure of the slowstart bursts
(burst size and headway) to measure the ability of the dominant
bottleneck to absorb and smooth slowstart bursts.
Note that a stream of repeated slowstart bursts has three different
average rates, depending on the averaging time interval. At the
finest timescale (a few packet times at the sender interface), the
peak of the average IP rate is the same as the sender interface rate;
at a medium timescale (a few ACK times at the dominant bottleneck),
the peak of the average IP rate is twice the implied bottleneck IP
capacity; and at timescales longer than the target_RTT and when the
burst size is equal to the target_window_size, the average rate is
equal to the target_data_rate. This pattern corresponds to repeating
the last RTT of TCP slowstart when delayed ACK and sender-side byte
counting are present but without the limits specified in Appropriate
Byte Counting [RFC3465].
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time ==> ( - equals one packet)
Fine time structure of the packet stream:
---- ---- ---- ---- ----
|<>| sender interface rate bursts (typically 3 or 4 packets)
|<===>| burst headway (from the ACK headway)
\____repeating sender______/
rate bursts
Coarse (RTT-level) time structure of the packet stream:
---- ---- ---- ---- ---- ---- ---- ...
|<========================>| slowstart burst size (from the window)
|<==============================================>| slowstart headway
(from the RTT)
\__________________________/ \_________ ...
one slowstart burst Repeated slowstart bursts
Figure 2: Multiple Levels of Slowstart Bursts
6.2. Constant Window Pseudo CBR
Pseudo constant bit rate (CBR) is implemented by running a standard
self-clocked protocol such as TCP with a fixed window size. If that
window size is test_window, the data rate will be slightly above the
target_rate.
Since the test_window is constrained to be an integer number of
packets, for small RTTs or low data rates, there may not be
sufficiently precise control over the data rate. Rounding the
test_window up (as defined above) is likely to result in data rates
that are higher than the target rate, but reducing the window by one
packet may result in data rates that are too small. Also, cross
traffic potentially raises the RTT, implicitly reducing the rate.
Cross traffic that raises the RTT nearly always makes the test more
strenuous (i.e., more demanding for the network path).
Note that Constant Window Pseudo CBR (and Scanned Window Pseudo CBR
in the next section) both rely on a self-clock that is at least
partially derived from the properties of the subnet under test. This
introduces the possibility that the subnet under test exhibits
behaviors such as extreme RTT fluctuations that prevent these
algorithms from accurately controlling data rates.
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An FSTIDS specifying a Constant Window Pseudo CBR test must
explicitly indicate under what conditions errors in the data rate
cause tests to be inconclusive. Conventional paced measurement
traffic may be more appropriate for these environments.
6.3. Scanned Window Pseudo CBR
Scanned Window Pseudo CBR is similar to the Constant Window Pseudo
CBR described above, except the window is scanned across a range of
sizes designed to include two key events: the onset of queuing and
the onset of packet loss or ECN CE marks. The window is scanned by
incrementing it by one packet every 2*target_window_size delivered
packets. This mimics the additive increase phase of standard Reno
TCP congestion avoidance when delayed ACKs are in effect. Normally,
the window increases are separated by intervals slightly longer than
twice the target_RTT.
There are two ways to implement this test: 1) applying a window clamp
to standard congestion control in a standard protocol such as TCP and
2) stiffening a non-standard transport protocol. When standard
congestion control is in effect, any losses or ECN CE marks cause the
transport to revert to a window smaller than the clamp, such that the
scanning clamp loses control of the window size. The NPAD (Network
Path and Application Diagnostics) 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 CE marks.
Such a stiffened transport explicitly violates mandatory Internet
congestion control [RFC5681] and is not suitable for in situ testing.
It is only appropriate for engineering testing under laboratory
conditions. The Windowed Ping tool implements such a test [WPING].
This tool has been updated (see [mpingSource]).
The test procedures in Section 8.2 describe how to the partition the
scans into regions and how to interpret the results.
6.4. Concurrent or Channelized Testing
The procedures described in this document are only directly
applicable to single-stream measurement, e.g., one TCP connection or
measurement stream. In an ideal world, we would disallow all
performance claims based on multiple concurrent streams, but this is
not practical due to at least two issues. First, many very high-rate
link technologies are channelized and at last partially pin the flow-
to-channel mapping to minimize packet reordering within flows.
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Second, TCP itself has scaling limits. Although the former problem
might be overcome through different design decisions, the latter
problem is more deeply rooted.
All congestion control algorithms that are philosophically aligned
with [RFC5681] (e.g., claim some level of TCP compatibility,
friendliness, or fairness) have scaling limits; that is, as a long
fat network (LFN) with a fixed RTT and MTU gets faster, these
congestion control algorithms get less accurate and, as a
consequence, have difficulty filling the network [CCscaling]. These
properties are a consequence of the original Reno AIMD congestion
control design and the requirement in [RFC5681] that all transport
protocols have similar responses to congestion.
There are a number of reasons to want to specify performance in terms
of multiple concurrent flows; however, this approach is not
recommended for data rates below several megabits per second, which
can be attained with run lengths under 10000 packets on many paths.
Since the required run length is proportional to the square of the
data rate, at higher rates, the run lengths can be unreasonably
large, and multiple flows might be the only feasible approach.
If multiple flows are deemed necessary to meet aggregate performance
targets, then this must be stated both in the design of the TIDS and
in any claims about network performance. The IP diagnostic tests
must be performed concurrently with the specified number of
connections. For the tests that use bursty test streams, the bursts
should be synchronized across streams unless there is a priori
knowledge that the applications have some explicit mechanism to
stagger their own bursts. In the absence of an explicit mechanism to
stagger bursts, many network and application artifacts will sometimes
implicitly synchronize bursts. A test that does not control burst
synchronization may be prone to false pass results for some
applications.
7. Interpreting the Results
7.1. Test Outcomes
To perform an exhaustive test of a complete network path, each test
of the TIDS is applied to each subpath of the complete path. If any
subpath fails any test, then a standard transport protocol running
over the complete path can also be expected to fail to attain the
Target Transport Performance under some conditions.
In addition to passing or failing, a test can be deemed to be
inconclusive for a number of reasons. Proper instrumentation and
treatment of inconclusive outcomes is critical to the accuracy and
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robustness of Model-Based Metrics. Tests can be inconclusive if the
precomputed traffic pattern or data rates were not accurately
generated; the measurement results were not statistically
significant; the required preconditions for the test were not met; or
other causes. See Section 5.4.
