Internet Engineering Task Force                              Sally Floyd
INTERNET-DRAFT                                                    Editor
draft-irtf-tmrg-metrics-04.txt                             7 August 2006
Expires: February 2007

      Metrics for the Evaluation of Congestion Control Mechanisms

Status of this Memo

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    This document discusses the metrics to be considered in an
    evaluation of new or modified congestion control mechanisms for the
    Internet.  This includes metrics for the evaluation of new transport
    protocols, of proposed modifications to TCP, of application-level
    congestion control, and of Active Queue Management (AQM) mechanisms
    in the router.  This document is intended to be the first in a
    series of documents aimed at improving the models that we use in the
    evaluation of transport protocols.

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    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).  We are not aware of any controversies
    regarding the content of this document.

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                             Table of Contents

    1. Conventions . . . . . . . . . . . . . . . . . . . . . . . . .   4
    2. Introduction. . . . . . . . . . . . . . . . . . . . . . . . .   5
    3. Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . .   6
       3.1. Throughput, Delay, and Loss Rates. . . . . . . . . . . .   7
          3.1.1. Throughput. . . . . . . . . . . . . . . . . . . . .   7
          3.1.2. Delay . . . . . . . . . . . . . . . . . . . . . . .   8
          3.1.3. Packet Loss Rates . . . . . . . . . . . . . . . . .   8
       3.2. Response Times and Minimizing Oscillations . . . . . . .   9
          3.2.1. Response to Changes . . . . . . . . . . . . . . . .   9
          3.2.2. Minimizing Oscillations . . . . . . . . . . . . . .  10
       3.3. Fairness and Convergence . . . . . . . . . . . . . . . .  11
       3.4. Robustness for Challenging Environments. . . . . . . . .  13
       3.5. Robustness to Failures and to Misbehaving
       Users . . . . . . . . . . . . . . . . . . . . . . . . . . . .  14
       3.6. Deployability. . . . . . . . . . . . . . . . . . . . . .  14
       3.7. Metrics for Specific Types of Transport. . . . . . . . .  15
       3.8. User-Based Metrics . . . . . . . . . . . . . . . . . . .  15
    4. Metrics in the IP Performance Metrics (IPPM) Working
    Group. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  15
    5. Comments on Methodology . . . . . . . . . . . . . . . . . . .  15
    6. Security Considerations . . . . . . . . . . . . . . . . . . .  16
    7. IANA Considerations . . . . . . . . . . . . . . . . . . . . .  16
    8. Acknowledgements. . . . . . . . . . . . . . . . . . . . . . .  16
    Informative References . . . . . . . . . . . . . . . . . . . . .  16
    Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . .  19
    Full Copyright Statement . . . . . . . . . . . . . . . . . . . .  20
    Intellectual Property. . . . . . . . . . . . . . . . . . . . . .  20

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1.  Conventions

    The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
    document are to be interpreted as described in [RFC 2119].


    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:

    * 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:

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    * 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 Seam 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
      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.

2.  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.

    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

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    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,
    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.
    Subsequent documents are also planned on the methodologies in using
    these sets of scenarios.

    This is work from the Transport Modeling Research Group (TMRG) in
    the IRTF (Internet Research Task Force).

3.  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.

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    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
    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 more appropriate
    methodology for using each general metric.

    For some of the metrics, such as fairness between flows, 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.

3.1.  Throughput, Delay, and Loss Rates

    Because of the clear tradeoffs between throughput, delay, and loss
    rates, it can be useful to consider the three metrics together.

    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.

3.1.1.  Throughput

    Throughput can be measured as a router-based metric of aggregate
    link throughput, 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 or flow throughput 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 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,

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    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 [QuickStart].

    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.

3.1.2.  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 includes the possible delay of retransmitting a lost

    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

3.1.3.  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
    Congestion Notification, RFC 3168) 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 [RFC 3448].

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    Some users might care about packet loss rates only in so far as they
    affect 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 [RFC 3611].  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).

    In some cases it is useful to distinguish between packets dropped at
    routers due to congestion, and packets lost in the network due to

    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.

3.2.  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.

3.2.1.  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

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    both mobility and of the heterogenity 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 [RFC 3448], 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].
    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 congested 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.

3.2.2.  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
    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

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    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, [HKLRX05] 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

    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 above.

3.3.  Fairness and Convergence

    Another set of metrics are those of fairness and of convergence
    times.  Fairness can be considered between flows of the same
    protocol, and between flows using different protocols (e.g.,
    fairness between TCP and a new transport protocol).

    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].

    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.

    Epsilon-fairness: A metric related to max-min fairness is epsilon-
    fairness, where a rate allocation is defined as epsilon-fair if

       min_i x_i / max_i x_i >= 1 - epsilon.

    where x_i is the resource allocation to the i-th user.  Epsilon-
    fairness measures the worst-case ratio between any two throughput
    rates [ZKL04].

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    Proportional fairness: In contrast, an 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].

    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

    In [CRM05], Colussi et al. propose a new definition of 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

    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

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    links [F91].

    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).

    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 [FK04],
    with [FK05] 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 flows.  Convergence times also concern the time for
    convergence to fairness between two existing flows after a sudden
    change such as a change in link capacity on 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]. '

3.4.  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, variable bandwidth, mobility) as well as
    new metrics to be considered (e.g., energy consumption).

