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Versions: 00 01 02 03 04 05 06 07 08 RFC 6077

   Network Working Group                                  Michael Welzl
   Internet Draft                                 Dimitri Papadimitriou
                                                                Editors

                                                         Michael Scharf
                                                            Bob Briscoe

   Expires: February 4, 2009                             August 5, 2008


            Open Research Issues in Internet Congestion Control

      draft-irtf-iccrg-welzl-congestion-control-open-research-02.txt


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Copyright Notice

   Copyright (C) The IETF Trust (2008).


Abstract

   This document describes some of the open problems in Internet
   congestion control that are known today. This includes several new


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   challenges that are becoming important as the network grows, as well
   as some issues that have been known for many years. These challenges
   are generally considered to be open research topics that may require
   more study or application of innovative techniques before Internet-
   scale solutions can be confidently engineered and deployed.


Conventions used in this document

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC-2119 [i].


Table of Contents

   1. Introduction...................................................3
   2. Global Challenges..............................................4
      2.1 Heterogeneity..............................................4
      2.2 Stability..................................................6
      2.3 Fairness...................................................7
   3. Detailed Challenges............................................8
      3.1 Challenge 1: Router Support................................8
      3.2 Challenge 2: Corruption Loss..............................12
      3.3 Challenge 3: Small Packets................................14
      3.4 Challenge 4: Pseudo-Wires.................................18
      3.5 Challenge 5: Multi-domain Congestion Control..............20
      3.6 Challenge 6: Precedence for Elastic Traffic...............21
      3.7 Challenge 7: Misbehaving Senders and Receivers............22
      3.8 Other challenges..........................................23
   4. Security Considerations.......................................25
   5. Contributors..................................................26
   6. References....................................................26
   6.1 Normative References.........................................26
      Acknowledgments...............................................32
















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1. Introduction

   This document describes some of the open research topics in the
   domain of Internet congestion control that are known today. We begin
   by reviewing some proposed definitions of congestion and congestion
   control based on current understandings.

   Congestion can be defined as the reduction in utility due to overload
   in networks that support both spatial and temporal multiplexing, but
   no reservation [Keshav07]. Congestion control is a (typically
   distributed) algorithm to share network resources among competing
   traffic sources. Two components of distributed congestion control
   have been defined in the context of prima-dual modeling [Kelly98].
   Primal congestion control refers to the algorithm executed by the
   traffic sources algorithm for controlling their sending rates or
   window sizes. This is normally a closed-loop control, where this
   operation depends on feedback. TCP algorithms fall in this category.
   Dual congestion control is implemented by the routers through
   gathering information about the traffic traversing them. A dual
   congestion control algorithm updates, implicitly or explicitly, a
   congestion measure and sends it back, implicitly or explicitly, to
   the traffic sources that use that link. Queue management algorithms
   such as Random Early Detection (RED) [Floyd93] or Random Exponential
   Marking (REM) [Ath01] fall in the "dual" category.

   Congestion control provides for a fundamental set of mechanisms for
   maintaining the stability and efficiency of the Internet. Congestion
   control has been associated with TCP since Van Jacobson's work in
   1988, but there is also congestion control outside of TCP (e.g. for
   real-time multimedia applications, multicast, and router-based
   mechanisms). The Van Jacobson end-to-end congestion control
   algorithms [Jacobson88] [RFC2581] are used by the Internet transport
   protocol TCP [RFC4614]. They have been proven to be highly successful
   over many years but have begun to reach their limits, as the
   heterogeneity of both the data link and physical layer and
   applications are pulling TCP congestion control (which performs
   poorly as the bandwidth or delay increases) outside of its natural
   operating regime. A side effect of these deficits is that there is an
   increasing share of hosts that use non-standardized congestion
   control enhancements (for instance, many Linux distributions have
   been shipped with "CUBIC" as default TCP congestion control
   mechanism).

   While the original Jacobson algorithm requires no congestion-related
   state in routers, more recent modifications have departed from the
   strict application of the end-to-end principle [Saltzer84]. Active
   Queue Management (AQM) in routers, e.g., RED and its variants such as
   xCHOKE [Pan00], RED with In/Out (RIO) [Clark98], improves performance
   by keeping queues small (implicit feedback via dropped packets),


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   while Explicit Congestion Notification (ECN) [Floyd94] [RFC3168]
   passes one bit of congestion information back to senders when an AQM
   would normally drop a packet. These measures do improve performance,
   but there is a limit to how much can be accomplished without more
   information from routers. The requirement of extreme scalability
   together with robustness has been a difficult hurdle to accelerating
   information flow. Primal-Dual TCP/AQM distributed algorithm stability
   and equilibrium properties have been extensively studied (cf. [Low02]
   [Low03]).

   Congestion control includes many new challenges that are becoming
   important as the network grows in addition to the issues that have
   been known for many years. These are generally considered to be open
   research topics that may require more study or application of
   innovative techniques before Internet-scale solutions can be
   confidently engineered and deployed. In what follows, an overview of
   some of these challenges is given.

2. Global Challenges

   This section describes the global challenges to be addressed in the
   domain of Internet congestion control.

2.1 Heterogeneity

   The Internet encompasses a large variety of heterogeneous IP networks
   that are realized by a multitude of technologies, which result in a
   tremendous variety of link and path characteristics: capacity can be
   either scarce in very slow speed radio links (several kbps), or there
   may be an abundant supply in high-speed optical links (several
   gigabit per second). Concerning latency, scenarios range from local
   interconnects (much less than a millisecond) to certain wireless and
   satellite links with very large latencies (up to a second). Even
   higher latencies can occur in interstellar communication.  As a
   consequence, both the available bandwidth and the end-to-end delay in
   the Internet may vary over many orders of magnitude, and it is likely
   that the range of parameters will further increase in future.

   Additionally, neither the available bandwidth nor the end-to-end
   delay is constant. At the IP layer, competing cross-traffic, traffic
   management in routers, and dynamic routing can result in sudden
   changes of the characteristics of an end-to-end path. Additional
   dynamics can be caused by link layer mechanisms, such as shared media
   access (e.g., in wireless networks), changes of links
   (horizontal/vertical handovers), topology modifications (e.g., in
   ad-hoc networks), link layer error correction and dynamic bandwidth
   provisioning schemes. From this follows that path characteristics can
   be subject to substantial changes within short time frames.



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   The congestion control algorithms have to deal with this variety in
   an efficient way. The congestion control principles introduced by Van
   Jacobson assume a rather static scenario and implicitly target
   configurations where the bandwidth-delay product is of the order of
   some dozens of packets at most. While these principles have proved to
   work well in the Internet for almost two decades, much larger
   bandwidth-delay products and increased dynamics challenge them more
   and more. There are many situations where today's congestion control
   algorithms react in a suboptimal way, resulting in low resource
   utilization, non-optimal congestion avoidance, or unfairness.

   This gave rise to a multitude of new proposals for congestion control
   algorithms. For instance, since the Additive-Increase Multiplicative
   Decrease (AIMD) behavior of TCP is too conservative in practical
   environments when then congestion window is large, several high-speed
   congestion control extensions have been developed. However, these new
   algorithms raise fairness issues, and they may be less robust in
   certain situations for which they have not been designed. Up to now,
   there is still no common agreement in the IETF on which algorithm and
   protocol to choose.

   It is always possible to tune congestion control parameters based on
   some knowledge about the environment and the application scenario.
   However, the fundamental question is whether it is possible to define
   one congestion control mechanism that operates reasonable well in the
   whole range of scenarios that exist in the Internet. Hence, it is an
   important research question how new Internet congestion control
   mechanisms would have to be designed, which maximum degree of
   dynamics it could efficiently handle, and whether it could keep the
   genererality of the existing end-to-end solutions.

   Some improvements of congestion control could be realized by simple
   changes of single functions in end-system or network components.
   However, new mechanism can also require a fundamental redesign of the
   overall network architecture, and they may even affect the design of
   Internet applications. This can imply significant interoperability
   and backward compatibility challenges and/or create network
   accessibility obstacles. In particular, networks and/or applications
   that do not use or support a new congestion control mechanism could
   be penalized by a significantly worse performance compared to what
   they would get if everybody used the existing mechanisms (cf. the
   discussion on fairness in Section 2.3). [RFC5033] defines several
   criteria to evaluate the appropriateness of a new congestion control
   mechanism. However, the fundamental question is how much performance
   deterioration is acceptable for "legacy" applications. This tradeoff
   between performance and cost has to be very carefully examined for
   all new congestion control schemes.