For example, consider a test that implements Constant Window Pseudo
CBR (Section 6.2) by adding rate controls and detailed IP packet
transfer instrumentation to TCP (e.g., using the extended performance
statistics for TCP as described in [RFC4898]). TCP includes built-in
control systems that might interfere with the sending data rate. If
such a test meets the required packet transfer statistics (e.g., run
length) while failing to attain the specified data rate, it must be
treated as an inconclusive result, because we cannot a priori
determine if the reduced data rate was caused by a TCP problem or a
network problem or if the reduced data rate had a material effect on
the observed packet transfer statistics.
Note that for capacity tests, if the observed packet transfer
statistics meet the statistical criteria for failing (based on
acceptance of hypothesis H1 in Section 7.2), 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 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 stream does not depend on network
conditions or IP packets received. Any mechanism that introduces
feedback between the path's measurements and the test stream
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.
Inconclusive tests may be caused by situations in which a test
outcome is ambiguous because of network limitations or an unknown
limitation on the IP diagnostic test itself, which may have been
caused by some uncontrolled feedback from the network.
Note that procedures that attempt to search the target parameter
space to find the limits on a parameter such as target_data_rate are
at risk of breaking the location-independent properties of Model-
Based Metrics if any part of the boundary between passing,
inconclusive, or failing results is sensitive to RTT (which is
normally the case). For example, the maximum data rate for a
marginal link (e.g., exhibiting excess errors) is likely to be
sensitive to the test_path_RTT. The maximum observed data rate over
the test path has very little value for predicting the maximum rate
over a different path.
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One of the goals for evolving TIDS designs will be to keep sharpening
the distinctions between inconclusive, passing, and failing tests.
The criteria for inconclusive, passing, and failing tests must be
explicitly stated for every test in the TIDS or FSTIDS.
One of the goals for 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 packet transfer statistics and ancillary
metrics [RFC3148] for deeper study of the behavior of the network
path and to measure the tools themselves. Raw packet transfer
statistics can help to drive tool evolution. Under some conditions,
it might be possible to re-evaluate the raw data for satisfying
alternate Target Transport Performance. However, it is important to
guard against sampling bias and other implicit feedback that can
cause false results and exhibit measurement point vantage
sensitivity. Simply applying different delivery criteria based on a
different Target Transport Performance is insufficient if the test
traffic patterns (bursts, etc.) do not match the alternate Target
Transport Performance.
7.2. Statistical Criteria for Estimating run_length
When evaluating the observed run_length, we need to determine
appropriate packet stream sizes and acceptable error levels for
efficient measurement. In practice, can we compare the empirically
estimated packet loss and ECN CE marking ratios with the targets as
the sample size grows? How large a sample is needed to say that the
measurements of packet transfer indicate a particular run length is
present?
The generalized measurement can be described as recursive testing:
send packets (individually or in patterns) and observe the packet
transfer performance (packet loss ratio, other metric, or 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 CE marks
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, one mark per target_run_length,
where a "mark" is defined as a lost packet, a packet with ECN CE
mark, or other signal. This constitutes the null hypothesis:
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H0: no more than one mark in target_run_length =
3*(target_window_size)^2 packets
We can stop sending packets if ongoing measurements support accepting
H0 with the specified Type I error = alpha (= 0.05, for example).
We also have an alternative hypothesis to evaluate: is performance
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 this document; 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) [Wald45] [Montgomery90]. Note that as originally
framed, the events under consideration were all manufacturing
defects. In networking, ECN CE 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
hypotheses specified as above:
H0: p0 = one defect in target_run_length
H1: p1 = one defect in target_run_length/4
As packets are sent and measurements collected, the tester evaluates
the cumulative defect count against two boundaries representing H0
Acceptance or Rejection (and acceptance of H1):
Acceptance line: Xa = -h1 + s*n
Rejection line: Xr = h2 + s*n
where n increases linearly for each packet sent and
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h1 = { log((1-alpha)/beta) }/k
h2 = { log((1-beta)/alpha) }/k
k = log{ (p1(1-p0)) / (p0(1-p1)) }
s = [ log{ (1-p0)/(1-p1) } ]/k
for p0 and p1 as defined in the null and alternative hypotheses
statements above, and alpha and beta as the Type I and Type II
errors.
The SPRT specifies simple stopping rules:
o Xa < defect_count(n) < Xr: continue testing
o defect_count(n) <= Xa: Accept H0
o defect_count(n) >= Xr: Accept H1
The calculations above are implemented in the R-tool for Statistical
Analysis [Rtool], in the add-on package for Cross-Validation via
Sequential Testing (CVST) [CVST].
Using the equations above, we can calculate the minimum number of
packets (n) needed to accept H0 when x defects are observed. For
example, when x = 0:
Xa = 0 = -h1 + s*n
and n = h1 / s
Note that the derivations in [Wald45] and [Montgomery90] differ.
Montgomery's simplified derivation of SPRT may assume a Bernoulli
processes, where the packet loss probabilities are independent and
identically distributed, making the SPRT more accessible. Wald's
seminal paper showed that this assumption is not necessary. It helps
to remember that the goal of SPRT is not to estimate the value of the
packet loss rate but only whether or not the packet loss ratio is
likely (1) low enough (when we accept the H0 null hypothesis),
yielding success or (2) too high (when we accept the H1 alternate
hypothesis), yielding failure.
7.3. 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
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make protocols and applications more tolerant to reordering (for
example, see [RFC5827]), in response to the gradual increase in
reordering in the network. This increase has been due to the
deployment of technologies such as multithreaded routing lookups and
Equal-Cost Multipath (ECMP) routing. These techniques increase
parallelism in the network and are critical to enabling overall
Internet growth to exceed Moore's Law.
With transport retransmission strategies, there are fundamental
trade-offs among reordering tolerance, how quickly losses can be
repaired, and overhead from spurious retransmissions. In advance of
new retransmission strategies, we propose the following strawman:
transport protocols should be able to adapt to reordering as long as
the reordering extent is not more than the maximum of one quarter
window or 1 ms, whichever is larger. (These values come from
experience prototyping Early Retransmit [RFC5827] and related
algorithms. They agree with the values being proposed for "RACK: a
time-based fast loss detection algorithm" [RACK].) Within this limit
on reorder extent, there should be no bound on reordering density.