    Robustness for challenging environments: Robustness needs to be

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    explored for paths with reordering, corruption, variable bandwidth,
    asymmetric routing, router configuration changes, mobility, and the
    like.  In general, Internet architecture has valued robustness over
    efficiency, e.g., when there are tradeoffs between robustness and
    the throughput, delay, and fairness metrics described above.

    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 is defined as the fraction of useful data from all of
    the data delivered.  High goodput indicates an efficient use of the
    radio spectrum and lower interference to other users [GF04].

3.5.  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,

3.6.  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);
    * Only at the receiver (e.g., delayed acknowledgements in TCP);
    * Both the sender and receiver (e.g., SACK TCP);
    * At a single router (e.g., RED);
    * All of the routers along the end-to-end path;
    * Both end nodes and all routers along the path (e.g., XCP).

    Another deployability issue concerns the complexity of the code.
    Roughly how many lines of code are required to implement the
    mechanism in software?  Is floating point math required?  We note
    that we don't suggest these questions as ways to reduce the

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    deployability metric to a single number; we suggest them as issues
    that could be considered in evaluating the deployability of a
    proposed congestion control mechanism.

3.7.  Metrics for Specific Types of Transport

    In some cases modified metrics are needed for evaluting transport
    protocols intended for QoS-enabled environments or for below-best-
    effort traffic [VKD02] [KK03].

3.8.  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).

4.  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, delay
    and loss, delay variation, loss patterns, packet reordering, bulk
    transfer capacity, and link capacity.  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, particularly for specific issues of how to measure these
    metrics in simulations or in testbeds.

5.  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.

    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

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    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

    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].

6.  Security Considerations

    There are no security considerations in this document.

7.  IANA Considerations

    There are no IANA considerations in this document.

8.  Acknowledgements

    Thanks to Armando Caro, Dah Ming Chiu, Dado Colussi, Wesley Eddy,
    Nelson Fonseca, Janardhan Iyengar, Doug Leith, Saverio Mascolo, Sean
    Moore, Injong Rhee, Andras Veres, and Damon Wischik, and members of
    the Transport Modeling Research Group for feedback and

Informative References

    [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.

    [BJ81] K. Bharath-Kumar and J. Jeffrey, A New Approach to
        Performance-Oriented Flow Control, IEEE Transactions on
        Communications, Vol.COM-29 N.4, April 1981.

    [CRM05] A New Approach to TCP-Fairness, Report C-2005-49, University
        of Helsinki, Finland, 2005.

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    [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.

    [FK05] S. Floyd and E. Kohler, TFRC for Voice: the VoIP Variant,
        draft-ietf-dccp-tfrc-voip-02.txt, internet draft, work in
        progress, July 2005.

    [FHP00] S. Floyd, M. Handley, and J. Padhye, A Comparison of
        Equation-Based and AIMD Congestion Control, May 2000.   URL

    [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.

    [FK04] S. Floyd and J. Kempf, IAB Concerns Regarding Congestion
        Control for Voice Traffic in the Internet, RFC 3714, March 2004.

    [FK02] S. Floyd and E. Kohler, Internet Research Needs Better
        Models, Hotnets-I. October 2002.

    [GF04] A. Gurtov and S. Floyd, Modeling Wireless Links for Transport
        Protocols, ACM CCR, 34(2):85-96, April 2004.

    [HKLRX05] S. Ha, Y. Kim, L. Le, I. Rhee, and L. Xu, A Step Toward
        Realistic Evaluation of High-speed TCP Protocols, technnical
        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

    [J91] R. Jain, The Art of Computer Systems Performance Analysis:
        Techniques for Experimental Design, Measurement, Simulation, and
        Modeling, John Wiley & Sons, 1991.

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    [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.

    [HKLRX05] S. Ha, Y. Kim, L. Le, I. Rhee and L. Xu, A Step toward
        Realistic Performance Evaluation of High-Speed TCP Variants,

    [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.

    [LLS05] Y-T. Li, D. Leith, and R. Shorten, Experimental Evaluation
        of TCP Protocols for High-Speed Networks, Hamilton Institute,
        2005.  URL "".

    [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,

    [QuickStart] Quick-Start Web Page, URL

    [RFC 2119] S. Bradner. Key Words For Use in RFCs to Indicate
        Requirement Levels. RFC 2119.

    BIBREF RFC3168 "RFC 3168" K Ramakrishnan, S. Floyd, and D. Black,
        The Addition of Explicit Congestion Notification (ECN) to IP,
        RFC 3168, September 2001.

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    [RFC 3448] M. Handley, S. Floyd, J. Padhye, and J. Widmer, TCP
        Friendly Rate Control (TFRC): Protocol Specification, RFC 3448,
        Proposed Standard, January 2003.

    [RFC 3611] T. Friedman, R. Caceres, and A. Clark, RTP Control
        Protocol Extended Reports (RTCP XR), RFC 3611, November 2003.

    [RX05] I. Rhee and L. Xu, CUBIC: A New TCP-Friendly High-Speed TCP
        Variant, PFLDnet 2005.

    [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] 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.

    [WCL05] D. X. Wei, P. Cao and S. H. Low, Time for a TCP Benchmark
        Suite?, Technical Report, Caltech CS, Stanford EAS, Caltech,

    [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.

    [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 <>
    ICSI Center for Internet Research
    1947 Center Street, Suite 600
    Berkeley, CA 94704

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