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Network Working Group                     Dimitri Papadimitriou, Editor
Internet Draft                                           Alcatel-Lucent
Intended status: Informational                            Michael Welzl
Expires: March 02, 2011                              University of Oslo
                                                         Michael Scharf
                                                University of Stuttgart
                                                            Bob Briscoe
                                                               BT & UCL

                                                     September 01, 2010

            Open Research Issues in Internet Congestion Control


Status of this Memo

   This Internet-Draft is submitted to IETF in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF), its areas, and its working groups. Note that
   other groups may also distribute working documents as Internet-

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time. It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   The list of current Internet-Drafts can be accessed at

   The list of Internet-Draft Shadow Directories can be accessed at

   This Internet-Draft will expire on March 02, 2011.


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

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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............................................9
      3.1 Challenge 1: Network Support...............................9
           3.1.1 Performance and Robustness.........................12
           3.1.2 Granularity of network component functions.........13
           3.1.3 Information Acquisition............................14
           3.1.4 Feedback signaling.................................15
      3.2 Challenge 2: Corruption Loss..............................15
      3.3 Challenge 3: Packet Size..................................17
      3.4 Challenge 4: Flow Startup.................................21
      3.5 Challenge 5: Multi-domain Congestion Control..............23
           3.5.1 Multi-domain Transport of Explicit Congestion
           3.5.2 Multi-domain Exchange of Topology or Explicit Rate
           3.5.3 Multi-domain Pseudowires...........................25
      3.6 Challenge 6: Precedence for Elastic Traffic...............26
      3.7 Challenge 7: Misbehaving Senders and Receivers............28
      3.8 Other Challenges..........................................29
           3.8.1 RTT Estimation.....................................29
           3.8.2 Malfunctioning Devices.............................31
           3.8.3 Dependence on RTT..................................32
           3.8.4 Congestion Control in Multi-layered Networks.......32
           3.8.5 Multipath End-to-end Congestion Control and Traffic
           3.8.6 ALGs and Middleboxes...............................33
   4. Security Considerations.......................................34
   5. IANA Considerations...........................................35
   6. References....................................................36
      6.2 Informative References....................................36
   7. Acknowledgments...............................................46
   8. Author's Addresses............................................46
   9. Contributors..................................................46
   Copyright Statement..............................................47

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1. Introduction

   This document, result of the ICCRG Research Group, 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

   Congestion can be defined as a state or condition that occurs when
   network resources are overloaded resulting in impairments for network
   users as objectively measured by the probability of loss and/or of
   delay. The overload results in the reduction of utility 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 primal-dual modeling [Kelly98]. Primal congestion
   control refers to the algorithm executed by the traffic sources 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 or congestion rate 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 into 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) [RFC5783]. The Van Jacobson end-to-end congestion
   control algorithms [Jacobson88] [RFC2581] [RFC5681] 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 beyond its
   natural operating regime, because it performs poorly as the bandwidth
   or delay increases. A side effect of these deficiencies is that an
   increasing share of hosts use non-standardized congestion control
   enhancements (for instance, many Linux distributions have been
   shipped with "CUBIC" [Ha08] as the default TCP congestion control

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   While the original Van 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]
   in order to avoid congestion collapse. Active Queue Management (AQM)
   in routers, e.g., RED and some of its variants such as Adaptive RED
   (ARED), improves performance by keeping queues small (implicit
   feedback via dropped packets), while Explicit Congestion Notification
   (ECN) [Floyd94] [RFC3168] passes one bit of congestion information
   back to senders when an AQM would normally drop a packet. It is to be
   noted that other variants of RED built on AQM such as Weighted RED
   (WRED), and RED with In/Out (RIO) [Clark98] for quality enforcement
   whereas Stabilized RED (SRED), and XCHOKe [Pan00] are flow policers.
   In [Bonald00], authors analytically evaluated RED performance.

   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], [Low03.1], [Low03.2],
   [Kelly98], [Kelly05]).

   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 or over a second).
   Even higher latencies can occur in space 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.

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   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 due to mobility
   (horizontal/vertical handovers), topology modifications (e. g., in
   ad-hoc or meshed networks), link layer error correction and dynamic
   bandwidth provisioning schemes. From this, it follows that path
   characteristics can be subject to substantial changes within short
   time frames.

   Congestion control algorithms have to deal with this variety in an
   efficient and stable 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 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
   among other things in low resource utilization, and non-optimal
   congestion avoidance.

   This has resulted in 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 the congestion window is large, several
   high-speed congestion control extensions have been developed.
   However, these new algorithms may be less robust or starve legacy
   flows in certain situations for which they have not been designed. At
   the time of writing, there is no common agreement in the IETF on
   which algorithm(s) and protocol(s) to choose.