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2.2 Stability

   Control theory, which is a mathematical tool for describing dynamic
   systems, lends itself to modeling congestion control - TCP is a
   perfect example of a typical "closed loop" system that can be
   described in control theoretic terms. In control theory, there is a
   mathematically defined notion of system stability. In a stable
   system, for any bounded input over any amount of time, the output
   will also be bounded. For congestion control, what is actually meant
   with stability is typically asymptotic stability: a mechanism should
   converge to a certain state irrespective of the initial state of the
   network.

   Control theoretic modeling of a realistic network can be quite
   difficult, especially when taking distinct packet sizes and
   heterogeneous RTTs into account. It has therefore become common
   practice to model simpler cases and leave the more complicated
   (realistic) situations for simulations. Clearly, if a mechanism is
   not stable in a simple scenario, it is generally useless; this method
   therefore helps to eliminate faulty congestion control candidates at
   an early stage.

   Some fundamental facts, which are known from control theory are
   useful as guidelines when designing a congestion control mechanism.
   For instance, a controller should only be fed a system state that
   reflects its output. A (low-pass) filter function should be used in
   order to pass only states to the controller that are expected to last
   long enough for its action to be meaningful [Jain88]. Action should
   be carried out whenever such feedback arrives, as it is a fundamental
   principle of control that the control frequency should be equal to
   the feedback frequency. Reacting faster leads to oscillations and
   instability while reacting slower makes the system tardy [Jain90].

   TCP stability can be attributed to two key aspects which were
   introduced in [Jacobson88]: the AIMD control law during congestion
   avoidance, which is based on a simple, vector based analysis of two
   controllers sharing one resource with synchronous RTTs [Chiu89], and
   the "conservation of packets principle", which, once the control has
   reached "steady state", tries to maintain an equal amount of packets
   in flight at any time by only sending a packet into the network when
   a packet has left the network (as indicated by an ACK arriving at the
   sender). The latter aspect has guided many decisions regarding
   changes that were made to TCP over the years.

   The reasoning in [Jacobson88] assumes all senders to be acting at the
   same time. The stability of TCP under more realistic network
   conditions has been investigated in a large number of ensuing works,
   leading to no clear conclusion that TCP would also be asymptotically
   stable under arbitrary network conditions. The stability impact of


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   Slow Start (which can be significant as short-lived HTTP flows often
   never leave this phase) is also not entirely clear.

2.3 Fairness

   Recently, the way the Internet community reasons about fairness has
   been called into deep questioning [Bri07]. Much of the community has
   taken fairness to mean approximate equality between the rates of
   flows (flow rate fairness) that experience equivalent path congestion
   as with TCP [RFC2581] and TFRC [RFC3448]. [RFC3714] depicts the
   resulting situation as "The Amorphous Problem of Fairness".

   A parallel tradition has been built on [Kelly98] where, as long as
   each user is accountable for the cost their rate causes to others
   [MKMV95], the set of rates that everyone chooses is deemed fair (cost
   fairness) - because with any other set of choices people would lose
   more value than they gained overall.

   In comparison, the debate between max-min, proportional and TCP
   fairness is about mere details. These three all share the assumption
   that equal flow rates are desirable; they merely differ in the second
   order issue of how to share out excess capacity in a network of many
   bottlenecks. In contrast, cost fairness should lead to extremely
   unequal flow rates by design. Equivalently, equal flow rates would
   typically be considered extremely unfair.

   The two traditional approaches are not protocol options that can each
   be followed in different parts of a network. They result in research
   agendas and issues that are different in their respective objectives
   resulting in different set of open issues.

   If we assume TCP-friendliness as a goal with flow rate as the metric,
   open issues would be:

   - Should rate fairness depend on the packet rate or the bit rate?
   - Should the flow rate depend on RTT (as in TCP) or should only flow
     dynamics depend on RTT (e.g. as in Fast TCP [Jin04])?
   - How to estimate whether a particular flow start strategy is fair,
     or whether a particular fast recovery strategy after a reduction in
     rate due to congestion is fair?
   - How should we judge what is reasonably fair if an application
     needs, for example, even smoother flows than TFRC, or it needs to
     burst occasionally, or with any other application behavior?
   - During brief congestion bursts (e.g. due to new flow arrivals) how
     to judge at what point it becomes unfair for some flows to continue
     at a smooth rate while others reduce their rate?
   - Which mechanism(s) should be used to enforce approximate flow rate
     fairness?
   - How can we introduce some degree of fairness that takes account of


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     flow duration?
   - How to judge the fairness of applications using a large number of
     flows over separate paths (e.g., via an overlay)?

   If we assume cost fairness as a goal with congestion volume as the
   metric, open issues would be:

   - Can one application's sensitivity to instantaneous congestion
     really be protected by longer-term accountability of competing
     applications?
   - Which protocol mechanism(s) should give accountability for causing
     congestion?
   - How to design one or two generic transport protocols (such as to
     TCP, UDP, etc.) with the addition of application policy control?
   - Which policy enforcement should be used by networks and which
     interactions between application policy and network policy
     enforcement?
   - How to design a new policy enforcement framework that will
     appropriately compete with existing flows aiming for rate equality
     (e.g. TCP)?

   The question of how to reason about fairness is a pre-requisite to
   agreeing on the research agenda. However, that question does not
   require more research in itself, it is merely a debate that needs to
   be resolved by studying existing research and by assessing how bad
   fairness problems could become if they are not addressed rigorously.

3. Detailed Challenges

3.1 Challenge 1: Router Support

   Routers can be involved in congestion control in two ways: first,
   they can implicitly optimize their functions, such as queue
   management and scheduling strategies, in order to support the
   operation of an end-to-end congestion control. Second, routers can
   participate in congestion control via explicit notification
   mechanisms.

   In the first category, various approaches have been proposed and also
   deployed, such as different AQM techniques. Even though these
   implicit techniques are known to improve network performance during
   congestion phases, they are still only partly deployed in the
   Internet. This may be due to the fact that finding optimal and robust
   parameterizations for these mechanisms is a non-trivial problem.
   Indeed, the problem with various AQM schemes is the difficulty to
   identify correct values of the parameter set that affects the
   performance of the queuing scheme (due to variation in the number of
   sources, the capacity and the feedback delay) [Fioriu00] [Hollot01]
   [Zhang03]. Many AQM schemes (RED, REM, BLUE, PI-Controller but also


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   Adaptive Virtual Queue (AVQ)) do not define a systematic rule for
   setting their parameters.

   By using explicit feedback from the network, connection endpoints can
   obtain more accurate information about the current network
   characteristics on the path. This allows endpoints to make more
   precise decisions that can better prevent packet loss and that can
   also improve fairness among different flows. Examples for explicit
   router feedback include Explicit Congestion Notification (ECN)
   [RFC3168], Quick-Start [RFC4782], the eXplicit Control Protocol (XCP)
   [Katabi02] [Falk07], the Rate Control Protocol (RCP) [Dukk06], and
   CADPC/PTP [Welzl03].

   Explicit router feedback can address some of the inherent
   shortcomings of TCP. For instance, XCP has been developed to overcome
   the inefficiency, unfairness and instability that TCP suffers from
   when the per-flow bandwidth-delay product increases. By decoupling
   resource utilization/congestion control from fairness control, XCP
   achieves fair bandwidth allocation, high utilization, a small
   standing queue size, and near-zero packet drops, with both steady and
   highly varying traffic. Importantly, XCP does not maintain any per-
   flow state in routers and requires few CPU cycles per packet, hence
   making it potentially applicable in high-speed routers. However, XCP
   is still subject to research: as [Andrew05] has pointed out, XCP is
   locally stable but globally unstable when the maximum RTT of a flow
   is much larger than the mean RTT. This instability can be removed by
   changing the update strategy for the estimation interval, but this
   makes the system vulnerable to erroneous RTT advertisements. The
   authors of [PAP02] have shown that, when flows with different RTTs
   are applied, XCP sometimes discriminates among heterogeneous traffic
   flows, even if XCP is generally fair to different flows. [Low05]
   provides for a complete characterization of the XCP equilibrium
   properties.

