Internet Engineering Task Force Sally Floyd
INTERNET-DRAFT Editor
Intended status: Informational 30 July 2007
Expires: January 2008
Metrics for the Evaluation of Congestion Control Mechanisms
draft-irtf-tmrg-metrics-10.txt
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Abstract
This document discusses the metrics to be considered in an
evaluation of new or modified congestion control mechanisms for the
Internet. These include metrics for the evaluation of new transport
protocols, of proposed modifications to TCP, of application-level
Floyd [Page 1]
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congestion control, and of Active Queue Management (AQM) mechanisms
in the router. This document is the first in a series of documents
aimed at improving the models that we use in the evaluation of
transport protocols.
This document is a product of the Transport Modeling Research Group
(TRMG), and has received detailed feedback from many members of the
Research Group (RG). As the document tries to make clear, there is
not necessarily a consensus within the research community (or the
IETF community, the vendor community, the operations community, or
any other community) about the metrics that congestion control
mechanisms should be designed to optimize, in terms of tradeoffs
between throughput and delay, fairness between competing flows, and
the like. However, we believe that there is a clear consensus that
congestion control mechanisms should be evaluated in terms of
tradeoffs between a range of metrics, rather than in terms of
optimizing for a single metric.
Floyd [Page 2]
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Table of Contents
1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . 6
2. Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1. Throughput, Delay, and Loss Rates. . . . . . . . . . . . 8
2.1.1. Throughput. . . . . . . . . . . . . . . . . . . . . 8
2.1.2. Delay . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.3. Packet Loss Rates . . . . . . . . . . . . . . . . . 9
2.2. Response Times and Minimizing Oscillations . . . . . . . 10
2.2.1. Response to Changes . . . . . . . . . . . . . . . . 11
2.2.2. Minimizing Oscillations . . . . . . . . . . . . . . 11
2.3. Fairness and Convergence . . . . . . . . . . . . . . . . 13
2.3.1. Metrics for fairness between flows. . . . . . . . . 13
2.3.2. Metrics for fairness between flows with
different resource requirements. . . . . . . . . . . . . . 14
2.3.3. Comments on fairness. . . . . . . . . . . . . . . . 15
2.4. Robustness for Challenging Environments. . . . . . . . . 16
2.5. Robustness to Failures and to Misbehaving
Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.6. Deployability. . . . . . . . . . . . . . . . . . . . . . 17
2.7. Metrics for Specific Types of Transport. . . . . . . . . 18
2.8. User-Based Metrics . . . . . . . . . . . . . . . . . . . 18
3. Metrics in the IP Performance Metrics (IPPM) Working
Group. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4. Comments on Methodology . . . . . . . . . . . . . . . . . . . 18
5. Security Considerations . . . . . . . . . . . . . . . . . . . 19
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 19
7. Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . 19
Informative References . . . . . . . . . . . . . . . . . . . . . 19
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 24
Full Copyright Statement . . . . . . . . . . . . . . . . . . . . 24
Intellectual Property. . . . . . . . . . . . . . . . . . . . . . 24
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TO BE DELETED BY THE RFC EDITOR UPON PUBLICATION:
Changes from draft-irtf-tmrg-metrics-09.txt:
* Copied a paragraph from the Abstract to the Introduction, and
updated references. From feedback from Tony Li in the IRSG
review.
Changes from draft-irtf-tmrg-metrics-08.txt:
* General feedback from Michael Welzl, adding explanations
to some of the fairness discussions.
Changes from draft-irtf-tmrg-metrics-07.txt:
* General feedback from Mark Allman.
* Feedback and contributions from Lachlan Andrew about fairness.
Changes from draft-irtf-tmrg-metrics-06.txt:
* Added a paragraph about tradeoffs between
fairness and throughput.
* Minor editing changes.
Changes from draft-irtf-tmrg-metrics-05.txt:
* Added a sentence about drop synchronization.
Feedback from David Ros.
* Added a citation to fairness draft by Briscoe.
Changes from draft-irtf-tmrg-metrics-04.txt:
* Added text to the abstract.
Changes from draft-irtf-tmrg-metrics-03.txt:
* Added a paragraph about sudden changes due to mobility
and the heterogeneity of wireless access types.
Suggestion from Andras Veres.
* Add covariance as one of the metrics for oscillations.
Suggestion from Saverio Mascolo, original text
contribution from Injong Rhee.
Changes from draft-irtf-tmrg-metrics-02.txt:
Floyd [Page 4]
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* Added a few sentences to the Abstract about the
status of the document.
Changes from draft-irtf-tmrg-metrics-01.txt:
* Added a discussion about the metrics in IPPM.
Changes from draft-irtf-tmrg-metrics-01c.txt:
* Added to the discussion of network-based, flow-based,
and user-based metrics, based on email from Dado Colussi,
Sean Moore, Damon Wischik, Dah Ming Chiu, and others.
* Changed "packet drop rate" to "packet loss rate".
Suggestion from Nelson Fonseca.
* Added a discussion of the Colussi et al. paper on a new
definition of fairness.
* Added a discussion of the Chiu and Tan paper on redefining
fairness for inelastic traffic.
Changes from draft-irtf-tmrg-metrics-01b.txt:
* Added a discussion of goodput vs. throughput.
Suggestion from Nelson Fonseca.
Changes from draft-irtf-tmrg-metrics-01a.txt:
* Added to the discussion of packet drop rate metrics.