By implication, recording that is less than these bounds should not
be treated as a network impairment. However, [RFC4737] still
applies: reordering should be instrumented, and the maximum
reordering that can be properly characterized by the test (because of
the bound on history buffers) should be recorded with the measurement
results.
Reordering tolerance and diagnostic limitations, such as the size of
the history buffer used to diagnose packets that are way out of
order, must be specified in an FSTIDS.
8. IP Diagnostic Tests
The IP diagnostic tests below are organized according to the
technique used to generate the test stream as described in Section 6.
All of the results are evaluated in accordance with Section 7,
possibly with additional test-specific criteria.
We also introduce some combined tests that are more efficient when
networks are expected to pass but conflate diagnostic signatures when
they fail.
8.1. Basic Data Rate and Packet Transfer Tests
We propose several versions of the basic data rate and packet
transfer statistics test that differ in how the data rate is
controlled. The data can be paced on a timer or window controlled
(and self-clocked). The first two tests implicitly confirm that
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sub_path has sufficient raw capacity to carry the target_data_rate.
They are recommended for relatively infrequent testing, such as an
installation or periodic auditing process. The third test,
Background Packet Transfer Statistics, is a low-rate test designed
for ongoing monitoring for changes in subpath quality.
8.1.1. Delivery Statistics at Paced Full Data Rate
This test confirms 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 Section 6.1 with a burst
size of 1 (single packets) or 2 (packet pairs).
The test is considered to be inconclusive if the packet transmission
cannot be accurately controlled for any reason.
RFC 6673 [RFC6673] is appropriate for measuring packet transfer
statistics at full data rate.
8.1.2. Delivery Statistics at Full Data Windowed Rate
This test confirms that the observed run length is at least the
target_run_length while sending at an average rate approximately
equal to the target_data_rate, by controlling (or clamping) the
window size of a conventional transport protocol to test_window.
Since losses and ECN CE marks cause transport protocols to reduce
their data rates, this test is expected to be less precise about
controlling its data rate. It should not be considered inconclusive
as long as at least some of the round trips reached the full
target_data_rate without incurring losses or ECN CE marks. To pass
this test, the network must deliver target_window_size packets in
target_RTT time without any losses or ECN CE marks at least once per
two target_window_size round trips, in addition to meeting the run
length statistical test.
8.1.3. Background Packet Transfer Statistics Tests
The Background Packet Transfer Statistics Test is a low-rate version
of the target rate test above, designed for ongoing lightweight
monitoring for changes in the observed subpath run length without
disrupting users. It should be used in conjunction with one of the
above full-rate tests because it does not confirm that the subpath
can support raw data rate.
RFC 6673 [RFC6673] is appropriate for measuring background packet
transfer statistics.
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8.2. Standing Queue Tests
These engineering tests confirm that the bottleneck is well behaved
across the onset of packet loss, which typically follows after the
onset of queuing. 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 or ECN CE marks to signal to
the transport protocol that it should reduce its window or data rate.
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 not have an appropriate AQM [RFC7567] and, as a
consequence, be subject to bufferbloat [wikiBloat]. Queues without
AQM have the potential to inflict excess delays on all flows sharing
the bottleneck. Excess losses (more than half of the window) at the
onset of loss make loss recovery problematic for the transport
protocol. Non-linear, erratic, or excessive RTT increases suggest
poor interactions between the channel acquisition algorithms and the
transport self-clock. All of the tests in this section use the same
basic scanning algorithm, described here, but score the link or
subpath on the basis of how well it avoids each of these problems.
Some network technologies rely on virtual queues or other techniques
to meter traffic without adding any queuing delay, in which case the
data rate will vary with the window size all the way up to the onset
of load-induced packet loss or ECN CE marks. For these technologies,
the discussion of queuing in Section 6.3 does not apply, but it is
still necessary to confirm that the onset of losses or ECN CE marks
be at an appropriate point and progressive. If the network
bottleneck does not introduce significant queuing delay, modify the
procedure described in Section 6.3 to start the scan at a window
equal to or slightly smaller than the test_window.
Use the procedure in Section 6.3 to sweep the window across the onset
of queuing 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_window_size
packets delivered. A scan can typically be divided into three
regions: below the onset of queuing, a standing queue, and at or
beyond the onset of loss.
Below the onset of queuing, the RTT is typically fairly constant, and
the data rate varies in proportion to the window size. Once the data
rate reaches the subpath IP rate, the data rate becomes fairly
constant, and the RTT increases in proportion to the increase in
window size. The precise transition across the start of queuing can
be identified by the maximum network power, defined to be the ratio
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data rate over the RTT. The network power can be computed at each
window size, and the window with the maximum is taken as the start of
the queuing region.
If there is random background loss (e.g., bit errors), precise
determination of the onset of queue-induced packet loss may require
multiple scans. At window sizes large enough to cause loss in
queues, all transport protocols are expected to experience periodic
losses determined by the interaction between the congestion control
and AQM algorithms. For standard congestion control algorithms, the
periodic losses are likely to be relatively widely spaced, and the
details are typically dominated by the behavior of the transport
protocol itself. For the case of stiffened transport protocols (with
non-standard, aggressive congestion control algorithms), the details
of periodic losses will be dominated by how the window increase
function responds to loss.
8.2.1. Congestion Avoidance
A subpath passes the congestion avoidance standing queue test if more
than target_run_length packets are delivered between the onset of
queuing (as determined by the window with the maximum network power
as described above) and the first loss or ECN CE mark. If this test
is implemented using a standard congestion control algorithm with a
clamp, it can be performed in situ in the production internet as a
capacity test. For an example of such a test, see [Pathdiag].
For technologies that do not have conventional queues, use the
test_window in place of the onset of queuing. That is, a subpath
passes the congestion avoidance standing queue test if more than
target_run_length packets are delivered between the start of the scan
at test_window and the first loss or ECN CE mark.
8.2.2. 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 transport capacity;
however, if there is no mechanism to limit buffer queue occupancy,
then a single stream with sufficient data to deliver is likely to
cause the problems described in [RFC7567] and [wikiBloat]. This may
cause only minor symptoms for the dominant flow but has the potential
to make the subpath unusable for other flows and applications.
