Network Working Group S. Floyd, Ed.
Request for Comments: 5166 March 2008
Category: Informational

Metrics for the Evaluation of Congestion Control Mechanisms

Status of This Memo

   This memo provides information for the Internet community.  It does
   not specify an Internet standard of any kind.  Distribution of this
   memo is unlimited.

IESG Note

   This document is not an IETF Internet Standard.  It represents the
   individual opinion(s) of one or more members of the TMRG Research
   Group of the Internet Research Task Force.  It may be considered for
   standardization by the IETF or adoption as an IRTF Research Group
   consensus document in the future.

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 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
   (TMRG), 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 trade-offs
   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 trade-
   offs between a range of metrics, rather than in terms of optimizing
   for a single metric.

   1. Introduction ....................................................2
   2. Metrics .........................................................3
      2.1. Throughput, Delay, and Loss Rates ..........................4
           2.1.1. Throughput ..........................................5
           2.1.2. Delay ...............................................6
           2.1.3. Packet Loss Rates ...................................6
      2.2. Response Times and Minimizing Oscillations .................7
           2.2.1. Response to Changes .................................7
           2.2.2. Minimizing Oscillations .............................8
      2.3. Fairness and Convergence ...................................9
           2.3.1. Metrics for Fairness between Flows .................10
           2.3.2. Metrics for Fairness between Flows with
                  Different Resource Requirements ....................10
           2.3.3. Comments on Fairness ...............................12
      2.4. Robustness for Challenging Environments ...................13
      2.5. Robustness to Failures and to Misbehaving Users ...........14
      2.6. Deployability .............................................14
      2.7. Metrics for Specific Types of Transport ...................15
      2.8. User-Based Metrics ........................................15
   3. Metrics in the IP Performance Metrics (IPPM) Working Group .....15
   4. Comments on Methodology ........................................16
   5. Security Considerations ........................................16
   6. Acknowledgements ...............................................16
   7. Informative References .........................................17

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 trade-off 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 here.

   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.
   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
   (TMRG), 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 trade-offs
   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
   trade-offs 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 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 trade-offs between throughput, delay, and loss
   rates, it can be useful to consider these three metrics together.
   The trade-offs 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 trade-offs 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; 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.  In general, this
   document doesn't distinguish between throughput and goodput, and uses
   the general term "throughput".

   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 [DM06], 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; Voice over IP (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.  Per-packet delay can also include delay
   at the sender waiting for the transport protocol to send the packet.
   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.

   Note that queueing delay at a router is shared by all flows at a FIFO
   (First-In First-Out) queue.  Thus, the router-based queueing delay
   induced by bulk data transfers can be important even if the bulk data
   transfers themselves are not concerned with per-packet transfer
   times.

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
   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 RTP Control Protocol Extended Reports (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 trade-offs 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.

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
   bandwidth-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
   AIMD (Additive Increase Multiplicative Decrease) or for related
   mechanisms using parameters that make them more slowly-responding
   than TCP [BB01] [BBFS01].

   In addition to the responsiveness and smoothness of aggregate
   traffic, one can consider the trade-offs 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; 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
   variations can result in fluctuations in router queue size and
   therefore of queue overflows.  These queue overflows can cause loss
   synchronizations across coexisting 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 experimental 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.

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).  For the discussion of fairness metrics, let
   x_i be the throughput for the i-th connection.

   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
   it 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.  (In some 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

      (min_i x_i) / (max_i x_i) >= 1 - epsilon.

   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 number 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 reach 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 [KMT98] 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 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.

   The usefulness of flow-rate fairness has been challenged in [B07] by
   Briscoe, and defended in [FA08] by Floyd and Allman.  In [B07],
   Briscoe argues that flow-rate fairness should not be a desired goal,
   and that instead "we should judge fairness mechanisms on how they
   share out the 'cost' of each user's actions on others".  Floyd and

   Allman in [FA08] argue that the current system based on TCP
   congestion control and flow-rate fairness has been useful in the real
   world, posing minimal demands on network and economic infrastructure
   and enabling users to get a share of the network resources.

2.3.3. Comments on Fairness

   Trade-offs between fairness and throughput: The fairness measures in
   the section above generally measure both fairness and throughput,
   giving different weights to each.  Potential trade-offs 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 trade-off 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 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 affect 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,
   and 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 trade-offs 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].

   The goodput ratio: For wireless networks, the goodput ratio can be a
   useful metric, where the goodput ratio can be defined as the useful
   data delivered to users as a fraction of the total amount of data
   transmitted on the network.  A high goodput ratio 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., Selective Acknowledgment
        (SACK) TCP [RFC2018]);

      * At a single router (e.g., Random Early Detection (RED) [FJ93]);

      * All of the routers along the end-to-end path;

      * Both end-nodes and all routers along the path (e.g., Explicit
        Control Protocol (XCP) [KHR02]).