   It is always possible to tune congestion control parameters based on
   some knowledge of the environment and the application scenario.
   However, the interaction between multiple congestion control
   techniques interacting with each other is not yet well understood.
   The fundamental challenge is whether it is possible to define one
   congestion control mechanism that operates reasonably well in a
   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 they can efficiently handle, and whether they can keep the
   generality of the existing end-to-end solutions.

   Some improvements to congestion control could be realized by simple
   changes of single functions in end-system or optimizations of network
   components. However, new mechanism(s) might also require a

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   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, a key
   issue 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.

2.2 Stability

   Control theory is a mathematical tool for describing dynamic systems.
   It 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. However, control theory has had to be
   extended to model the interactions between multiple control loops in
   a network. 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 by global stability is
   typically asymptotic stability: a mechanism should converge to a
   certain state irrespective of the initial state of the network. Local
   stability means that if the system is perturbed from its stable state
   it will quickly return towards the locally stable state.

   Some fundamental facts 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 ideally be
   equal to the feedback frequency. Reacting faster leads to
   oscillations and instability while reacting slower makes the system
   tardy [Jain90].

   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 to 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

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   an early stage. However, a mechanism that is found to be stable in
   simulations can still note be safely deployed in real networks, since
   simulation scenarios make simplifying assumptions.

   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. On the other hand,
   research has concluded that stability can be assured with constraints
   on dynamics that are less stringent than the "conservation of packets
   principle". From control theory, only rate increase (not the target
   rate) needs to be inversely proportional to RTT (whereas window-based
   control converges on a target rate inversely proportional to RTT).
   A congestion control mechanism can therefore converge on a rate that
   is independent of RTT as long as its dynamics depend on RTT (e.g.
   FAST TCP [Jin04]).

   In the stability analysis of TCP and of these more modern controls,
   the impact of Slow Start on stability (which can be significant as
   short-lived HTTP flows often never leave this phase) is not entirely

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],[RFC5681] and TFRC [RFC5348]. [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.

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   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 an inter-network. They lead to
   research agendas that are different in their respective objectives,
   resulting in a different set of open issues.

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

   - Should flow fairness depend on the packet rate or the bit rate?
   - Should the target flow rate depend on RTT (as in TCP) or should
     only flow dynamics depend on RTT (e.g. as in Fast TCP [Jin04])?
   - How should we 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?
   - 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
     should we judge at what point it becomes unfair for some flows to
     continue at a smooth rate while others reduce their rate?
   - Which mechanism(s) could be used to enforce approximate flow rate
   - Should we introduce some degree of fairness that takes account of
     different users' flow activity over time?
   - How should we 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
   - Which protocol mechanism(s) are needed to give accountability for
     causing congestion?
   - How might we design one or two weighted transport protocols (such
     as TCP, UDP, etc.) with the addition of application policy control
     over the weight?
   - Which policy enforcement might be used by networks and what are
     the interactions between application policy and network policy

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   - 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. If the relevant metric is flow-rate
   it places constraints at protocol design-time, whereas if the metric
   is congestion volume the constraints move to run-time, while design-
   time constraints can be relaxed [Bri08]. 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,
   and whether we can rely on trust to maintain approximate fairness
   without requiring policing complexity [RFC5290]. The latter points
   may themselves lead to additional research. However, it is also
   accepted that more research will not necessarily lead to convince
   either side to change their opinions. More debate would be needed. It
   seems also that if the architecture is built to support cost-fairness
   then equal instantaneous cost rates for flows sharing a bottleneck
   result in flow-rate fairness; that is, flow-rate fairness can be seen
   as a special case of cost-fairness. One can be used to build the
   other, but not vice-versa.

3. Detailed Challenges

3.1 Challenge 1: Network Support

   This challenge is perhaps the most critical to get right. Changes to
   the balance of functions between the endpoints and network equipment
   could require a change to the per-datagram data plane interface
   between the transport and network layers. Network equipment vendors
   need to be assured that any new interface is stable enough (on decade
   timescales) to build into firmware and hardware, and OS vendors will
   not use a new interface unless it is likely to be widely deployed.

   Network components 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, network
   components can participate in congestion control via explicit
   signaling mechanisms. Explicit signaling mechanisms, whether in-
   band or out-of-band, require a communication between network
   components and end-systems. Signals realized within or over the IP
   layer are only meaningful to network components that process IP
   packets. This always includes routers and potentially also
   middleboxes, but not pure link layer devices. The following section
   distinguishes clearly between the term "network component" and the
   term "router"; the term "router" is used whenever the processing of
   IP packets is explicitly required. One fundamental challenge of

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   network supported congestion control is that typically not all
   network components along a path are routers (cf. Section 3.1.3).

   The first (optimizing) category of implicit mechanisms can be
   implemented in any network component that processes and stores
   packets. 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) [Firoiu00] [Hollot01] [Zhang03]. Many AQM schemes
   (RED, REM, BLUE, PI-Controller but also Adaptive Virtual Queue (AVQ))
   do not define a systematic rule for setting their parameters.