Suggestions from Janardhan Iyengar, Sean Moore,
Armando Caro, and Nelson Fonseca.
* Added a sentence about throughput used as a metric for
transfer times for very short flows.
Response to email from Sean Moore.
Changes from draft-irtf-tmrg-metrics-00.txt:
* Added a list of relevant congestion control mechanisms to
the abstract. Suggestion from Sean Moore.
* Added to the Introduction. Suggestion from Dado Colussi.
* Added a sentence about jitter to the discussion of minimizing
oscillations. Suggestion from Wesley Eddy.
* Added a note about convergence between existing flows after
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a change in bandwidth. Suggestion from Wesley Eddy.
* Added to the section on deployability. Suggestion from
Wesley Eddy.
Changes from draft-floyd-transport-metrics-00.txt:
* Added metrics for:
- robustness in challenging environments,
- deployability,
- robustness to failures and to misbehaving users
* Added a discussion of fairness and packet size.
1. Introduction
As a step towards improving our methodologies for evaluating
congestion control mechanisms, in this document we discuss some of
the metrics to be considered. We also consider the relationship
between metrics, e.g., the well-known tradeoff between throughput
and delay. This document doesn't attempt to specify every metric
that a study might consider; for example, there are domain-specific
metrics that researchers might consider that are over and above the
metrics laid out in this document.
We consider metrics for aggregate traffic (taking into account the
effect of flows on competing traffic in the network) as well as the
heterogeneous goals of different applications or transport protocols
(e.g., of high throughput for bulk data transfer, and of low delay
for interactive voice or video). Different transport protocols or
AQM mechanisms might have goals of optimizing different sets of
metrics, with one transport protocol optimized for per-flow
throughput and another optimized for robustness over wireless links,
and with different degrees of attention to fairness with competing
traffic. We hope this document will be used as a step in evaluating
proposed congestion control mechanisms for a wide range of metrics,
for example, noting that Mechanism X is good at optimizing Metric A,
but pays the price with poor performance for Metric B. The goal
would be to have a broad view of both the strengths and weaknesses
of newly-proposed congestion control mechanisms.
Subsequent documents are planned to present sets of simulation and
testbed scenarios for the evaluation of transport protocols and of
congestion control mechanisms, based on the best current practice of
the research community. These are not intended to be complete or
final benchmark test suites, but simply to be one step of many to be
used by researchers in evaluating congestion control mechanisms.
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Subsequent documents are also planned on the methodologies in using
these sets of scenarios.
This document is a product of the Transport Modeling Research Group
(TRMG), and has received detailed feedback from many members of the
Research Group (RG). As the document tries to make clear, there is
not necessarily a consensus within the research community (or the
IETF community, the vendor community, the operations community, or
any other community) about the metrics that congestion control
mechanisms should be designed to optimize, in terms of tradeoffs
between throughput and delay, fairness between competing flows, and
the like. However, we believe that there is a clear consensus that
congestion control mechanisms should be evaluated in terms of
tradeoffs between a range of metrics, rather than in terms of
optimizing for a single metric.
2. Metrics
The metrics that we discuss are the following:
o Throughput;
o Delay;
o Packet loss rates;
o Response to sudden changes or to transient events;
o Minimizing oscillations in throughput or in delay;
o Fairness and convergence times;
o Robustness for challenging environments;
o Robustness to failures and to misbehaving users;
o Deployability;
o Metrics for specific types of transport;
o User-based metrics.
We consider each of these below. Many of the metrics have network-
based, flow-based, and user-based interpretations. For example,
network-based metrics can consider aggregate bandwidth and aggregate
drop rates, flow-based metrics can consider end-to-end transfer
times for file transfers or end-to-end delay and packet drop rates
for interactive traffic, and user-based metrics can consider user
Floyd Section 2. [Page 7]
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wait time or user satisfaction with the multimedia experience. Our
main goal in this document is to explain the set of metrics that can
be relevant, and not to legislate on the most appropriate
methodology for using each general metric.
For some of the metrics, such as fairness, there is not a clear
agreement in the network community about the desired goals. In
these cases, the document attempts to present the range of
approaches.
2.1. Throughput, Delay, and Loss Rates
Because of the clear tradeoffs between throughput, delay, and loss
rates, it can be useful to consider these three metrics together.
The tradeoffs are most clear in terms of queue management at the
router; is the queue configured to maximize aggregate throughput, to
minimize delay and loss rates, or some compromise between the two?
An alternative would be to consider a separate metric such as power,
defined in this context as throughput over delay, that combines
throughput and delay. However, we do not propose in this document a
clear target in terms of the tradeoffs between throughput and delay;
we are simply proposing that the evaluation of transport protocols
include an exploration of the competing metrics.
Using flow-based metrics instead of router-based metrics, the
relationship between per-flow throughput, delay, and loss rates can
often be given by the transport protocol itself. For example, in
TCP, the sending rate s in packets per second is given as
s = 1.2/(RTT*sqrt(p)),
for the round-trip time RTT and loss rate p [FF99].
2.1.1. Throughput
Throughput can be measured as a router-based metric of aggregate
link utilization, as a flow-based metric of per-connection transfer
times, and as user-based metrics of utility functions or user wait
times. It is a clear goal of most congestion control mechanisms to
maximize throughput, subject to application demand and to the
constraints of the other metrics.