   Some congestion control mechanisms (e.g., XCP [KHR02], Quick-Start
   [RFC4782]) may also have deployment issues with IPsec, IP in IP,
   MPLS, other tunnels, or with non-router queues such as those in
   Ethernet switches.

   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 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 quality-of-service (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], and [RFC2681], delay
   variation [RFC3393], loss patterns [RFC3357], packet reordering
   [RFC4737], bulk transfer capacity [RFC3148], and link capacity
   [RFC5136].  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.

   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

   Section 2.5 discusses the robustness of congestion control mechanisms
   to failures and to misbehaving users.  Transport protocols also have
   other security concerns that are unrelated to congestion control
   mechanisms; these are not discussed in this document.

6. Acknowledgements

   Thanks to Lachlan Andrew, Mark Allman, Armando Caro, Dah Ming Chiu,
   Eric Coe, Dado Colussi, Wesley Eddy, Aaron Falk, Nelson Fonseca,
   Janardhan Iyengar, Doug Leith, Sara Landstrom, Tony Li, Saverio
   Mascolo, Sean Moore, Injong Rhee, David Ros, Juergen Schoenwaelder,
   Andras Veres, Michael Welzl, and Damon Wischik, and members of the
   Transport Modeling Research Group for feedback and contributions.

7. 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.

   [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",
             Computer Communications Review, V.37 N.2, April 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.

   [DM06]    N. Dukkipati and N. McKeown, Why Flow-Completion Time is
             the Right Metric for Congestion Control, ACM SIGCOMM,
             January 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.

   [FA08]    S. Floyd and M. Allman, Comments on the Usefulness of
             Simple Best-Effort Traffic, Work in Progress, January 2007.

   [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,

   [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", Work in Progress,
             February 2008.

   [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.

   [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] Mathis, M., Mahdavi, J., Floyd, S., and A. Romanow, "TCP
             Selective Acknowledgment Options", RFC 2018, October 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., and M. Zekauskas, "A Round-trip
             Delay Metric for IPPM", RFC 2681, September 1999.

   [RFC2914] Floyd, S., "Congestion Control Principles", BCP 41, RFC
             2914, September 2000.

   [RFC3148] Mathis, M. and M. Allman, "A Framework for Defining
             Empirical Bulk Transfer Capacity Metrics", RFC 3148, July
             2001.

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

   [RFC3357] Koodli, R. and R. Ravikanth, "One-way Loss Pattern Sample
             Metrics", RFC 3357, August 2002.

   [RFC3393] Demichelis, C. and P. Chimento, "IP Packet Delay Variation
             Metric for IP Performance Metrics (IPPM)", RFC 3393,
             November 2002.

   [RFC3448] Handley, M., Floyd, S., Padhye, J., and J. Widmer, "TCP
             Friendly Rate Control (TFRC): Protocol Specification", RFC
             3448, January 2003.

   [RFC3611] Friedman, T., Ed., Caceres, R., Ed., and A. Clark, Ed.,
             "RTP Control Protocol Extended Reports (RTCP XR)", RFC
             3611, November 2003.

   [RFC3714] Floyd, S., Ed., and J. Kempf, Ed., "IAB Concerns Regarding
             Congestion Control for Voice Traffic in the Internet", RFC
             3714, March 2004.

   [RFC3782] Floyd, S., Henderson, T., and A. Gurtov, "The NewReno
             Modification to TCP's Fast Recovery Algorithm", RFC 3782,
             April 2004.

   [RFC4737] Morton, A., Ciavattone, L., Ramachandran, G., Shalunov, S.,
             and J. Perser, "Packet Reordering Metrics", RFC 4737,
             November 2006.

   [RFC4782] Floyd, S., Allman, M., Jain, A., and P. Sarolahti, "Quick-
             Start for TCP and IP", RFC 4782, January 2007.

   [RFC4828] Floyd, S. and E. Kohler, "TCP Friendly Rate Control (TFRC):
             The Small-Packet (SP) Variant", RFC 4828, April 2007.

   [RFC5136] Chimento, P. and J. Ishac, "Defining Network Capacity", RFC
             5136, February 2008.

   [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.

   [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.

Author's Address

   Sally Floyd
   ICSI Center for Internet Research
   1947 Center Street, Suite 600
   Berkeley, CA 94704
   USA

   EMail: floyd@icir.org

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