   Several other explicit router feedback schemes have been developed
   with different design objectives. For instance, RCP uses a per-packet
   feedback similar to XCP. Different to XCP, RCP focuses on the
   reduction of flow completion times and therefore tolerates larger
   instantaneous queue sizes [Dukk06].

   Both implicit and explicit router support should be considered in the
   context of the end-to-end argument [Saltzer84], which is one of the
   key design principles of the Internet. It suggests that functions
   that can be realized both in the end-systems and in the network
   should be implemented in the end-systems. This principle ensures that
   the network provides a general service and that remains as simple as
   possible (any additional complexity is placed above the IP layer,
   i.e., at the edges) so as to ensure reliability and robustness. In
   particular, this means that Internet protocols should not rely on the


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   maintenance of applicative state (i.e., information about the state
   of the end-to-end communication) inside the network [RFC1958] and
   that the network state (e.g. routing state) maintained by the
   Internet shall minimize its interaction with the states maintained at
   the end-points/hosts.

   However, as discussed for instance in [Moors02], congestion control
   cannot be realized as a pure end-to-end function only. Congestion is
   an inherent network phenomenon and can only be resolved efficiently
   by some cooperation of end-systems and the network. Congestion
   control in today's Internet protocols follows the end-to-end design
   principle insofar as only minimal feedback from the network is used
   (e. g., packet loss and delay). The end-systems only decide how to
   react and how to avoid congestion. The crux is that, on the one hand,
   there would be substantial benefit by further assistance from the
   network, but, on the other hand, such router support could lead to
   duplication of functions, which might even harmfully interact with
   end-to-end protocol mechanisms. The different requirements of
   applications (cf. the fairness discussion in Section 2.3) call for a
   variety of different congestion control approaches, but putting such
   application-specific behavior inside the network should be avoided,
   as such design would clearly be at odds with the end-to-end design
   principle.

   The end-to-end argument is generally regarded as a key ingredient for
   ensuring a scalable network design. In order to ensure that new
   congestion control mechanisms are scalable, violating this principle
   must therefore be avoided.

   In general, router support raises many issues that have not been
   completely solved yet.

3.1.1 Performance and robustness

   Congestion control is subject to some tradeoffs: on one hand, it must
   allow high link utilizations and fair resource sharing but on the
   other hand, the algorithms must also be robust and conservative in
   particular during congestion phases.

   Router support can help to improve performance and fairness, but it
   can also result in additional complexity and more control loops. This
   requires a careful design of the algorithms in order to ensure
   stability and avoid e.g. oscillations. A further challenge is the
   fact that information may be imprecise. For instance, severe
   congestion can delay feedback signals. Also, the measurement of
   parameters such as RTTs or data rates may contain estimation errors.
   Even though there has been significant progress in providing
   fundamental theoretical models for such effects, research has not
   completely explored the whole problem space yet.


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   Open questions are:

   - How much can routers theoretically improve performance in the
     complete range of communication scenarios that exists in the
     Internet without damaging or impacting end-to-end mechanisms
     already in place?

   - Is it possible to design robust mechanisms that offer significant
     benefits without additional risks?

   - What is the minimum support that is needed from routers in order
     to achieve significantly better performance than with end-to-end
     mechanisms?

3.1.2 Granularity of router functions

   There are several degrees of freedom concerning router involvement,
   ranging from some few additional functions in network management
   procedures one the one end, and additional per packet processing on
   the other end of the solution space. Furthermore, different amounts
   of state can be kept in routers (no per-flow state, partial per-flow
   state, soft state, hard state). The additional router processing is a
   challenge for Internet scalability and could also increase end-to-end
   latencies.

   There are many solutions that do not require per-flow state and thus
   do not cause a large processing overhead. However, scalability issues
   could also be caused, for instance, by synchronization mechanisms for
   state information among parallel processing entities, which are e. g.
   used in high-speed router hardware designs.

   Open questions are:

   - What granularity of router processing can be realized without
     affecting Internet scalability?

   - How can additional processing efforts be kept at a minimum?

3.1.3 Information acquisition

   In order to support congestion control, routers have to obtain at
   least a subset of the following information. Obtaining that
   information may result in complex tasks.

   1. Capacity of (outgoing) links

   Link characteristics depend on the realization of lower protocol
   layers. Routers do not necessarily know the link layer network


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   topology and link capacities, and these are not always constant (e.
   g., on shared wireless links). Depending on the network technology,
   there can be queues or bottlenecks that are not directly visible at
   the IP layer. Difficulties also arise when using IP-in-IP tunnels
   [RFC2003] or MPLS [RFC3031] [RFC3032]. In these cases, link
   information could be determined by cross-layer information exchange,
   but this requires link layer technology specific interfaces. An
   alternative could be online measurements, but this can cause
   significant additional network overhead.

   2. Traffic carried over (outgoing) links

   Accurate online measurement of data rates is challenging when traffic
   is bursty. For instance, measuring a "current link load" requires
   defining the right measurement interval/ sampling interval. This is a
   challenge for proposals that require knowledge e.g. about the current
   link utilization.

   3. Internal buffer statistics

   Some proposals use buffer statistics such as a virtual queue length
   to trigger feedback.  However, routers can include multiple
   distributed buffer stages that make it difficult to obtain such
   metrics.

   Open questions are: Can and should this information be made
   available, e.g., by additional interfaces or protocols?

3.1.4 Feedback signaling

   Explicit notification mechanisms can be realized either by in-band
   signaling (notifications piggybacked along with the data traffic) or
   by out-of-band signaling. The latter case requires additional
   protocols and can be further subdivided into path-coupled and path-
   decoupled approaches.

   Open questions concerning feedback signaling include:

   - At which protocol layer should the feedback signaling occur
     (IP/network layer assisted, transport layer assisted, hybrid
     solutions, shim layer, intermediate sub-layer, etc.) ?

   - What is the optimal frequency of feedback (only in case of
     congestion events, per RTT, per packet, etc.)?

3.2 Challenge 2: Corruption Loss

   It is common for congestion control mechanisms to interpret packet
   loss as a sign of congestion. This is appropriate when packets are


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   dropped in routers because of a queue that overflows, but there are
   other possible reasons for packet drops. In particular, in wireless
   networks, packets can be dropped because of corruption, rendering the
   typical reaction of a congestion control mechanism inappropriate.

   TCP over wireless and satellite is a topic that has been investigated
   for a long time [Krishnan04]. There are some proposals where the
   congestion control mechanism would react as if a packet had not been
   dropped in the presence of corruption (cf. TCP HACK [Balan01]), but
   discussions in the IETF have shown that there is no agreement that
   this type of reaction is appropriate. For instance, it has been said
   that congestion can manifest itself as corruption on shared wireless
   links, and it is questionable whether a source that sends packets
   that are continuously impaired by link noise should keep sending at a
   high rate.

   Generally, two questions must be addressed when designing congestion
   control mechanism that takes corruption into account:

   1. How is corruption detected?

   2. What should be the reaction?

   In addition to question 1 above, it may be useful to consider
   detecting the reason for corruption, but this has not yet been done
   to the best of our knowledge.

   Corruption detection can be done using an in-band or out-of-band
   signaling mechanism, much in the same way as described for
   Challenge 1. Additionally, implicit detection can be considered: link
   layers sometimes retransmit erroneous frames, which can cause the
   end-to-end delay to increase - but, from the perspective of a sender
   at the transport layer, there are many other possible reasons for
   such an effect.

   Header checksums provide another implicit detection possibility: if a
   checksum only covers all the necessary header fields and this
   checksum does not show an error, it is possible for errors to be
   found in the payload using a second checksum. Such error detection is
   possible with UDP-Lite and DCCP; it was found to work well over a
   GPRS network in a study [Chester04] and poorly over a WiFi network in
   another study [Rossi06] [Welzl08]. Note that, while UDP-Lite and DCCP
   enable the detection of corruption, the specifications of these
   protocols do not foresee any specific reaction to it for the time
   being.