Throughput is sometimes distinguished from goodput, where throughput
is the link utilization or flow rate in bytes per second, and
goodput, also measured in bytes per second, is the subset of
throughput consisting of useful traffic. That is, `goodput'
excludes duplicate packets, packets that will be dropped downstream,
packet fragments or ATM cells that are dropped at the receiver
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because they can't be re-assembled into complete packets, and the
like.
We note that maximizing throughput is of concern in a wide range of
environments, from highly-congested networks to under-utilized ones,
and from long-lived flows to very short ones. As an example,
throughput has been used as one of the metrics for evaluating Quick-
Start, a proposal to allow flows to start-up faster than slow-start,
where throughput has been evaluated in terms of the transfer times
for connections with a range of transfer sizes [RFC4782] [SAF06].
In some contexts, it might be sufficient to consider the aggregate
throughput or the mean per-flow throughput, while in other contexts
it might be necessary to consider the distribution of per-flow
throughput. Some researchers evaluate transport protocols in terms
of maximizing the aggregate user utility, where a user's utility is
generally defined as a function of the user's throughput [KMT98].
Individual applications can have application-specific needs in terms
of throughput. For example, real-time video traffic can have highly
variable bandwidth demands; VoIP traffic is sensitive to the amount
of bandwidth received immediately after idle periods. Thus, user
metrics for throughput can be more complex than simply the per-
connection transfer time.
2.1.2. Delay
Like throughput, delay can be measured as a router-based metric of
queueing delay over time, or as a flow-based metric in terms of per-
packet transfer times. For reliable transfer, the per-packet
transfer time seen by the application includes the possible delay of
retransmitting a lost packet.
Users of bulk data transfer applications might care about per-packet
transfer times only in so far as they affect the per-connection
transfer time. On the other end of the spectrum, for users of
streaming media, per-packet delay can be a significant concern.
Note that in some cases the average delay might not capture the
metric of interest to the users; for example, some users might care
about the worst-case delay, or about the tail of the delay
distribution.
2.1.3. Packet Loss Rates
Packet loss rates can be measured as a network-based or as a flow-
based metric.
When evaluating the effect of packet losses or ECN marks (Explicit
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Congestion Notification) [RFC3168] on the performance of a
congestion control mechanism for an individual flow, researchers
often use both the packet loss/mark rate for that connection, and
the congestion event rate (also called the loss event rate), where a
congestion event or loss event consists of one or more lost or
marked packets in one round-trip time [RFC3448].
Some users might care about the packet loss rate only in so far as
it affects per-connection transfer times, while other users might
care about packet loss rates directly. RFC 3611, RTP Control
Protocol Extended Reports, describes a VoIP performance-reporting
standard called RTCP XR, which includes a set of burst metrics. In
RFC 3611, a burst is defined as the maximal sequence starting and
ending with a lost packet, and not including a sequence of Gmin or
more packets that are not lost [RFC3611]. The burst metrics in RFC
3611 consist of the burst density (the fraction of packets in
bursts), gap density (the fraction of packets in the gaps between
bursts), burst duration (the mean duration of bursts in seconds),
and gap duration (the mean duration of gaps in seconds). RFC 3357
derives metrics for "loss distance" and "loss period", along with
statistics that capture loss patterns experienced by packet streams
on the Internet [RFC3357].
In some cases it is useful to distinguish between packets dropped at
routers due to congestion, and packets lost in the network due to
corruption.
One network-related reason to avoid high steady-state packet loss
rates is to avoid congestion collapse in environments containing
paths with multiple congested links. In such environments, high
packet loss rates could result in congested links wasting scarce
bandwidth by carrying packets that will only be dropped downstream,
before being delivered to the receiver [RFC2914]. We also note that
in some cases the retransmit rate can be high, and the goodput
correspondingly low, even with a low packet drop rate [AEO03].
2.2. Response Times and Minimizing Oscillations
In this section we consider response times and oscillations
together, as there are well-known tradeoffs between improving
response times and minimizing oscillations. In addition, the
scenarios that illustrate the dangers of poor response times are
often quite different from the scenarios that illustrate the dangers
of unnecessary oscillations.
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2.2.1. Response to Changes
One of the key concerns in the design of congestion control
mechanisms has been the response times to sudden congestion in the
network. On the one hand, congestion control mechanisms should
respond reasonably promptly to sudden congestion from routing or
bandwidth changes, or from a burst of competing traffic. At the
same time, congestion control mechanisms should not respond too
severely to transient changes, e.g., to a sudden increase in delay
that will dissipate in less than the connection's round-trip time.
Congestion control mechanisms also have to contend with sudden
changes in the bandwidth-delay product due to mobility. Such
bandwith-delay product changes are expected to become more frequent
and to have greater impact than path changes today. As a result of
both mobility and of the heterogeneity of wireless access types
(802.11b,a,g, WIMAX, WCDMA, HS-WCDMA, E-GPRS, Bluetooth, etc.), both
the bandwidth and the round-trip delay can change suddenly,
sometimes by several orders of magnitude.
Evaluating the response to sudden or transient changes can be of
particular concern for slowly-responding congestion control
mechanisms such as equation-based congestion control [RFC3448], and
for AIMD (Additive Increase Multiplicative Decrease) or related
mechanisms using parameters that make them more slowly-responding
that TCP [BB01] [BBFS01].