The test will pass if the onset of loss occurs before a standing
queue has introduced delay greater than twice the target_RTT or
another well-defined and specified limit. Note that there is not yet
a model for how much standing queue is acceptable. The factor of two
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chosen here reflects a rule of thumb. In conjunction with the
previous test, this test implies that the first loss should occur at
a queuing delay that is between one and two times the target_RTT.
Specified RTT limits that are larger than twice the target_RTT must
be fully justified in the FSTIDS.
8.2.3. Non-excessive Loss
This test confirms that the onset of loss is not excessive. The test
will pass if losses are equal to or less than the increase in the
cross traffic plus the test stream window increase since the previous
RTT. This could be restated as non-decreasing total throughput of
the subpath at the onset of loss. (Note that when there is a
transient drop in subpath throughput and there is not already a
standing queue, a subpath that passes other queue tests in this
document will have sufficient queue space to hold one full RTT worth
of data).
Note that token bucket policers will not pass this test, which is as
intended. TCP often stumbles badly if more than a small fraction of
the packets are dropped in one RTT. Many TCP implementations will
require a timeout and slowstart to recover their self-clock. Even if
they can recover from the massive losses, the sudden change in
available capacity at the bottleneck wastes serving and front-path
capacity until TCP can adapt to the new rate [Policing].
8.2.4. Duplex Self-Interference
This engineering test confirms a bound on the interactions between
the forward data path and the ACK return path when they share a half-
duplex link.
Some historical half-duplex technologies had the property that each
direction held the channel until it completely drained its queue.
When a self-clocked transport protocol, such as TCP, has data and
ACKs passing in opposite directions through such a link, the behavior
often reverts to stop-and-wait. Each additional packet added to the
window raises the observed RTT by two packet times, once as the
additional packet passes through the data path and once for the
additional delay incurred by the ACK waiting on the return path.
The Duplex Self-Interference Test fails if the RTT rises by more than
a fixed bound above the expected queuing time computed from the
excess window divided by the subpath IP capacity. This bound must be
smaller than target_RTT/2 to avoid reverting to stop-and-wait
behavior (e.g., data packets and ACKs both have to be released at
least twice per RTT).
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8.3. Slowstart Tests
These tests mimic slowstart: data is sent at twice the effective
bottleneck rate to exercise the queue at the dominant bottleneck.
8.3.1. Full Window Slowstart Test
This capacity test confirms that slowstart is not likely to exit
prematurely. To perform this test, send slowstart bursts that are
target_window_size total packets and accumulate packet transfer
statistics as described in Section 7.2 to score the outcome. The
test will pass if it is statistically significant that the observed
number of good packets delivered between losses or ECN CE marks is
larger than the target_run_length. The test will fail if it is
statistically significant that the observed interval between losses
or ECN CE marks is smaller than the target_run_length.
The test is deemed inconclusive if the elapsed time to send the data
burst is not less than half of the time to receive the ACKs. (That
is, it is acceptable to send data too fast, but sending it slower
than twice the actual bottleneck rate as indicated by the ACKs is
deemed inconclusive). The headway for the slowstart bursts should be
the target_RTT.
Note that these are the same parameters that are used for the
Sustained Full-Rate Bursts Test, except the burst rate is at
slowstart rate rather than sender interface rate.
8.3.2. Slowstart AQM Test
To perform this test, do a continuous slowstart (send data
continuously at twice the implied IP bottleneck capacity) until the
first loss; stop and allow the network to drain and repeat; gather
statistics on how many packets were delivered before the loss, the
pattern of losses, maximum observed RTT, and window size; and justify
the results. There is not currently sufficient theory to justify
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 vs. elephants" problem). The
queue sojourn time for the first packet delivered after the first
loss should be at least one half of the target_RTT.
This engineering test should be performed on a quiescent network or
testbed, since cross traffic has the potential to change the results
in ill-defined ways.
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8.4. Sender Rate Burst Tests
These tests determine how well the network can deliver bursts sent at
the sender's interface rate. Note that this test most heavily
exercises the front path and is likely to include infrastructure that
may be out of scope for an access ISP, even though the bursts might
be caused by ACK compression, thinning, or channel arbitration in the
access ISP. See Appendix B.
Also, there are a several details about sender interface rate bursts
that are not fully defined here. These details, such as the assumed
sender interface rate, should be explicitly stated in an FSTIDS.
Current standards permit TCP to send full window bursts following an
application pause. (Congestion Window Validation [RFC2861] and
updates to support Rate-Limited Traffic [RFC7661] are not required).
Since full window bursts are consistent with standard behavior, it is
desirable that the network be able to deliver such bursts; otherwise,
application pauses will cause unwarranted losses. Note that the AIMD
sawtooth requires a peak window that is twice target_window_size, so
the worst-case burst may be 2*target_window_size.
It is also understood in the application and serving community that
interface rate bursts have a cost to the network that has to be
balanced against other costs in the servers themselves. For example,
TCP Segmentation Offload (TSO) reduces server CPU in exchange for
larger network bursts, which increase the stress on network buffer
memory. Some newer TCP implementations can pace traffic at scale
[TSO_pacing] [TSO_fq_pacing]. It remains to be determined if and how
quickly these changes will be deployed.
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 many server rate bursts should be tolerated by
the network. Some bursts must be tolerated by the network, but it is
probably unreasonable to expect the network to be able to efficiently
deliver all data as a series of bursts.
For this reason, this is the only test for which we encourage
derating. A TIDS could include a table containing pairs of derating
parameters: burst sizes and how much each burst size is permitted to
reduce the run length, relative to the target_run_length.
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8.5. Combined and Implicit Tests
Combined tests efficiently confirm multiple network properties in a
single test, possibly as a side effect of normal content delivery.
They require less measurement traffic than other testing strategies
at the cost of conflating diagnostic signatures when they fail.
These are by far the most efficient for monitoring networks that are
nominally expected to pass all tests.
8.5.1. Sustained Full-Rate Bursts Test
The Sustained Full-Rate Bursts Test implements a combined worst-case
version of all of the capacity tests above. To perform this test,
send target_window_size bursts of packets at server interface rate
with target_RTT burst headway (burst start to next burst start), and
verify that the observed packet transfer statistics meets the
target_run_length.