   The idea of having a transport endpoint detect and accordingly react
   to corruption poses a number of interesting questions regarding
   cross-layer interactions. As IP is designed to operate over arbitrary


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   link layers, it is therefore difficult to design a congestion control
   mechanism on top of it, which appropriately reacts to corruption -
   especially as the specific data link layers that are in use along an
   end-to-end path are typically unknown to entities at the transport
   layer.

   While the IETF has not yet specified how a congestion control
   mechanism should react to corruption, proposals exist in the
   literature. For instance, TCP Westwood sets the congestion window
   equal to the measured bandwidth at time of congestion in response to
   three DupACKs or a timeout. This measurement is obtained by counting
   and filtering the ACK rate. This setting provides a significant
   goodput improvement in noisy channels because the "blind" by half
   window reduction of standard TCP is avoided, i.e. the window is not
   reduced by too much [Mascolo01].

   Open questions concerning corruption loss include:

   - How should corruption loss be detected?

   - How should a source react when it is known that corruption has
     occurred?

3.3 Challenge 3: Small Packets

   Over past years, the performance of TCP congestion avoidance
   algorithms has been extensively studied. The well known "square root
   formula" provides the performance of the TCP congestion avoidance
   algorithm for TCP Reno [RFC2581]. [Padhye98] enhances the model to
   account for timeouts, receiver window, and delayed ACKs.

   For the sake of the present discussion, we will assume that the TCP
   throughput is expressed using the simplified formula. Using this
   formula, the TCP throughput is proportional to the packet size and
   inversely proportional to the RTT and the square root of the drop
   probability:

                      MSS   1
                B ~ C --- -------
                      RTT sqrt(p)

   where,

         MSS is the TCP segment size (in bytes)
         RTT is the end-to-end round trip time of the TCP connection (in
         seconds)
         p is the packet drop probability




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   Neglecting the fact that the TCP rate linearly depends on it,
   choosing the ideal packet size is a trade-off between high throughput
   (the larger a packet, the smaller the relative header overhead) and
   low delay (the smaller a packet, the shorter the time that is needed
   until it is filled with data). Observing that TCP is not suited for
   applications such as streaming media (since reliable in-order
   delivery and congestion control can cause arbitrarily long delays),
   this trade-off has not usually been considered for TCP applications,
   and the influence of the packet size on the sending rate is not
   typically seen as a significant issue.

   The situation is different for the Datagram Congestion Control
   Protocol (DCCP) [RFC4340], which has been designed to enable
   unreliable but congestion-controlled datagram transmission, avoiding
   the arbitrary delays associated with TCP. DCCP is intended for
   applications such as streaming media that can benefit from control
   over the tradeoffs between delay and reliable in-order delivery.

   DCCP provides for a choice of modular congestion control mechanisms.
   DCCP uses Congestion Control Identifiers (CCIDs) to specify the
   congestion control mechanism. Three profiles are currently specified:
   - DCCP Congestion Control ID 2 (CCID 2) [RFC4341]:
     TCP-like Congestion Control. CCID 2 sends data using a close
     variant of TCP's congestion control mechanisms, incorporating a
     variant of SACK [RFC2018, RFC3517]. CCID 2 is suitable for senders
     who can adapt to the abrupt changes in congestion window typical of
     TCP's AIMD congestion control, and particularly useful for senders
     who would like to take advantage of the available bandwidth in an
     environment with rapidly changing conditions.
   - DCCP Congestion Control ID 3 (CCID 3) [RFC4342]:
     TCP-Friendly Rate Control (TFRC) [RFC3448bis] is a congestion
     control mechanism designed for unicast flows operating in a best-
     effort Internet environment. It is reasonably fair when competing
     for bandwidth with TCP flows, but has a much lower variation of
     throughput over time compared with TCP, making it more suitable for
     applications such as streaming media where a relatively smooth
     sending rate is of importance. CCID 3 is appropriate for flows that
     would prefer to minimize abrupt changes in the sending rate,
     including streaming media applications with small or moderate
     receiver buffering before playback.
   - DCCP Congestion Control ID 4 [draft-ietf-ccid4-02.txt]:
     TFRC Small Packets (TFRC-SP) [RFC4828], a variant of TFRC
     mechanism has been designed for applications that exchange small
     packets. The objective of TFRC-SP is to achieve the same bandwidth
     in bps (bits per second) as a TCP flow using packets of up to 1500
     bytes. TFRC-SP enforces a minimum interval of 10 ms between data
     packets to prevent a single flow from sending small packets
     arbitrarily frequently. TFRC is a congestion control mechanism for
     unicast flows operating in a best-effort Internet environment, and


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     is designed for DCCP that controls the sending rate based on a
     stochastic Markov model for TCP Reno. CCID 4 has been designed to
     be used either by applications that use a small fixed segment size,
     or by applications that change their sending rate by varying the
     segment size. Because CCID 4 is intended for applications that use
     a fixed small segment size, or that vary their segment size in
     response to congestion, the transmit rate derived from the TCP
     throughput equation is reduced by a factor that accounts for packet
     header size, as specified in [RFC4828].

   The resulting open questions are:
   - How does TFRC-SP operate under various network conditions?
   - How to design congestion control so as to scale with packet
     size (dependency of congestion algorithm on packet size)? Early
     assessment shows that packet size dependency should remain at
     the transport layer.

   Today, many network resources are designed so that packet processing
   cannot be overloaded even for incoming loads at the maximum bit-rate
   of the line. If packet processing can handle sustained load r [packet
   per second] and the minimum packet size is h [bit] (i.e. packet
   headers with no payload), then a line rate of x [bit per second] will
   never be able to overload packet processing as long as x <= r.h.
   However, realistic equipment is often designed to only cope with a
   near-worst-case workload with a few larger packets in the mix, rather
   than the worst-cast of all minimum size packets. In this case, x =
   r.(h + e) for some small value of e.

   Therefore, it is likely that most congestion seen on today's Internet
   is due to an excess of bits rather than packets, although packet-
   congestion is not impossible for runs of small packets (e.g. TCP ACKs
   or DoS attacks with small UDP datagrams).

   This observation raises additional open issues:

   - Will bit congestion remain prevalent?

   Being able to assume that congestion is generally due to excess bits
   not excess packets is a useful simplifying assumption in the design
   of congestion control protocols. Can we rely on this assumption into
   the future?

   Over the last three decades, performance gains have mainly been
   through increased packet rates, not bigger packets. But if bigger
   maximum segment sizes become more prevalent, tiny segments (e.g.
   ACKs) will still continue to be widely used - a widening range of
   packet sizes.




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   The open question is thus whether or not packet processing rates (r)
   will keep up with growth in transmission rates (x). A superficial
   look at Moore's Law type trends would suggest that processing (r)
   will continue to outstrip growth in transmission (x). But predictions
   based on actual knowledge of technology futures would be useful.
   Another open question is whether there are likely to be more small
   packets in the average packet mix. If the answers to either of these
   questions predict that packet congestion could become prevalent,
   congestion control protocols will have to be more complicated.

   - Confusable Causes of Drop

   There is a considerable body of research on how to distinguish
   whether packet drops are due to transmission corruption or to
   congestion. But the full list of confusable causes of drop is longer
   and includes transmission loss, congestion loss (bit congestion and
   packet congestion), and policing loss.

   If congestion is due to excess bits, the bit rate should be reduced.
   If congestion is due to excess packets, the packet rate can be
   reduced without reducing the bit rate - by using larger packets.
   However, if the transport cannot tell which of these causes led to a
   specific drop, its only safe response is to reduce the bit rate. This
   is why the Internet would be more complicated if packet congestion
   were prevalent, as reducing the bit rate normally also reduces the
   packet rate, while reducing the packet rate doesn't necessarily
   reduce the bit rate.

   Given distinguishing between transmission loss and congestion is
   already an open issue (Section 3.2), if that problem is ever solved,
   a further open issue would be whether to standardize a solution that
   distinguishes all the above causes of drop, not just two of them.

   Nonetheless, even if we find a way for network equipment to
   explicitly distinguish which sort of drop has occurred, we will never
   be able to assume that such a smart AQM solution is deployed at every
   congestible resource throughout the Internet - at every higher layer
   device like firewalls, proxies, servers and at every lower layer
   device like low-end home hubs, DSLAMs, WLAN cards, cellular base-
   stations and so on. Thus, transport protocols will always have to
   cope with drops due to unguessable causes, so we should always treat
   AQM smarts as an optimization, not a given.