In addition to the responsiveness and smoothness of aggregate
traffic, one can consider the tradeoffs between responsiveness,
smoothness, and aggressiveness for an individual connection [FHP00]
[YKL01]. In this case smoothness can be defined by the largest
reduction in the sending rate in one round-trip time, in a
deterministic environment with a packet drop exactly every 1/p
packets. The responsiveness is defined as the number of round-trip
times of sustained congestion required for the sender to halve the
sending rate, and the aggressiveness is defined as the maximum
increase in the sending rate in one round-trip time, in packets per
second, in the absence of congestion. This aggressiveness can be a
function of the mode of the transport protocol; for TCP, the
aggressiveness of slow-start is quite different from the
aggressiveness of congestion avoidance mode.
2.2.2. Minimizing Oscillations
One goal is that of stability, in terms of minimizing oscillations
of queueing delay or of throughput. In practice, stability is
frequently associated with rate fluctuations or variance. Rate
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variations can result in fluctuations in router queue size and
therefore of queue overflows. These queue overflows can cause loss
synchronizations across co-existing flows and periodic under-
utilization of link capacity, both of which are considered to be
general signs of network instability. Thus, measuring the rate
variations of flows is often used to measure the stability of
transport protocols. To measure rate variations, [JWL04], [RX05],
and [FHPW00] use the coefficient of variation (CoV) of per-flow
transmission rates and [WCL05] suggests the use of standard
deviations of per-flow rates. Since rate variations are a function
of time scales, it makes sense to measure these rate variations over
various time scales.
Measuring per-flow rate variations, however, is only one aspect of
transport protocol stability. A realistic experiment setting always
involves multiple flows of the transport protocol being observed,
along with a significant amount of cross traffic, with rates varying
over time, on both the forward and reverse paths. As a congestion
control protocol must adapt its rate to the varying rates of
competing traffic, just measuring the per-flow statistics of a
subset of the traffic could be misleading because it measures the
rate fluctuations due in part to the adaptation to competing traffic
on the path. Thus, per-flow statistics are most meaningful if they
are accompanied by the statistics measured at the network level. As
a complementary metric to the per-flow statistics, [HKLRX06] uses
measurements of the rate variations of the aggregate flows observed
in bottleneck routers over various time scales.
Minimizing oscillations in queueing delay or throughput has related
per-flow metrics of minimizing jitter in round-trip times and loss
rates.
An orthogonal goal for some congestion control mechanisms, e.g., for
equation-based congestion control, is to minimize the oscillations
in the sending rate for an individual connection, given an
environment with a fixed, steady-state packet drop rate. (As is
well known, TCP congestion control is characterized by a pronounced
oscillation in the sending rate, with the sender halving the sending
rate in response to congestion.) One metric for the level of
oscillations is the smoothness metric given in Section 2.2.1 above.
As discussed in [FK07], the synchronization of loss events can also
affect convergence times, throughput, and delay.
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2.3. Fairness and Convergence
Another set of metrics is that of fairness and convergence times.
Fairness can be considered between flows of the same protocol, and
between flows using different protocols (e.g., TCP-friendliness,
referring to fairness between TCP and a new transport protocol).
Fairness can also be considered between sessions, between users, or
between other entities.
There are a number of different fairness measures. These include
max-min fairness [HG86], proportional fairness [KMT98] [K01], the
fairness index proposed in [JCH84], and the product measure, a
variant of network power [BJ81].
2.3.1. Metrics for fairness between flows
This section discusses fairness metrics that consider the fairness
between flows, but that don't take into account different
characteristics of flows (e.g., the number of links in the path, or
the round-trip time).
Jain's fairness index: The fairness index in [JCH84] is
(( sum_i x_i )^2) / (n * sum_i ( (x_i)^2 )),
where there are n users. This fairness index ranges from 0 to 1,
and is maximum when all users receive the same allocation. This
index is k/n when k users equally share the resource, and the other
n-k users receive zero allocation.
The product measure: The product measure
product_i x_i ,
the product of the throughput of the individual connections, is also
used as a measure of fairness. For our purposes, let x_i be the
throughput for the i-th connection. (In other contexts x_i is taken
as the power of the i-th connection, and the product measure is
referred to as network power.) The product measure is particularly
sensitive to segregation; the product measure is zero if any
connection receives zero throughput. In [MS91] it is shown that for
a network with many connections and one shared gateway, the product
measure is maximized when all connections receive the same
throughput.
Epsilon-fairness: A rate allocation is defined as epsilon-fair if
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(min_i x_i) / (max_i x_i) >= 1 - epsilon.
where x_i is the resource allocation to the i-th flow or user.
Epsilon-fairness measures the worst-case ratio between any two
throughput rates [ZKL04]. Epsilon-fairness is related to max-min
fairness, defined later in this document.
2.3.2. Metrics for fairness between flows with different resource
requirements
This section discusses metrics for fairness between flows with
different resource requirements, that is, with different utility
functions, round-trip times, or numbers of links on the path. Many
of these metrics can be described as solutions to utility
maximization problems [K01]; the total utility quantifies both the
fairness and the throughput.
Max-min fairness: In order to satisfy the max-min fairness criteria,
the smallest throughput rate must be as large as possible. Given
this condition, the next-smallest throughput rate must be as large
as possible, and so on. Thus, the max-min fairness gives absolute
priority to the smallest flows. (Max-min fairness can be explained
by the progressive filling algorithm, where all flow rates start at
zero, and the rates all grow at the same pace. Each flow rate stops
growing only when one or more links on the path reaches link
capacity.)
Proportional fairness: In contrast, a feasible allocation x is
defined as proportionally fair if for any other feasible allocation
x*, the aggregate of proportional changes is zero or negative:
sum_i ( (x*_i - x_i)/x_i ) <= 0.