Key observations:
o The subpath under test is expected to go idle for some fraction of
the time, determined by the difference between the time to drain
the queue at the subpath_IP_capacity and the target_RTT. If the
queue does not drain completely, it may be an indication that the
subpath has insufficient IP capacity or that there is some other
problem with the test (e.g., it is inconclusive).
o The burst sensitivity can be derated by sending smaller bursts
more frequently (e.g., by sending target_window_size*derate packet
bursts every target_RTT*derate, where "derate" is less than one).
o When not derated, this test is the most strenuous capacity test.
o A subpath that passes this test is likely to be able to sustain
higher rates (close to subpath_IP_capacity) for paths with RTTs
significantly smaller than the target_RTT.
o This test can be implemented with instrumented TCP [RFC4898],
using a specialized measurement application at one end (e.g.,
[MBMSource]) and a minimal service at the other end (e.g.,
[RFC863] and [RFC864]).
o This test is efficient to implement, since it does not require
per-packet timers, and can make use of TSO in modern network
interfaces.
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o If a subpath is known to pass the standing queue engineering tests
(particularly that it has a progressive onset of loss at an
appropriate queue depth), then the Sustained Full-Rate Bursts Test
is sufficient to assure that the subpath under test will not
impair Bulk Transport Capacity at the target performance under all
conditions. See Section 8.2 for a discussion of the standing
queue tests.
Note that this test is clearly independent of the subpath RTT or
other details of the measurement infrastructure, as long as the
measurement infrastructure can accurately and reliably deliver the
required bursts to the subpath under test.
8.5.2. Passive Measurements
Any non-throughput-maximizing application, such as fixed-rate
streaming media, can be used to implement passive or hybrid (defined
in [RFC7799]) versions of Model-Based Metrics with some additional
instrumentation and possibly a traffic shaper or other controls in
the servers. The essential requirement is that the data transmission
be constrained such that even with arbitrary application pauses and
bursts, the data rate and burst sizes stay within the envelope
defined by the individual tests described above.
If the application's serving data rate can be constrained to be 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 (which is set to the test_window
and computed for the actual serving path).
Under the above constraints, the serving_window_clamp will limit both
the serving data rate and burst sizes to be no larger than the
parameters specified by the procedures in Section 8.1.2, 8.4, or
8.5.1. 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 8.4, and the sender rate burst
sizes are implicitly derated by the serving_window_clamp divided by
the target_window_size at the very least. (Depending on the
application behavior, the data might be significantly smoother than
specified by any of the burst tests.)
In an alternative implementation, the data rate and bursts might be
explicitly controlled by a programmable traffic shaper or by pacing
at the sender. This would provide better control over transmissions
but is more complicated to implement, although the required
technology is available [TSO_pacing] [TSO_fq_pacing].
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Note that these techniques can be applied to any content delivery
that can be operated at a constrained data rate to inhibit TCP
equilibrium behavior.
Furthermore, note that Dynamic Adaptive Streaming over HTTP (DASH) is
generally in conflict with passive Model-Based Metrics measurement,
because it is a rate-maximizing protocol. It can still meet the
requirement here if the rate can be capped, for example, by knowing a
priori the maximum rate needed to deliver a particular piece of
content.
9. Example
In this section, we illustrate a TIDS designed to confirm that an
access ISP can reliably deliver HD video from multiple content
providers to all of its customers. With modern codecs, minimal HD
video (720p) generally fits in 2.5 Mb/s. Due to the ISP's
geographical size, network topology, and modem characteristics, the
ISP determines that most content is within a 50 ms RTT of its users.
(This example RTT is sufficient to cover the propagation delay to
continental Europe or to either coast of the United States with low-
delay modems; it is sufficient to cover somewhat smaller geographical
regions if the modems require additional delay to implement advanced
compression and error recovery.)
+----------------------+-------+---------+
| End-to-End Parameter | value | units |
+----------------------+-------+---------+
| target_rate | 2.5 | Mb/s |
| target_RTT | 50 | ms |
| target_MTU | 1500 | bytes |
| header_overhead | 64 | bytes |
| | | |
| target_window_size | 11 | packets |
| target_run_length | 363 | packets |
+----------------------+-------+---------+
Table 1: 2.5 Mb/s over a 50 ms Path
Table 1 shows the default TCP model with no derating and, as such, is
quite conservative. The simplest TIDS would be to use the Sustained
Full-Rate Bursts Test, described in Section 8.5.1. Such a test would
send 11 packet bursts every 50 ms and confirm that there was no more
than 1 packet loss per 33 bursts (363 total packets in 1.650
seconds).
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Since this number represents the entire end-to-end loss budget,
independent subpath tests could be implemented by apportioning the
packet loss ratio across subpaths. For example, 50% of the losses
might be allocated to the access or last mile link to the user, 40%
to the network interconnections with other ISPs, and 1% to each
internal hop (assuming no more than 10 internal hops). Then, all of
the subpaths can be tested independently, and the spatial composition
of passing subpaths would be expected to be within the end-to-end
loss budget.
9.1. Observations about Applicability
Guidance on deploying and using MBM belong in a future document.
However, the example above illustrates some of the issues that may
need to be considered.
Note that another ISP, with different geographical coverage,
topology, or modem technology may need to assume a different
target_RTT and, as a consequence, a different target_window_size and
target_run_length, even for the same target_data rate. One of the
implications of this is that infrastructure shared by multiple ISPs,
such as Internet Exchange Points (IXPs) and other interconnects may
need to be evaluated on the basis of the most stringent
target_window_size and target_run_length of any participating ISP.
One way to do this might be to choose target parameters for
evaluating such shared infrastructure on the basis of a hypothetical
reference path that does not necessarily match any actual paths.
Testing interconnects has generally been problematic: conventional
performance tests run between measurement points adjacent to either
side of the interconnect are not generally useful. Unconstrained TCP
tests, such as iPerf [iPerf], are usually overly aggressive due to
the small RTT (often less than 1 ms). With a short RTT, these tools
are likely to report inflated data rates because on a short RTT,
these tools can tolerate very high packet loss ratios and can push
other cross traffic off of the network. As a consequence, these
measurements are useless for predicting actual user performance over
longer paths and may themselves be quite disruptive. Model-Based
Metrics solves this problem. The interconnect can be evaluated with
the same TIDS as other subpaths. Continuing our example, if the
interconnect is apportioned 40% of the losses, 11 packet bursts sent
every 50 ms should have fewer than one loss per 82 bursts (902
packets).