   - What does a congestion notification on a packet of a certain size
   mean?

   The open issue here is whether a loss or explicit congestion mark
   should be interpreted as a single congestion event irrespective of
   the size of the packet lost or marked, or whether the strength of the


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   congestion notification is weighted by the size of the packet. This
   issue is discussed at length in [Bri08], along with other aspects of
   packet size and congestion control.

   [Bri08] makes the strong recommendation that network equipment should
   drop or mark packets with a probability independent of each specific
   packet's size, while congestion controls should respond to dropped or
   marked packets in proportion to the packet's size. This issue is
   deferred to the Transport Area Working Group.

   - Packet Size and Congestion Control Protocol Design

   If the above recommendation is correct - that the packet size of a
   congestion notification should be taken into account when the
   transport reads, not when the network writes the notification - it
   opens up a significant program of protocol engineering and re-
   engineering. Indeed, TCP does not take packet size into account when
   responding to losses or ECN. At present this is not a pressing
   problem because use of 1500B data segments is very prevalent for TCP
   and the range of alternative segment sizes is not large. However, we
   should design the Internet's protocols so they will scale with packet
   size, so an open issue is whether we should evolve TCP, or expect new
   protocols to take over.

   As we continue to standardize new congestion control protocols, we
   must then face the issue of how they should take account of packet
   size. If we determine that TCP was incorrect in not taking account of
   packet size, even if we don't change TCP, we should not allow new
   protocols to follow TCP's example in this respect. For example, as
   explained here above, the small-packet variant of TCP-friendly rate
   control (TFRC-SP [RFC4828]) is an experimental protocol that aims to
   take account of packet size. Whatever packet size it uses, it ensures
   its rate approximately equals that of a TCP using 1500B segments.
   This raises the further question of whether TCP with 1500B segments
   will be a suitable long-term gold standard, or whether we need a more
   thoroughgoing review of what it means for a congestion control to
   scale with packet size.

3.4 Challenge 4: Pseudo-Wires

   Pseudowires (PW) may carry non-TCP data flows (e.g. TDM traffic).
   Structure Agnostic TDM over Packet (SATOP) [RFC4553], Circuit
   Emulation over Packet Switched Networks (CESoPSN), TDM over IP, are
   not responsive to congestion control in a TCP-friendly manner as
   prescribed by [RFC2914]. Moreover, it is not possible to simply
   reduce the flow rate of a TDM PW when facing packet loss.

   Carrying TDM PW over an IP network poses a real problem. Indeed,
   providers can rate control corresponding incoming traffic but it may


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   not be able to detect that a PW carries TDM traffic. This can be
   illustrated with the following example.

              ...........       ............
             .           .     .
      S1 --- E1 ---      .     .
             .     |     .     .
             .      === E5 === E7 ---
             .     |     .     .     |
      S2 --- E2 ---      .     .     |
             .           .     .     |      |
              ...........      .     |      v
                               .      ----- R --->
              ...........      .     |      ^
             .           .     .     |      |
      S3 --- E3 ---      .     .     |
             .     |     .     .     |
             .      === E6 === E8 ---
             .     |     .     .
      S4 --- E4 ---      .     .
             .           .     .
              ...........       ............

             \---- P1 ---/     \---------- P2 -----


   Sources S1, S2, S3 and S4 are originating TDM over IP traffic. P1
   provider edges E1, E2, E3, and E4 are rate limiting such traffic. The
   SLA of provider P1 with transit provider P2 is such that the latter
   assumes a BE traffic pattern and that the distribution shows the
   typical properties of common BE traffic (elastic, non-real time, non-
   interactive).

   The problem arises for transit provider P2 that is not able to detect
   that IP packets are carrying constant-bit rate service traffic that
   is by definition unresponsive to any congestion control mechanisms.

   Assuming P1 providers are rate limiting BE traffic, a transit P2
   provider router R may be subject to serious congestion as all TDM PWs
   cross the same router. TCP-friendly traffic would follow TCP's AIMD
   algorithm of reducing the sending rate in half in response to each
   packet drop. Nevertheless, the TDM PWs will take all the available
   capacity, leaving no room for any other type of traffic. Note that
   the situation may simply occur because S4 suddenly turns up a TDM PW.

   As it is not possible to assume that edge routers will soon have the
   ability to detect the type of the carried traffic, it is important
   for transit routers (P2 provider) to be able to apply a fair, robust,
   responsive and efficient congestion control technique in order to


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   prevent impacting normally behaving Internet traffic. However, it is
   still an open question how the corresponding mechanisms in the data
   and control planes have to be designed.

3.5 Challenge 5: Multi-domain Congestion Control

   Transport protocols such as TCP operate over the Internet that is
   divided into autonomous systems. These systems are characterized by
   their heterogeneity as IP networks are realized by a multitude of
   technologies. The variety of conditions and their variations leads to
   correlation effects between policers that regulate traffic against
   certain conformance criteria.

   With the advent of techniques allowing for early detection of
   congestion, packet loss is no longer the sole metric of congestion.
   ECN (Explicit Congestion Notification) marks packets - set by active
   queue management techniques - to convey congestion information trying
   to prevent packet losses (packet loss and the number of packets
   marked gives an indication of the level of congestion). Using TCP
   ACKs to feed back that information allows the hosts to realign their
   transmission rate and thus encourage them to efficiently use the
   network. In IP, ECN uses the two unused bits of the TOS field
   [RFC2474]. Further, ECN in TCP uses two bits in the TCP header that
   were previously defined as reserved [RFC793].

   ECN [RFC3168] is an example of a congestion feedback mechanism from
   the network toward hosts, while the policer must sit at every
   potential point of congestion. The congestion-based feedback scheme
   however has limitations when applied on an inter-domain basis.
   Indeed, the same congestion feedback mechanism is required along the
   entire path for optimal control at end-systems.

   Another solution in a multi-domain environment may be the TCP rate
   controller (TRC), a traffic conditioner which regulates the TCP flow
   at the ingress node in each domain by controlling packet drops and
   RTT of the packets in a flow. The outgoing traffic from a TRC
   controlled domain is shaped in such a way that no packets are dropped
   at the policer. However, the TRC depends on the end-to-end TCP model,
   and thus the diversity of TCP implementations is a general problem.

   Security is another challenge for multi-domain operation. At some
   domain boundaries, an increasing number of application layer gateways
   (e. g., proxies) are deployed, which split up end-to-end connections
   and prevent end-to-end congestion control.

   Furthermore, authentication and authorization issues can arise at
   domain boundaries whenever information is exchanged, and so far the
   Internet does not have a single general security architecture that
   could be used in all cases. Many autonomous systems also only


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   exchange some limited amount of information about their internal
   state (topology hiding principle), even though having more precise
   information could be highly beneficial for congestion control. The
   future evolution of the Internet inter-domain operation has to show
   whether more multi-domain information exchange can be realized.

3.6 Challenge 6: Precedence for Elastic Traffic

   Traffic initiated by so-called elastic applications adapt to the
   available bandwidth using feedback about the state of the network.
   For all these flows the application dynamically adjusts the data
   generation rate. Examples encompass short-lived elastic traffic
   including HTTP and instant messaging traffic as well as long file
   transfers with FTP. In brief, elastic data applications can show
   extremely different requirements and traffic characteristics.

   The idea to distinguish several classes of best-effort traffic types
   is rather old, since it would be beneficial to address the relative
   delay sensitivities of different elastic applications. The notion of
   traffic precedence was already introduced in [RFC791], and it was
   broadly defined as "An independent measure of the importance of this
   datagram."

   For instance, low precedence traffic should experience lower average
   throughput than higher precedence traffic. Several questions arise
   here: what is the meaning of "relative"? What is the role of the
   Transport Layer?

   The preferential treatment of higher precedence traffic with
   appropriate congestion control mechanisms is still an open issue that
   may, depending on the proposed solution, impact both the host and the
   network precedence awareness, and thereby congestion control.
   [RFC2990] points out that interactions between congestion control and
   DiffServ [RFC2475] have yet to be addressed, and this statement is
   still valid at the time of writing.