"This criterion favours smaller flows, but less emphatically than
max-min fairness" [K01]. (Using the language of utility functions,
proportional fairness can be achieved by using logarithmic utility
functions, and maximizing the sum of the per-flow utility functions;
see [F98] for a fuller explanation.)
Minimum potential delay fairness: Minimum potential delay fairness
has been shown to model TCP [KS03], and is a compromise between max-
min fairness and proportional fairness. An allocation x is defined
as having minimum potential delay fairness if
sum_i (1/x_i)
is smaller than for any other feasible allocation. That is, it
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would minimize the average download time if each flow was an equal-
sized file.
In [CRM05], Colussi et al. propose a new definition of TCP fairness,
that "a set of TCP fair flows do not cause more congestion than a
set of TCP flows would cause", where congestion is defined in terms
of queueing delay, queueing delay variation, the congestion event
rate [e.g., loss event rate], and the packet loss rate.
Chiu and Tan in [CT06] argue for redefining the notion of fairness
when studying traffic controls for inelastic traffic, proposing that
inelastic flows adopt other traffic controls such as admission
control.
Briscoe in [B07] argues that flow-rate fairness should not be a
desired goal, and that instead "a realistic fairness mechanism must
share out the `cost' of each users actions on others".
2.3.3. Comments on fairness
Tradeoffs between fairness and throughput: The fairness measures in
the section above generally measure both fairness and throughput,
giving different weights to each. Potential tradeoffs between
fairness and throughput are also discussed by Tang et al. in
[TWL06], for a framework where max-min fairness is defined as the
most fair. In particular, [TWL06] shows that in some topologies
throughput is proportional to fairness, while in other topologies
throughput is inversely proportional to fairness.
Fairness and the number of congested links: Some of these fairness
metrics are discussed in more detail in [F91]. We note that there
is not a clear consensus for the fairness goals, in particular for
fairness between flows that traverse different numbers of congested
links [F91]. Utility maximization provides one framework for
describing this tradeoff in fairness.
Fairness and round-trip times: One goal cited in a number of new
transport protocols has been that of fairness between flows with
different round-trip times [KHR02] [XHR04]. We note that there is
not a consensus in the networking community about the desirability
of this goal, or about the implications and interactions between
this goal and other metrics [FJ92] (Section 3.3). One common
argument against the goal of fairness between flows with different
round-trip times has been that flows with long round-trip times
consume more resources; this aspect is covered by the previous
paragraph. Researchers have also noted the difference between the
RTT-unfairness of standard TCP, and the greater RTT-unfairness of
Floyd Section 2.3.3. [Page 15]
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some proposed modifications to TCP [LLS05].
Fairness and packet size: One fairness issue is that of the relative
fairness for flows with different packet sizes. Many file transfer
applications will use the maximum packet size possible; in
contrast, low-bandwidth VoIP flows are likely to send small packets,
sending a new packet every 10 to 40 ms., to limit delay. Should a
small-packet VoIP connection receive the same sending rate in
*bytes* per second as a large-packet TCP connection in the same
environment, or should it receive the same sending rate in *packets*
per second? This fairness issue has been discussed in more detail
in [RFC3714], with [RFC4828] also describing the ways that packet
size can effect the packet drop rate experienced by a flow.
Convergence times: Convergence times concern the time for
convergence to fairness between an existing flow and a newly-
starting one, and are a special concern for environments with high-
bandwidth long-delay flows. Convergence times also concern the time
for convergence to fairness after a sudden change such as a change
in the network path, the competing cross-traffic, or the
characteristics of a wireless link. As with fairness, convergence
times can matter both between flows of the same protocol, and
between flows using different protocols [SLFK03]. One metric used
for convergence times is the delta-fair convergence time, defined as
the time taken for two flows with the same round-trip time to go
from shares of 100/101-th and 1/101-th of the link bandwidth, to
having close to fair sharing with shares of (1+delta)/2 and
(1-delta)/2 of the link bandwidth [BBFS01]. A similar metric for
convergence times measures the convergence time as the number of
round-trip times for two flows to reach epsilon-fairness, when
starting from a maximally-unfair state [ZKL04].
2.4. Robustness for Challenging Environments
While congestion control mechanisms are generally evaluated first
over environments with static routing in a network of two-way point-
to-point links, some environments bring up more challenging problems
(e.g., corrupted packets, reordering, variable bandwidth, mobility)
as well as new metrics to be considered (e.g., energy consumption).
Robustness for challenging environments: Robustness needs to be
explored for paths with reordering, corruption, variable bandwidth,
asymmetric routing, router configuration changes, mobility, and the
like [GF04]. In general, the Internet architecture has valued
robustness over efficiency, e.g., when there are tradeoffs between
robustness and the throughput, delay, and fairness metrics described
above.
Floyd Section 2.4. [Page 16]
INTERNET-DRAFT TMRG, METRICS July 2007
Energy consumption: In mobile environments the energy consumption
for the mobile end-node can be a key metric that is affected by the
transport protocol [TM02].
Goodput: For wireless networks, goodput can be a useful metric,
where goodput can be defined as the useful data delivered to users
as a fraction of the amount of data transmitted on the network.
High goodput indicates an efficient use of the radio spectrum and
lower interference with other users.
2.5. Robustness to Failures and to Misbehaving Users
One goal is for congestion control mechanisms to be robust to
misbehaving users, such as receivers that `lie' to data senders
about the congestion experienced along the path or otherwise attempt
to bypass the congestion control mechanisms of the sender [SCWA99].