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10. Validation
Since some aspects of the models are likely to be too conservative,
Section 5.2 permits alternate protocol models, and Section 5.3
permits test parameter derating. If either of these techniques is
used, we require demonstrations that such a TIDS can robustly detect
subpaths that will prevent authentic applications using state-of-the-
art protocol implementations from meeting the specified Target
Transport Performance. This correctness criteria is potentially
difficult to prove, because it implicitly requires validating a TIDS
against all possible paths and subpaths. The procedures described
here are still experimental.
We suggest two approaches, both of which should be applied. First,
publish a fully open description of the TIDS, including what
assumptions were used and how it was derived, such that the research
community can evaluate the design decisions, test them, and comment
on their applicability. Second, demonstrate that applications do
meet the Target Transport Performance when running over a network
testbed that has the tightest possible constraints that still allow
the tests in the TIDS to pass.
This procedure resembles an epsilon-delta proof in calculus.
Construct a test network such that all of the individual tests of the
TIDS pass by only small (infinitesimal) margins, and demonstrate that
a variety of authentic applications running over real TCP
implementations (or other protocols as appropriate) meets the Target
Transport Performance over such a network. The workloads should
include multiple types of streaming media and transaction-oriented
short flows (e.g., synthetic web traffic).
For example, for the HD streaming video TIDS described in Section 9,
the IP capacity should be exactly the header_overhead above 2.5 Mb/s,
the per packet random background loss ratio should be 1/363 (for a
run length of 363 packets), the bottleneck queue should be 11
packets, and the front path should have just enough buffering to
withstand 11 packet interface rate bursts. We want every one of the
TIDS tests to fail if we slightly increase the relevant test
parameter, so, for example, sending a 12-packet burst should cause
excess (possibly deterministic) packet drops at the dominant queue at
the bottleneck. This network has the tightest possible constraints
that can be expected to pass the TIDS, yet it should be possible for
a real application using a stock TCP implementation in the vendor's
default configuration to attain 2.5 Mb/s over a 50 ms path.
The most difficult part of setting up such a testbed is arranging for
it to have the tightest possible constraints that still allow it to
pass the individual tests. Two approaches are suggested:
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o constraining (configuring) the network devices not to use all
available resources (e.g., by limiting available buffer space or
data rate)
o pre-loading subpaths with cross traffic
Note that it is important that a single tightly constrained
environment just barely 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 (e.g., queue space). This effect is potentially
bidirectional: extra latitude in the queue space tests has the
potential to enable TCP to compensate for insufficient data-rate
headroom.
To the extent that a TIDS is used to inform public dialog, it should
be fully documented publicly, including the details of the tests,
what assumptions were used, and how it was derived. All of the
details of the validation experiment should also be published with
sufficient detail for the experiments to be replicated by other
researchers. All components should be either open source or fully
described proprietary implementations that are available to the
research community.
11. Security Considerations
Measurement is often used to inform business and policy decisions
and, as a consequence, is potentially subject to manipulation.
Model-Based Metrics are expected to be a huge step forward because
equivalent measurements can be performed from multiple vantage
points, such that performance claims can be independently validated
by multiple parties.
Much of the acrimony in the Net Neutrality debate is due to the
historical lack of any effective vantage-independent tools to
characterize network performance. Traditional methods for measuring
Bulk Transport Capacity are sensitive to RTT and as a consequence
often yield very different results when run local to an ISP or
interconnect and when run over a customer's complete path. Neither
the ISP nor customer can repeat the other's measurements, leading to
high levels of distrust and acrimony. Model-Based Metrics are
expected to greatly improve this situation.
Note that in situ measurements sometimes require sending synthetic
measurement traffic between arbitrary locations in the network and,
as such, are potentially attractive platforms for launching DDoS
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attacks. All active measurement tools and protocols must be designed
to minimize the opportunities for these misuses. See the discussion
in Section 7 of [RFC7594].
Some of the tests described in this document are not intended for
frequent network monitoring since they have the potential to cause
high network loads and might adversely affect other traffic.
This document only describes a framework for designing a Fully
Specified Targeted IP Diagnostic Suite. Each FSTIDS must include its
own security section.
12. IANA Considerations
This document has no IANA actions.
13. Informative References
[RFC863] Postel, J., "Discard Protocol", STD 21, RFC 863,
DOI 10.17487/RFC0863, May 1983,
<
https://www.rfc-editor.org/info/rfc863>.
[RFC864] Postel, J., "Character Generator Protocol", STD 22,
RFC 864, DOI 10.17487/RFC0864, May 1983,
<
https://www.rfc-editor.org/info/rfc864>.
[RFC2330] Paxson, V., Almes, G., Mahdavi, J., and M. Mathis,
"Framework for IP Performance Metrics", RFC 2330,
DOI 10.17487/RFC2330, May 1998,
<
https://www.rfc-editor.org/info/rfc2330>.
[RFC2861] Handley, M., Padhye, J., and S. Floyd, "TCP Congestion
Window Validation", RFC 2861, DOI 10.17487/RFC2861, June
2000, <
https://www.rfc-editor.org/info/rfc2861>.
[RFC3148] Mathis, M. and M. Allman, "A Framework for Defining
Empirical Bulk Transfer Capacity Metrics", RFC 3148,
DOI 10.17487/RFC3148, July 2001,
<
https://www.rfc-editor.org/info/rfc3148>.
[RFC3168] Ramakrishnan, K., Floyd, S., and D. Black, "The Addition
of Explicit Congestion Notification (ECN) to IP",
RFC 3168, DOI 10.17487/RFC3168, September 2001,
<
https://www.rfc-editor.org/info/rfc3168>.
[RFC3465] Allman, M., "TCP Congestion Control with Appropriate Byte
Counting (ABC)", RFC 3465, DOI 10.17487/RFC3465, February
2003, <
https://www.rfc-editor.org/info/rfc3465>.
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[RFC4737] Morton, A., Ciavattone, L., Ramachandran, G., Shalunov,
S., and J. Perser, "Packet Reordering Metrics", RFC 4737,
DOI 10.17487/RFC4737, November 2006,
<
https://www.rfc-editor.org/info/rfc4737>.