   There is also still work to be performed regarding lower precedence
   traffic - data transfers which are useful, yet not important enough
   to significantly impair any other traffic. Examples of applications
   that could make use of such traffic are web caches and web browsers
   (e.g. for pre-fetching) as well as peer-to-peer applications. There
   are proposals for achieving low precedence on a pure end-to-end basis
   (e.g. TCP-LP [Kuzmanovic03]), and there is a specification for
   achieving it via router mechanisms [RFC3662]. It seems, however, that
   such traffic is still hardly used, and sending lower precedence data
   is not yet a common service on the Internet.





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3.7 Challenge 7: Misbehaving Senders and Receivers

   In the current Internet architecture, congestion control depends on
   parties acting against their own interests. It is not in a receiver's
   interest to honestly return feedback about congestion on the path,
   effectively requesting a slower transfer. It is not in the sender's
   interest to reduce its rate in response to congestion if it can rely
   on others to do so. Additionally, networks may have strategic reasons
   to make other networks appear congested.

   Numerous strategies to divert congestion control have already been
   identified. The IETF has particularly focused on misbehaving TCP
   receivers that could confuse a compliant sender into assigning
   excessive network and/or server resources to that receiver (e.g.
   [Sav99], [RFC3540]). But, although such strategies are worryingly
   powerful, they do not yet seem common.

   A growing proportion of Internet traffic comes from applications
   designed not to use congestion control at all, or worse, applications
   that add more forward error correction the more losses they
   experience. Some believe the Internet was designed to allow such
   freedom so it can hardly be called misbehavior. But others consider
   that it is misbehavior to abuse this freedom [RFC3714], given one
   person's freedom can constrain the freedom of others (congestion
   represents this conflict of interests). Indeed, leaving freedom
   unchecked might result in congestion collapse in parts of the
   Internet. Proportionately, large volumes of unresponsive voice
   traffic could represent such a threat, particularly for countries
   with less generous provisioning [RFC3714]. More recently, Internet
   video on demand services are becoming popular that transfer much
   greater data rates without congestion control (e.g. the peer-to-peer
   Joost service currently streams media over UDP at about 700kbps
   downstream and 220kbps upstream).

   Note that the problem is not just misbehavior driven by a selfish
   desire for more bandwidth (see Section 4).

   Open research questions resulting from these considerations are:

   - By design, new congestion control protocols need to enable one end
     to check the other for protocol compliance.
   - Provide congestion control primitives that satisfy more demanding
     applications (smoother than TFRC, faster than high speed TCPs), so
     that application developers and users do not turn off congestion
     control to get the rate they expect and need.

   Note also that self-restraint is disappearing from the Internet. So,
   it may no longer be sufficient to rely on developers/users
   voluntarily submitting themselves to congestion control. As main


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   consequence, mechanisms to enforce fairness (see Section 2.3) need to
   have more emphasis within the research agenda.

3.8 Other challenges

   This section provides additional challenges and open research issues
   that are not (at this point in time) deemed very large or of
   different nature compared to the main challenges depicted since so
   far.

   Note that this section may be complemented in future release of this
   document by topics discussed during the last ICCRG meeting, co-
   located with PFLDNet 2008 International Workshop. Topics of interest
   include multipath congestion control, and congestion control for
   multimedia codecs that only support certain set of data rates.

3.8.1 RTT estimation

   Several congestion control schemes have to precisely know the round-
   trip time (RTT) of a path. The RTT is a measure of the current delay
   on a network. It is defined as the delay between the sending of a
   packet and the reception of a corresponding response, which is echoed
   back immediately by receiver upon receipt of the packet. This
   corresponds to the sum of the one-way delay of the packet and the
   (potentially different) one-way delay of the response. Furthermore,
   any RTT measurement also includes some additional delay due to the
   packet processing in both end-systems.

   There are various techniques to measure the RTT: Active measurements
   inject special probe packets to the network and then measure the
   response time, using e.g. ICMP. In contrast, passive measurements
   determine the RTT from ongoing communication processes, without
   sending additional packets.

   The connection endpoints of reliable transport protocols such as TCP,
   SCTP, and DCCP, as well as several application protocols, keep track
   of the RTT in order to dynamically adjust protocol parameters such as
   the retransmission timeout (RTO). They can implicitly measure the RTT
   on the sender side by observing the time difference between the
   sending of data and the arrival of the corresponding
   acknowledgements. For TCP, this is the default RTT measurement
   procedure, in combination with Karn's algorithm that prohibits RTT
   measurements from retransmitted segments [RFC2988]. Traditionally,
   TCP implementations take one RTT measurement at a time (i. e., about
   once per RTT). As alternative, the TCP timestamp option [RFC1323]
   allows more frequent explicit measurements, since a sender can safely
   obtain an RTT sample from every received acknowledgment. In
   principle, similar measurement mechanisms are used by protocols other
   than TCP.


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   Sometimes it would be beneficial to know the RTT not only at the
   sender, but also at the receiver. A passive receiver can deduce some
   information about the RTT by analyzing the sequence numbers of
   received segments. But this method is error-prone and only works if
   the sender permanently sends data. Other network entities on the path
   can apply similar heuristics in order to approximate the RTT of a
   connection, but this mechanism is protocol-specific and requires per-
   connection state. In the current Internet, there is no simple and
   safe solution to determine the RTT of a connection in network
   entities other than the sender.

   As outlined earlier in this document, the round-trip time is
   typically not a constant value. For a given path, there is
   theoretical minimum value, which is given by the minimum
   transmission, processing and propagation delay on that path. However,
   additional variable delays might be caused by congestion, cross-
   traffic, shared mediums access control schemes, recovery procedures,
   or other sub-IP layer mechanisms. Furthermore, a change of the path
   (e. g., route flipping, handover in mobile networks) can result in
   completely different delay characteristics.

   Due to this variability, one single measured RTT value is hardly
   sufficient to characterize a path. This is why many protocols use RTT
   estimators that derive an averaged value and keep track of a certain
   history of previous samples. For instance, TCP endpoints derive a
   smoothed round-trip time (SRTT) from an exponential weighted moving
   average [RFC2988]. Such a low-pass filter ensures that measurement
   noise and single outliers do not significantly affect the estimated
   RTT. Still, a fundamental drawback of low-pass filters is that the
   averaged value reacts slower to sudden changes of the measured RTT.
   There are various solutions to overcome this effect: For instance,
   the standard TCP retransmission timeout calculation considers not
   only the SRTT, but also a measure for the variability of the RTT
   measurements [RFC2988]. Since this algorithm is not well-suited for
   frequent RTT measurements with timestamps, certain implementations
   modify the weight factors (e.g., [SK02]). There are also proposals
   for more sophisticated estimators, such as Kalman filters or
   estimators that utilize mainly peak values.

   However, open questions concerning RTT estimation in the Internet
   remain:

   - Optimal measurement frequency: Currently, there is no common
     understanding of the right time scale of RTT measurement. In
     particular, the necessity of rather frequent measurements
     (e.g., per packet) is not well understood. There is some empirical
     evidence that such frequent sampling may not have a significant
     benefit [Allman99].


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   - Filter design: A closely related question is how to design good
     filters for the measured samples. The existing algorithms are known
     to be robust, but they are far from being perfect. The fundamental
     problem is that there is no single set of RTT values that could
     characterize the Internet as a whole, i.e., it is hard to define a
     design target.

   - Default values: RTT estimators can fail in certain scenarios, e.
     g., when any feedback is missing. In this case, default values have
     to be used. Today, most default values are set to conservative
     values that may not be optimal for most Internet communication.
     Still, the impact of more aggressive settings is not well
     understood.

   - Clock granularities: RTT estimation depends on the clock
     granularities of the protocol stacks. Even though there is a trend
     towards higher precision timers, the limited granularity may still
     prevent highly accurate RTT estimations.

3.8.2 Malfunctioning devices

   There is a long history of malfunctioning devices harming the
   deployment of new and potentially beneficial functionality in the
   Internet. Sometimes, such devices drop packets when a certain
   mechanism is used, causing users to opt for reliability instead of
   performance and disable the mechanism, or operating system vendors to
   disable it by default. One well-known example is ECN, whose
   deployment was long hindered by malfunctioning firewalls, but there
   are many other examples (e.g. the Window Scaling option of TCP).