Another goal is for congestion control mechanisms to be as robust as
possible to failures, such as failures of routers in using explicit
feedback to end-nodes or failures of end-nodes to follow the
prescribed protocols.
2.6. Deployability
One metric for congestion control mechanisms is their deployability
in the current Internet. Metrics related to deployability include
the ease of failure diagnosis, and the overhead in terms of packet
header size or added complexity at end-nodes or routers.
One key aspect of deployability concerns the range of deployment
needed for a new congestion control mechanism. Consider the
following possible deployment requirements:
* Only at the sender (e.g., NewReno in TCP [RFC3782]);
* Only at the receiver (e.g., delayed acknowledgements in TCP);
* Both the sender and receiver (e.g., SACK TCP [RFC2018]);
* At a single router (e.g., RED [FJ93]);
* All of the routers along the end-to-end path;
* Both end nodes and all routers along the path (e.g., XCP [KHR02]).
Another deployability issue concerns the complexity of the code.
How complex is the code required to implement the mechanism in
software? Is floating point math required? How much new state must
be kept to implement the new mechanism, and who holds that state,
the routers or the end-nodes? We note that we don't suggest these
questions as ways to reduce the deployability metric to a single
number; we suggest them as issues that could be considered in
Floyd Section 2.6. [Page 17]
INTERNET-DRAFT TMRG, METRICS July 2007
evaluating the deployability of a proposed congestion control
mechanism.
2.7. Metrics for Specific Types of Transport
In some cases modified metrics are needed for evaluating transport
protocols intended for QoS-enabled environments or for below-best-
effort traffic [VKD02] [KK03].
2.8. User-Based Metrics
An alternate approach that has been proposed for the evaluation of
congestion control mechanisms would be to evaluate in terms of user
metrics such as user satisfaction, or in terms of application-
specific utility functions. Such an approach would require the
definition of a range of user metrics or of application-specific
utility functions for the range of applications under consideration
(e.g., FTP, HTTP, VoIP).
3. Metrics in the IP Performance Metrics (IPPM) Working Group
The IPPM Working Group [IPPM] was established to define performance
metrics to be used by network operators, end users, or independent
testing groups. The metrics include metrics for connectivity
[RFC2678], delay and loss [RFC2679] [RFC2680] [RFC2681], delay
variation [RFC3393], loss patterns [RFC3357], packet reordering
[RFC4737], bulk transfer capacity [RFC3148], and link capacity
[CI07]. The IPPM documents give concrete, well-defined metrics,
along with a methodology for measuring the metric. The metrics
discussed in this document have a different purpose from the IPPM
metrics; in this document we are discussing metrics as used in
analysis, simulations, and experiments for the evaluation of
congestion control mechanisms. Further, we are discussing these
metrics in a general sense, rather than looking for specific
concrete definitions for each metric. However, there are many cases
where the metric definitions from IPPM could be useful, for specific
issues of how to measure these metrics in simulations or in
testbeds, and for providing common definitions for talking about
these metrics.
4. Comments on Methodology
The types of scenarios that are used to test specific metrics, and
the range of parameters that it is useful to consider, will be
discussed in separate documents, e.g., along with specific scenarios
for use in evaluating congestion control mechanisms.
Floyd Section 4. [Page 18]
INTERNET-DRAFT TMRG, METRICS July 2007
We note that it can be important to evaluate metrics over a wide
range of environments, with a range of link bandwidths, congestion
levels, and levels of statistical multiplexing. It is also
important to evaluate congestion control mechanisms in a range of
scenarios, including typical ranges of connection sizes and round-
trip times [FK02]. It is also useful to compare metrics for new or
modified transport protocols with those of the current standards for
TCP.
As an example from the literature on evaluating transport protocols,
Li et al. in "Experimental Evaluation of TCP Protocols for High-
Speed Networks" [LLS05] focus on the performance of TCP in high-
speed networks, and consider metrics for aggregate throughput, loss
rates, fairness (including fairness between flows with different
round-trip times), response times (including convergence times), and
incremental deployment. More general references on methodology
include [J91]. Papers that discuss the range of metrics for
evaluating congestion control include [MTZ04].
5. Security Considerations
There are no security considerations in this document.
6. IANA Considerations
There are no IANA considerations in this document.
7. Acknowledgements
Thanks to Lachlan Andrew, Mark Allman, Armando Caro, Dah Ming Chiu,
Eric Coe, Dado Colussi, Wesley Eddy, Nelson Fonseca, Janardhan
Iyengar, Doug Leith, Tony Li, Saverio Mascolo, Sean Moore, Injong
Rhee, David Ros, Andras Veres, Michael Welzl, and Damon Wischik, and
members of the Transport Modeling Research Group for feedback and
contributions.
Informative References
[AEO03] M. Allman, W. Eddy, and S. Ostermann, Estimating Loss Rates
With TCP, ACM Performance Evaluation Review, 31(3), December
2003.
[BB01] D. Bansal and H. Balakrishnan, Binomial Congestion Control
Algorithms, IEEE Infocom, April 2001.
[BBFS01] D. Bansal, H. Balakrishnan, S. Floyd, and S. Shenker,
Dynamic Behavior of Slowly-Responsive Congestion Control
Algorithms, SIGCOMM 2001.