[RFC4898] Mathis, M., Heffner, J., and R. Raghunarayan, "TCP
Extended Statistics MIB", RFC 4898, DOI 10.17487/RFC4898,
May 2007, <
https://www.rfc-editor.org/info/rfc4898>.
[RFC5136] Chimento, P. and J. Ishac, "Defining Network Capacity",
RFC 5136, DOI 10.17487/RFC5136, February 2008,
<
https://www.rfc-editor.org/info/rfc5136>.
[RFC5681] Allman, M., Paxson, V., and E. Blanton, "TCP Congestion
Control", RFC 5681, DOI 10.17487/RFC5681, September 2009,
<
https://www.rfc-editor.org/info/rfc5681>.
[RFC5827] Allman, M., Avrachenkov, K., Ayesta, U., Blanton, J., and
P. Hurtig, "Early Retransmit for TCP and Stream Control
Transmission Protocol (SCTP)", RFC 5827,
DOI 10.17487/RFC5827, May 2010,
<
https://www.rfc-editor.org/info/rfc5827>.
[RFC5835] Morton, A., Ed. and S. Van den Berghe, Ed., "Framework for
Metric Composition", RFC 5835, DOI 10.17487/RFC5835, April
2010, <
https://www.rfc-editor.org/info/rfc5835>.
[RFC6049] Morton, A. and E. Stephan, "Spatial Composition of
Metrics", RFC 6049, DOI 10.17487/RFC6049, January 2011,
<
https://www.rfc-editor.org/info/rfc6049>.
[RFC6576] Geib, R., Ed., Morton, A., Fardid, R., and A. Steinmitz,
"IP Performance Metrics (IPPM) Standard Advancement
Testing", BCP 176, RFC 6576, DOI 10.17487/RFC6576, March
2012, <
https://www.rfc-editor.org/info/rfc6576>.
[RFC6673] Morton, A., "Round-Trip Packet Loss Metrics", RFC 6673,
DOI 10.17487/RFC6673, August 2012,
<
https://www.rfc-editor.org/info/rfc6673>.
[RFC6928] Chu, J., Dukkipati, N., Cheng, Y., and M. Mathis,
"Increasing TCP's Initial Window", RFC 6928,
DOI 10.17487/RFC6928, April 2013,
<
https://www.rfc-editor.org/info/rfc6928>.
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[RFC7312] Fabini, J. and A. Morton, "Advanced Stream and Sampling
Framework for IP Performance Metrics (IPPM)", RFC 7312,
DOI 10.17487/RFC7312, August 2014,
<
https://www.rfc-editor.org/info/rfc7312>.
[RFC7398] Bagnulo, M., Burbridge, T., Crawford, S., Eardley, P., and
A. Morton, "A Reference Path and Measurement Points for
Large-Scale Measurement of Broadband Performance",
RFC 7398, DOI 10.17487/RFC7398, February 2015,
<
https://www.rfc-editor.org/info/rfc7398>.
[RFC7567] Baker, F., Ed. and G. Fairhurst, Ed., "IETF
Recommendations Regarding Active Queue Management",
BCP 197, RFC 7567, DOI 10.17487/RFC7567, July 2015,
<
https://www.rfc-editor.org/info/rfc7567>.
[RFC7594] Eardley, P., Morton, A., Bagnulo, M., Burbridge, T.,
Aitken, P., and A. Akhter, "A Framework for Large-Scale
Measurement of Broadband Performance (LMAP)", RFC 7594,
DOI 10.17487/RFC7594, September 2015,
<
https://www.rfc-editor.org/info/rfc7594>.
[RFC7661] Fairhurst, G., Sathiaseelan, A., and R. Secchi, "Updating
TCP to Support Rate-Limited Traffic", RFC 7661,
DOI 10.17487/RFC7661, October 2015,
<
https://www.rfc-editor.org/info/rfc7661>.
[RFC7680] Almes, G., Kalidindi, S., Zekauskas, M., and A. Morton,
Ed., "A One-Way Loss Metric for IP Performance Metrics
(IPPM)", STD 82, RFC 7680, DOI 10.17487/RFC7680, January
2016, <
https://www.rfc-editor.org/info/rfc7680>.
[RFC7799] Morton, A., "Active and Passive Metrics and Methods (with
Hybrid Types In-Between)", RFC 7799, DOI 10.17487/RFC7799,
May 2016, <
https://www.rfc-editor.org/info/rfc7799>.
[AFD] Pan, R., Breslau, L., Prabhakar, B., and S. Shenker,
"Approximate fairness through differential dropping", ACM
SIGCOMM Computer Communication Review, Volume 33, Issue 2,
DOI 10.1145/956981.956985, April 2003.
[CCscaling]
Paganini, F., Doyle, J., and S. Low, "Scalable laws for
stable network congestion control", Proceedings of IEEE
Conference on Decision and Control,,
DOI 10.1109/CDC.2001.980095, December 2001.
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[CVST] Krueger, T. and M. Braun, "R package: Fast Cross-
Validation via Sequential Testing", version 0.1, 11 2012.
[iPerf] Wikipedia, "iPerf", November 2017,
<
https://en.wikipedia.org/w/
index.php?title=Iperf&oldid=810583885>.
[MBMSource]
"mbm", July 2016, <
https://github.com/m-lab/MBM>.
[Montgomery90]
Montgomery, D., "Introduction to Statistical Quality
Control", 2nd Edition, ISBN 0-471-51988-X, 1990.
[mpingSource]
"mping", July 2016, <
https://github.com/m-lab/mping>.
[MSMO97] Mathis, M., Semke, J., Mahdavi, J., and T. Ott, "The
Macroscopic Behavior of the TCP Congestion Avoidance
Algorithm", Computer Communications Review, Volume 27,
Issue 3, DOI 10.1145/263932.264023, July 1997.
[Pathdiag] Mathis, M., Heffner, J., O'Neil, P., and P. Siemsen,
"Pathdiag: Automated TCP Diagnosis", Passive and Active
Network Measurement, Lecture Notes in Computer Science,
Volume 4979, DOI 10.1007/978-3-540-79232-1_16, 2008.
[Policing] Flach, T., Papageorge, P., Terzis, A., Pedrosa, L., Cheng,
Y., Karim, T., Katz-Bassett, E., and R. Govindan, "An
Internet-Wide Analysis of Traffic Policing", Proceedings
of ACM SIGCOMM, DOI 10.1145/2934872.2934873, August 2016.