   As new congestion control mechanisms are developed with the intention
   of eventually seeing them deployed in the Internet, it would be
   useful to collect information about failures caused by devices of
   this sort, analyze the reasons for these failures, and determine
   whether there are ways for such devices to do what they intend to do
   without causing unintended failures. Recommendation for vendors of
   these devices could be derived from such an analysis. It would also
   be useful to see whether there are ways for failures caused by such
   devices to become more visible to endpoints, or for those failures to
   become more visible to the maintainers of such devices.

4. Security Considerations

   Misbehavior may be driven by pure malice, or malice may in turn be
   driven by wider selfish interests, e.g. using distributed denial of
   service (DDoS) attacks to gain rewards by extortion [RFC4948]. DDoS
   attacks are possible both because of vulnerabilities in operating



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   systems and because the Internet delivers packets without requiring
   congestion control.

   To date, compliance with congestion control rules and being fair
   requires end points to cooperate. The possibility of uncooperative
   behavior can be regarded as a security issue; its implications are
   discussed throughout these documents in a scattered fashion.

   Currently the focus of the research agenda against denial of service
   is about identifying attack packets, attacking machines and networks
   hosting them, with a particular focus on mitigating source address
   spoofing. But if mechanisms to enforce congestion control fairness
   were robust to both selfishness and malice [Bri06] they would also
   naturally mitigate denial of service, which can be considered (from
   the perspective of well-behaving Internet user) as a congestion
   control enforcement problem.

5. Contributors

   This document is the result of a collective effort to which the
   following people have contributed:

   Dimitri Papadimitriou <dimitri.papadimitriou@alcatel-lucent.be>
   Michael Welzl <michael.welzl@uibk.ac.at>
   Wesley Eddy <weddy@grc.nasa.gov>
   Bela Berde <bela.berde@gmx.de>
   Paulo Loureiro <loureiro.pjg@gmail.com>
   Chris Christou <christou_chris@bah.com>
   Michael Scharf <michael.scharf@ikr.uni-stuttgart.de>

6. References

6.1 Normative References

   [RFC791]   Postel, J., "Internet Protocol", STD 5, RFC 791,
              September 1981.

   [RFC793]   Postel, J., "Transmission Control Protocol", STD 7,
              RFC793, September 1981.

   [RFC896]   Nagle, J., "Congestion Control in IP/TCP", RFC 896,
              January 1984.

   [RFC1323]  Jacobson, V., Braden, R., Borman, D., "TCP Extensions for
              High Performance", RFC 1323, May 1992.

   [RFC1958]  B. Carpenter, Ed., "Architectural Principles of the
              Internet", RFC 1958, June 1996.



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   [RFC2309]  Braden, B., et al., "Recommendations on queue management
              and congestion avoidance in the Internet", RFC 2309,
              April 1998.

   [RFC2003]  Perkins, C., "IP Encapsulation within IP", RFC 1633,
              October 1996.

   [RFC2474]  Nichols, K., Blake, S. Baker, F. and D. Black,
              "Definition of the Differentiated Services Field (DS
              Field) in the IPv4 and IPv6 Headers", RFC 2474, December
              1998.

   [RFC2475]  Blake, S., Black, D., Carlson, M., Davies, E., Wang, Z.
              and W. Weiss, "An Architecture for Differentiated
              Services", RFC 2475, December 1998.

   [RFC2581]  Allman, M., Paxson, V., and W. Stevens, "TCP Congestion
              Control", RFC 2581, April 1999.

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

   [RFC2988]  Paxson, V. and Allman, M., "Computing TCP's
              Retransmission Timer", RFC 2988, November 2000.

   [RFC2990]  Huston, G., "Next Steps for the IP QoS Architecture",
              RFC 2990, November 2000.

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

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

   [RFC3540]  N. Spring, D. Wetherall, "Robust Explicit Congestion
              Notification (ECN) Signaling with Nonces", RFC 3540, June
              2003.

   [RFC3662]  Roland Bless, Kathleen Nichols, Klaus Wehrle, "A Lower
              Effort Per-Domain Behavior for Differentiated Services",
              RFC 3662, December 2003.

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

   [RFC3985]  Bryant, S. and P. Pate, "Pseudo Wire Emulation Edge-to-


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              Edge (PWE3) Architecture", RFC 3985, March 2005.

   [RFC4340]  Kohler, E., Handley, M., and S. Floyd, "Datagram
              Congestion Control Protocol (DCCP)", RFC 4340, March
              2006.

   [RFC4341]  Floyd, S. and E. Kohler, "Profile for Datagram Congestion
              Control Protocol (DCCP) Congestion Control ID 2: TCP-like
              Congestion Control", RFC 4341, March 2006.

   [RFC4342]  Floyd, S., Kohler, E., and J. Padhye, "Profile for
              Datagram Congestion Control Protocol (DCCP) Congestion
              Control ID 3: TCP-Friendly Rate Control (TFRC)", RFC
              4342, March 2006.

   [RFC4553]  Vainshtein, A. and Y. Stein, "Structure-Agnostic Time
              Division Multiplexing (TDM) over Packet (SAToP)",
              RFC 4553, June 2006.

   [RFC4614]  Duke, M., R. Braden, R., Eddy, W., and Blanton, E., "A
              Roadmap for Transmission Control Protocol (TCP)
              Specification Documents", RFC 4614, September 2006.

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

   [RFC4948]  L. Andersson, E. Davies, L. Zhang, "Report from the IAB
              workshop on Unwanted Traffic March 9-10, 2006", RFC 4948,
              August 2007.

   [RFC5033]  S. Floyd, M. Allman, "Specifying New Congestion Control
              Algorithms", RFC 5033, Aug. 2007.


6.2 Informative References

   [Allman99] Allman, M. and V. Paxson, "On Estimating End-to-End
               Network Path Properties", Proc. SIGCOMM, Sept. 99.

   [Andrew00] L. Andrew, B. Wydrowski and S. Low, "An Example of
              Instability in XCP", Manuscript available at
              <http://netlab.caltech.edu/maxnet/XCP_instability.pdf>

   [Ath01]    S. Athuraliya, S. Low, V. Li, and Q. Yin, "REM: Active
              queue management", IEEE Network Magazine, vol.15, no.3,
              pp.48-53, May 2001.

   [Balan01]  Balan, R. K., Lee, B.P., Kumar, K.R.R., Jacob, L., Seah,



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              W.K.G., Ananda, A.L., "TCP HACK: TCP Header Checksum
              Option to Improve Performance over Lossy Links",
              Proceedings of IEEE Infocom, Anchorage, Alaska, April
              2001.

   [Bonald00] T. Bonald, M. May, and J.-C. Bolot, "Analytic Evaluation
              of RED Performance", Proceedings of IEEE INFOCOM, Tel
              Aviv, Israel, March 2000.

   [Bri07]    Bob Briscoe, "Flow Rate Fairness: Dismantling a Religion"
              ACM SIGCOMM Computer Communication Review 37(2) 63--74
              (April 2007).

   [Bri06]    Bob Briscoe, "Using Self-interest to Prevent Malice;
              Fixing the Denial of Service Flaw of the Internet,"
              Workshop on the Economics of Securing the Information
              Infrastructure (Oct 2006)
              <http://wesii.econinfosec.org/draft.php?paper_id=19>

   [Chester04] Chesterfield, J., Chakravorty, R., Banerjee, S.,
              Rodriguez, P., Pratt, I. and Crowcroft, J., "Transport
              level optimisations for streaming media over wide-area
              wireless networks", WIOPT'04, March 2004.

   [Chiu89]   D. M. Chiu and R. Jain, "Analysis of the increase and
              decrease algorithms for congestion avoidance in computer
              networks", Computer Networks and ISDN Systems, vol. 17,
              pp.1-14, 1989.

   [Clark98]  D. Clark and W. Fang, "Explicit Allocation of Best-Effort
              Packet Delivery Service", IEEE/ACM Transactions on
              Networking, vol.6, no.4, pp.362-373, August 1998.

   [Dukki06]  N. Dukkipati and N. McKeown, "Why Flow-Completion Time is
              the Right Metric for Congestion Control", ACM SIGCOMM
              Computer Communication Review Volume 36, issue 1, Jan.
              2006.