Floyd [Page 19]
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[BJ81] K. Bharath-Kumar and J. Jeffrey, A New Approach to
Performance-Oriented Flow Control, IEEE Transactions on
Communications, Vol.29 N.4, April 1981.
[B07] B. Briscoe, Flow Rate Fairness: Dismantling a Religion,
internet-draft draft-briscoe-tsvarea-fair-02.txt, work-in-
progress, July 2007.
[CI07] P. Chimento and J. Ishac, Defining Network Capacity,
internet-draft draft-ietf-ippm-bw-capacity-05, work-in-progress,
May 2007.
[CRM05] D. Colussi, A New Approach to TCP-Fairness, Report
C-2005-49, University of Helsinki, Finland, 2005.
[CT06] D. Chiu and A. Tam, Redefining Fairness in the Study of TCP-
friendly Traffic Controls, Technical Report, 2006.
[F91] S. Floyd, Connections with Multiple Congested Gateways in
Packet-Switched Networks Part 1: One-way Traffic, Computer
Communication Review, Vol.21 No.5, October 1991, p. 30-47.
[F98] F. Kelly, A. Maulloo, and D. Tan, Rate Control in
Communication Networks: Shadow Prices, Proportional Fairness and
Stability, Journal of the Operational Research Society 49
(1998), 237-252.
[FF99] Floyd, S., and Fall, K., "Promoting the Use of End-to-End
Congestion Control in the Internet", IEEE/ACM Transactions on
Networking, August 1999.
[FHP00] S. Floyd, M. Handley, and J. Padhye, A Comparison of
Equation-Based and AIMD Congestion Control, May 2000. URL
"http://www.icir.org/tfrc/".
[FHPW00] S. Floyd, M. Handley, J. Padhye, and J. Widmer, Equation-
Based Congestion Control for Unicast Applications, SIGCOMM 2000,
August 2000.
[FJ92] S. Floyd and V. Jacobson, On Traffic Phase Effects in Packet-
Switched Gateways, Internetworking: Research and Experience, V.3
N.3, September 1992, p.115-156.
[FJ93] S. Floyd and V. Jacobson, Random Early Detection gateways for
Congestion Avoidance, IEEE/ACM Transactions on Networking, V.1
N.4, August 1993,
Floyd [Page 20]
INTERNET-DRAFT TMRG, METRICS July 2007
[FK02] S. Floyd and E. Kohler, Internet Research Needs Better
Models, Hotnets-I. October 2002.
[FK07] S. Floyd and E. Kohler, Tools for the Evaluation of
Simulation and Testbed Scenarios, internet-draft draft-irtf-
tmrg-tools-04.txt, July 2007.
[GF04] A. Gurtov and S. Floyd, Modeling Wireless Links for Transport
Protocols, ACM CCR, 34(2):85-96, April 2004.
[HKLRX06] S. Ha, Y. Kim, L. Le, I. Rhee, and L. Xu, A Step Toward
Realistic Evaluation of High-speed TCP Protocols, technical
report, North Carolina State University, January 2006.
[HG86] E. Hahne and R. Gallager, Round Robin Scheduling for Fair
Flow Control in Data Communications Networks, IEEE International
Conference on Communications, June 1986.
[IPPM] IP Performance Metrics (IPPM) Working Group, URL
"http://www.ietf.org/html.charters/ippm-charter.html".
[J91] R. Jain, The Art of Computer Systems Performance Analysis:
Techniques for Experimental Design, Measurement, Simulation, and
Modeling, John Wiley & Sons, 1991.
[JCH84] R. Jain, D.M. Chiu, and W. Hawe, A Quantitative Measure of
Fairness and Discrimination for Resource Allocation in Shared
Systems, DEC TR-301, Littleton, MA: Digital Equipment
Corporation, 1984.
[JWL04] C. Jin, D. Wei, and S. Low, FAST TCP: Motivation,
Architecture, Algorithms, Performance, IEEE INFOCOM, March 2004.
[K01] F. Kelly, Mathematical Modelling of the Internet, "Mathematics
Unlimited - 2001 and Beyond" (Editors B. Engquist and W.
Schmid), Springer-Verlag, Berlin, pp. 685-702, 2001.
[KHR02] D. Katabi, M. Handley, and C. Rohrs, Congestion Control for
High Bandwidth-Delay Product Networks, ACM Sigcomm, 2002.
[KK03] A. Kuzmanovic and E. W. Knightly, TCP-LP: A Distributed
Algorithm for Low Priority Data Transfer, IEEE INFOCOM 2003,
April 2003.
[KMT98] F. Kelly, A. Maulloo and D. Tan, Rate Control in
Communication Networks: Shadow Prices, Proportional Fairness and
Stability. Journal of the Operational Research Society 49, pp.
237-252, 1998.
Floyd [Page 21]
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[KS03] S. Kunniyur and R. Srikant, End-to-end Congestion Control
Schemes: Utility Functions, Random Losses and ECN Marks,
IEEE/ACM Transactions on Networking, 11(5):689-702, October
2003.
[LLS05] Y-T. Li, D. Leith, and R. Shorten, Experimental Evaluation
of TCP Protocols for High-Speed Networks, Hamilton Institute,
2005. URL "http://www.hamilton.ie/net/eval/results-HI2005.pdf".
[MS91] D. Mitra and J. Seery, Dynamic Adaptive Windows for High
Speed Data Networks with Multiple Paths and Propagation Delays,
INFOCOM '91, pp 39-48.
[MTZ04] L. Mamatas, V. Tsaoussidis, and C. Zhang, Approaches to
Congestion Control in Packet Networks, 2004.