[RACK] Cheng, Y., Cardwell, N., Dukkipati, N., and P. Jha, "RACK:
a time-based fast loss detection algorithm for TCP", Work
in Progress, draft-ietf-tcpm-rack-03, March 2018.
[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, 2011,
<
http://www.R-project.org/>.
[TSO_fq_pacing]
Dumazet, E. and Y. Chen, "TSO, fair queuing, pacing:
three's a charm", Proceedings of IETF 88, TCPM WG,
November 2013,
<
https://www.ietf.org/proceedings/88/slides/
slides-88-tcpm-9.pdf>.
Mathis & Morton Experimental [Page 50]
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[TSO_pacing]
Corbet, J., "TSO sizing and the FQ scheduler", August
2013, <
https://lwn.net/Articles/564978/>.
[Wald45] Wald, A., "Sequential Tests of Statistical Hypotheses",
The Annals of Mathematical Statistics, Volume 16, Number
2, pp. 117-186, June 1945,
<
http://www.jstor.org/stable/2235829>.
[wikiBloat]
Wikipedia, "Bufferbloat", January 2018,
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https://en.wikipedia.org/w/
index.php?title=Bufferbloat&oldid=819293377>.
[WPING] Mathis, M., "Windowed Ping: An IP Level Performance
Diagnostic", Computer Networks and ISDN Systems, Volume
27, Issue 3, DOI 10.1016/0169-7552(94)90119-8, June 1994.
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Appendix A. Model Derivations
The reference target_run_length described in Section 5.2 is based on
very conservative assumptions: that all excess data in flight (i.e.,
the window size) above the target_window_size contributes to a
standing queue that raises the RTT and that classic Reno congestion
control with delayed ACKs 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
method, but this section provides offsetting requirements.
The estimates provided by these models make the most sense if network
performance is viewed logarithmically. In the operational Internet,
data rates span more than eight orders of magnitude, RTT spans more
than three orders of magnitude, and packet loss ratio spans at least
eight orders of magnitude if not more. 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 represents a factor of 2 in untransformed parameter.
This document gives a lot of latitude for calculating
target_run_length; however, people designing a TIDS 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, models were derived based on the assumption that the
subpath IP rate matches the target rate plus overhead, such that the
excess window needed for the AIMD sawtooth causes a fluctuating queue
at the bottleneck.
An alternate situation would be a bottleneck where there is no
significant queue and losses are caused by some mechanism that does
not involve extra delay, for example, by the use of a virtual queue
as done in Approximate Fair Dropping [AFD]. A flow controlled by
such a bottleneck would have a constant RTT and a data rate that
fluctuates in a sawtooth due to AIMD congestion control. Assume the
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losses are being controlled to make the average data rate meet some
goal that is equal to or greater than the target_rate. The necessary
run length to meet the target_rate can be computed as follows:
For some value of Wmin, the window will sweep from Wmin packets to
2*Wmin packets in 2*Wmin RTT (due to delayed ACK). Unlike the
queuing case where Wmin = target_window_size, we want the average of
Wmin and 2*Wmin to be the target_window_size, so the average data
rate is the target rate. Thus, we want Wmin =
(2/3)*target_window_size.
Between losses, each sawtooth delivers (1/2)(Wmin+2*Wmin)(2Wmin)
packets in 2*Wmin RTTs.
Substituting these together, we get:
target_run_length = (4/3)(target_window_size^2)
Note that this is 44% of the reference_run_length computed earlier.
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%.
Appendix B. The Effects of ACK Scheduling
For many network technologies, simple queuing models don't apply: the
network schedules, thins, or otherwise alters the timing of ACKs and
data, generally to raise the efficiency of the channel allocation
algorithms when confronted with relatively widely spaced small ACKs.
These efficiency strategies are ubiquitous for half-duplex, wireless,
and broadcast media.
Altering the ACK stream by holding or thinning ACKs typically has two
consequences: it raises the implied bottleneck IP capacity, making
the fine-grained slowstart bursts either faster or larger, and it
raises the effective RTT by the average time that the ACKs and data
are delayed. The first effect can be partially mitigated by
re-clocking 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 the endpoint currently
holding the channel has more traffic (data or ACKs) to send. Such
environments cause self-clocked protocols under full load to revert
to extremely inefficient stop-and-wait behavior. The channel
constrains the protocol to send an entire window of data as a single
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contiguous burst on the forward path, followed by the entire window
of ACKs on the return path. (A channel with this behavior would fail
the Duplex Self-Interference Test described in Section 8.2.4).
If a particular return path contains a subpath or device that alters
the timing of the ACK stream, then the entire front 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 implied bottleneck IP capacity, which is the average
rate at which the ACKs advance snd.una. Note that thinning the ACK
stream (relying on the cumulative nature of seg.ack to permit
discarding some ACKs) causes most TCP implementations to send
interface rate bursts to offset the longer times between ACKs in
order to maintain the average data rate.
Note that due to ubiquitous self-clocking in Internet protocols,
ill-conceived channel allocation mechanisms are likely to increases
the queuing stress on the front path because they cause larger full
sender rate data bursts.
Holding data or ACKs for channel allocation or other reasons (such as
forward error correction) always raises the effective RTT relative to
the minimum delay for the path. Therefore, it may be necessary to
replace target_RTT in the calculation in Section 5.2 by an
effective_RTT, which includes the target_RTT plus a term to account
for the extra delays introduced by these mechanisms.
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Acknowledgments
Ganga Maguluri suggested the statistical test for measuring loss
probability in the target run length. Alex Gilgur and Merry Mou
helped with the statistics.
Meredith Whittaker improved the clarity of the communications.
Ruediger Geib provided feedback that greatly improved the document.
This work was inspired by Measurement Lab: open tools running on an
open platform, using open tools to collect open data. See
<
http://www.measurementlab.net/>.
Authors' Addresses
Matt Mathis
Google, Inc
1600 Amphitheatre Parkway
Mountain View, CA 94043
United States of America
Email:
[email protected]
Al Morton
AT&T Labs
200 Laurel Avenue South
Middletown, NJ 07748
United States of America
Phone: +1 732 420 1571
Email:
[email protected]
Mathis & Morton Experimental [Page 55]