   [Floyd93]  S. Floyd and V. Jacobson, "Random early detection
              gateways for congestion avoidance", IEEE/ACM Trans. on
              Networking, vol.1, no.4, pp.397-413, Aug. 1993.

   [Falk07]   A. Falk et al "Specification for the Explicit Control
              Protocol (XCP)", Work in Progress, draft-falk-xcp-spec-
              03.txt, July 2007.

   [Firoiu00] V. Firoiu and M. Borden, "A Study of Active Queue
              Management for Congestion Control," Proceedings of
              IEEE INFOCOM, Tel Aviv, Israel, March 2000.


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   [Floyd94]  S. Floyd, "TCP and Explicit Congestion Notification",
              ACM Computer Communication Review, vol.24, no.5, October
              1994, pp.10-23.

   [Hollot01] C. Hollot, V. Misra, D. Towsley, and W.-B. Gong, "A
              Control Theoretic Analysis of RED", Proceedings of
              IEEE INFOCOM, Anchorage, Alaska, April 2001.

   [Jacobson88] V. Jacobson, "Congestion Avoidance and Control", Proc.
              of the ACM SIGCOMM'88 Symposium, pp.314-329, August
              1988.

   [Jain88]   R. Jain and K. Ramakrishnan, "Congestion Avoidance in
              Computer Networks with a Connectionless Network Layer:
              Concepts, Goals, and Methodology", Proceedings of IEEE
              Computer Networking Symposium: proceedings, Sheraton
              National Hotel, Washington, DC area, April 11-13, 1988.

   [Jain90]   R. Jain, "Congestion Control in Computer Networks: Trends
              and Issues", IEEE Network, May 1990, pp.24-30, ISSN
              0890-8044.

   [Jin04]    Chen Jin, David X. Wei and Steven Low "FAST TCP:
              Motivation, Architecture, Algorithms, Performance", In
              Proc. IEEE Conference on Computer Communications
              Infocomm'04, March 2004.

   [Katabi02] D. Katabi, M. Handley, and C. Rohr, "Internet Congestion
              Control for Future High Bandwidth-Delay Product
              Environments", Proceedings of the ACM SIGCOMM'02
              Symposium, pp.89-102, August 2002.

   [Kelly98]  F. Kelly, A. Maulloo, and D. Tan, "Rate control in
              communication networks: shadow prices, proportional
              fairness, and stability", Journal of the Operational
              Research Society, vol.49, pp.237-252, 1998.

   [Keshav07] S. Keshav, "What is congestion and what is congestion
              control", Presentation at IRTF ICCRG Workshop, Pfldnet
              2007, (Los Angeles), California, February 2007.

   [Krishnan04] R. Krishnan, J. Sterbenz, W. Eddy, C. Partridge, and M.
              Allman, "Explicit Transport Error Notification (ETEN) for
              Error-Prone Wireless and Satellite Networks", Computer
              Networks, vol.46, no.3, October 2004.





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   [Kuzmanovic03] A.Kuzmanovic and E.W.Knightly, "TCP-LP: A Distributed
              Algorithm for Low Priority Data Transfer", Proceedings of
              IEEE INFOCOM 2003, San Francisco, CA, April 2003.

   [Low05]    S. Low, L. Andrew and B. Wydrowski. "Understanding XCP:
              equilibrium and fairness", Proceedings of IEEE Infocom,
              Miami, USA, March 2005.

   [Low03.2]  S. Low, F. Paganini, J. Wang, and J. Doyle, "Linear
              stability of TCP/RED and a scalable control", Computer
              Networks Journal, vol.43, no.5, pp.633-647, December
              2003.

   [Low03.1]  S. Low, "A duality model of TCP and queue management
              algorithms", IEEE/ACM Trans. on Networking, vol.11, no.4,
              pp.525-536, August 2003.

   [Low02]    S. Low, F. Paganini, J. Wang, S. Adlakha, and J. C.
              Doyle, "Dynamics of TCP/RED and a Scalable Control",
              Proceedings of IEEE Infocom, New York, USA, June 2002.

   [Mascolo01] Saverio Mascolo, Claudio Casetti, Mario Gerla, M. Y.
              Sanadidi, Ren Wang, "TCP westwood: Bandwidth estimation
              for enhanced transport over wireless links", Proceedings
              of MOBICOM 2001, pp.287-297.

   [Moors02]  T. Moors, "A critical review of End-to-end arguments in
              system design", Proc. International Conference on
              Communications (ICC), Apr./May 2002.

   [MKMV95]   MacKie-Mason, J. and H. Varian, "Pricing Congestible
              Network Resources", IEEE Journal on Selected Areas in
              Communications, Advances in the Fundamentals of
              Networking' 13(7)1141--1149, 1995, <http://
              www.sims.berkeley.edu/~hal/Papers/
              pricing-congestible.pdf>.

   [Padhye98] Padhye, J., Firoiu, V., Towsley, D., Kurose, J., Modeling
              TCP Throughput: A Simple Model and Its Empirical
              Validation, UMASS CMPSCI Tech Report TR98-008, Feb. 1998.

   [Pan00]    R. Pan, B. Prabhakar, and K. Psounis, "CHOKe: a stateless
              AQM scheme for approximating fair bandwidth allocation",
              In Proceedings of IEEE Infocom, Tel Aviv, Israel, March
              2000.

   [Rossi06]  Rossi, M., "Evaluating TCP with Corruption Notification
              in an IEEE 802.11 Wireless LAN", master thesis,
              University of Innsbruck, November 2006. Available from


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              http://www.welzl.at/research/projects/corruption/

   [Sarola02] Sarolahti, P. and Kuznetsov, A., "Congestion Control in
              Linux TCP", Proc. USENIX Annual Technical Conference,
              June 2002.

   [Savage99] Savage, S., Wetherall, D., and T. Anderson, "TCP
              Congestion Control with a Misbehaving Receiver", ACM
              SIGCOMM Computer Communication Review (1999).

   [Saltzer84] Saltzer, J., Reed, D., and Clark, D. D.
              "End-to-end arguments in system design", ACM
              Transactions on Computer Systems 2, 4 (Nov. 1984).

   [TRILOGY]  "Trilogy Project", European Commission Seventh Framework
              Program Contract Number: INFSO-ICT-216372
              <http://www.trilogy-project.org>

   [Welzl03]  M. Welzl, "Scalable Performance Signalling and Congestion
              Avoidance", Springer, August 2003. ISBN 1-4020-7570-7.

   [Welzl08]  M. Welzl, M. Rossi, A. Fumagalli, and M. Tacca, "TCP/IP
              over IEEE 802.11b WLAN: the Challenge of Harnessing
              Known-Corrupt Data", In Proceedings of IEEE ICC 2008, 19-
              23 May 2008, Beijing, China.

   [Zhang03]  H. Zhang, C. Hollot, D. Towsley, and V. Misra. "A Self-
              Tuning Structure for Adaptation in TCP/AQM Networks",
              SIGMETRICS'03, June 10-14, 2003, San Diego, California,
              USA.

Acknowledgments

   The authors would like to thank the following people whose feedback
   and comments contributed to this document: Keith Moore, Jan
   Vandenabeele.

   Larry Dunn (his comments at the Manchester ICCRG and discussions with
   him helped with the section on packet-congestibility). Bob Briscoe's
   contribution was partly funded by [TRILOGY], a research project
   supported by the European Commission.

Author's Addresses

   Michael Welzl
   University of Innsbruck
   Technikerstr 21a
   A-6020 Innsbruck, Austria
   Phone: +43 (512) 507-6110


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   Email: michael.welzl@uibk.ac.at

   Dimitri Papadimitriou
   Alcatel-Lucent
   Copernicuslaan, 50
   B-2018 Antwerpen, Belgium
   Phone: +32 3 240 8491
   Email: dimitri.papadimitriou@alcatel-lucent.be

   Michael Scharf
   University of Stuttgart
   Pfaffenwaldring 47
   D-70569 Stuttgart
   Germany
   Phone: +49 711 685 69006
   Email: michael.scharf@ikr.uni-stuttgart.de

   Bob Briscoe
   BT & UCL
   B54/77, Adastral Park
   Martlesham Heath
   Ipswich  IP5 3RE
   UK
   Email: bob.briscoe@bt.com



























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Full Copyright Statement

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Acknowledgment

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