[RFC2018] TCP Selective Acknowledgement Options. M. Mathis, J.
Mahdavi, D. Floyd, and A. Romanow. RFC 2018, Proposed Standard,
April 1996.
[RFC2680] Almes, G., Kalidindi, S., and M. Zekauskas, "A One-way
Packet Loss Metric for IPPM", RFC 2680, September 1999.
[RFC2678] Mahdavi, J. and V. Paxson, "IPPM Metrics for Measuring
Connectivity", RFC 2678, September 1999.
[RFC2679] Almes, G., Kalidindi, S. and M. Zekauskas, "A One-way
Delay Metric for IPPM", RFC 2679, September 1999.
[RFC2681] Almes, G., Kalidindi, S., Zekauskas, M., "A Round-Trip
Delay Metric for IPPM". RFC 2681, STD 1, September 1999.
[RFC2914] S. Floyd, Congestion Control Principles, RFC 2914,
September 2000.
[RFC3148] Mathis, M. and M. Allman, "A Framework for Defining
Empirical Bulk Transfer Capacity Metrics", RFC 3148, July 2001.
[RFC3168] K.K. Ramakrishnan, S. Floyd, and D. Black, The Addition of
Explicit Congestion Notification (ECN) to IP, RFC 3168,
September 2001.
[RFC3357] R. Koodli and R. Ravikanth, One-way Loss Pattern Sample
Metrics, RFC 3357, Informational, August 2002.
[RFC3393] Demichelis, C. and P. Chimento, "IP Packet Delay Variation
Metric for IP Performance Metrics (IPPM)", RFC 3393, November
2002.
Floyd [Page 22]
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[RFC3448] M. Handley, S. Floyd, J. Padhye, and J. Widmer, TCP
Friendly Rate Control (TFRC): Protocol Specification, RFC 3448,
Proposed Standard, January 2003.
[RFC3611] T. Friedman, R. Caceres, and A. Clark, RTP Control
Protocol Extended Reports (RTCP XR), RFC 3611, November 2003.
[RFC3714] S. Floyd and J. Kempf, IAB Concerns Regarding Congestion
Control for Voice Traffic in the Internet, RFC 3714, March 2004.
[RFC3782] S. Floyd, T. Henderson, and A. Gurtov, The NewReno
Modification to TCP's Fast Recovery Algorithm, RFC 3782,
Proposed Standard, April 2004.
[RFC4737] A. Morton, L. Ciavattone, G. Ramachandran, S. Shalunov, J.
Perser, Packet Reordering Metrics, RFC 4737, November 2006.
[RFC4782] S. Floyd, M. Allman, A. Jain, and P. Sarolahti, Quick-
Start for TCP and IP, RFC 4782, Experimental, January 2007.
[RFC4828] S. Floyd and E. Kohler, TCP Friendly Rate Control (TFRC):
the Small-Packet (SP) Variant, RFC 4828, April 2007.
[RX05] I. Rhee and L. Xu, CUBIC: A New TCP-Friendly High-Speed TCP
Variant, PFLDnet 2005.
[SAF06] P. Sarolahti, M. Allman, and S. Floyd, Determining an
Appropriate Sending Rate Over an Underutilized Network Path,
Computer Networks, September 2006.
[SLFK03] R.N. Shorten, D.J. Leith, J. Foy, and R. Kilduff, Analysis
and Design of Congestion Control in Synchronised Communication
Networks. Proc. 12th Yale Workshop on Adaptive & Learning
Systems, May 2003.
[SCWA99] S. Savage, N. Cardwell, D. Wetherall, and T. Anderson, TCP
Congestion Control with a Misbehaving Receiver, ACM Computer
Communications Review, October 1999.
[TM02] V. Tsaoussidis and I. Matta, Open Issues of TCP for Mobile
Computing, Journal of Wireless Communications and Mobile
Computing: Special Issue on Reliable Transport Protocols for
Mobile Computing, February 2002.
[TWL06] A. Tang, J. Wang and S. H. Low. Counter-Intuitive
Throughput Behaviors in Networks Under End-to-End Control,
IEEE/ACM Transactions on Networking, 14(2):355-368, April 2006.
Floyd [Page 23]
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[WCL05] D. X. Wei, P. Cao and S. H. Low, Time for a TCP Benchmark
Suite?, Technical Report, Caltech CS, Stanford EAS, Caltech,
2005.
[VKD02] A. Venkataramani, R. Kokku, and M. Dahlin, TCP Nice: A
Mechanism for Background Transfers, Fifth USENIX Symposium on
Operating System Design and Implementation (OSDI), 2002.
[XHR04] L. Xu, K. Harfoush, and I. Rhee, Binary Increase Congestion
Control for Fast, Long Distance Networks, Infocom 2004.
[YKL01] Y. Yang, M. Kim, and S. Lam, Transient Behaviors of TCP-
friendly Congestion Control Protocols, Infocom 2001.
[ZKL04] Y. Zhang, S.-R. Kang, and D. Loguinov, Delayed Stability and
Performance of Distributed Congestion Control, ACM SIGCOMM,
August 2004.
Authors' Addresses
Sally Floyd <floyd at icir.org>
ICSI Center for Internet Research
1947 Center Street, Suite 600
Berkeley, CA 94704
USA
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Floyd [Page 24]
INTERNET-DRAFT TMRG, METRICS July 2007
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Floyd [Page 25]