   The second class of approaches uses explicit signalling. 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 control congestion.

   Explicit feedback techniques fall into three broad categories:
   - Explicit congestion feedback: one bit Explicit Congestion
     Notification (ECN) [RFC3168] or proposals for more than one bit
   - Explicit per-datagram rate feedback: the eXplicit Control Protocol
     (XCP) [Katabi02] [Falk07], the Rate Control Protocol (RCP)
   - Explicit rate feedback: by means of in-band signaling, such as by
     Quick-Start [RFC4782] or by means of out-of-band signaling, e.g.
     CADPC/PTP [Welzl03].

   Explicit router feedback can address some of the inherent
   shortcomings of TCP. For instance, XCP was developed to overcome the
   inefficiency, 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
   equal 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

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   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 generally equalizes rate among different flows. [Low05]
   provides for a complete characterization of the XCP equilibrium

   Several other explicit router feedback schemes have been developed
   with different design objectives. For instance, RCP uses per-packet
   feedback similar to XCP. But unlike XCP, RCP focuses on the reduction
   of flow completion times [Dukki06], taking an optimistic approach to
   flows likely to arrive in the next RTT and tolerating larger
   instantaneous queue sizes [Dukki05]. XCP on the other hand gives very
   poor flow completion times for short flows.

   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 it remains as simple
   as possible (any additional complexity is placed above the IP layer,
   i.e., at the edges) so as to ensure evolvability, reliability and
   robustness. Furthermore, the fate-sharing principle ([Clark88]
   "Design Philosophy of the DARPA Internet Protocols") mandates that an
   end-to-end Internet protocol design should not rely on the
   maintenance of any per-flow state (i.e., information about the state
   of the end-to-end communication) inside the network 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 [RFC1958].

   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 network 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
   per-flow behavior inside the network should be avoided, as such
   design would clearly be at odds with the end-to-end and fate sharing
   design principles.

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   The end-to-end and fate sharing principles are generally regarded as
   the key ingredients for ensuring a scalable and survivable network
   design. In order to ensure that new congestion control mechanisms are
   scalable, violating these principles must therefore be avoided.

   For instance, protocols like XCP and RCP seem not to require flow
   state in the network, but this is only the case if the network trusts
   i) the receiver not to lie when feeding back the network's delta to
   the requested rate; ii) the source not to lie when declaring its
   rate; and iii) the source not to cheat when setting its rate in
   response to the feedback [Katabi04].

   Solving these problems for non-cooperative environments like the
   public Internet requires flow state, at least on a sampled basis.
   However, because flows can create new identifiers whenever they want,
   sampling does not provide a deterrent---a flow can simply cheat until
   it is discovered then switch to a whitewashed identifier [Feldmann04]
   and continue cheating until it is discovered again [Bri09, S7.3].

   However, holding flow state in the network only seems to solve these
   policing problems in single autonomous system settings. A multi-
   domain system would seem to require a completely different protocol
   structure, as the information required for policing is only seen as
   packets leave the internetwork, but the networks where packets enter
   will also want to police compliance.

   Even if a new protocol structure were found, it seems unlikely
   network flow state could be avoided given the network's per-packet
   flow rate instructions would need to be compared against variations
   in the actual flow rate, which is inherently not a per-packet metric.
   These issues have been outstanding ever since Intserv was identified
   as unscalable in 1997 [RFC2208]. All subsequent attempts to involve
   network elements in limiting flow-rates (XCP, RCP etc) will run up
   against the same open issue if anyone attempts to standardise them
   for use on the public Internet.

   In general, network support of congestion control raises many issues
   that have not been completely solved yet.

3.1.1 Performance and Robustness

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

   Router support can help to improve performance 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

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   avoid e.g. oscillations. A further challenge is the fact that
   information may be imprecise. For instance, severe congestion can
   delay feedback signals. Also, in-network 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.

   Open questions are:

   - How much can network elements 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 congestion control mechanisms that
     offer significant benefits with minimum additional risks, even if
     the Internet traffic patterns will change in future?

   - What is the minimum support that is needed from the network in
     order to achieve significantly better performance than with
     end-to-end mechanisms and the current IP header limitations that
     provide at most unary ECN signals?

3.1.2 Granularity of network component functions

   There are several degrees of freedom concerning the involvement of
   network entities, ranging from some few additional functions in
   network management procedures on the one end to 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.

   Although there are many research proposals that do not require per-
   flow state and thus do not cause a large processing overhead, there
   are no known full solutions (i.e. including anti-cheating) that do
   not require per-flow processing. Also, 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?

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3.1.3 Information Acquisition

   In order to support congestion control, network components 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 operating at IP layer do not necessarily know the
      link layer network topology and link capacities, and these are not
      always constant (e.g., on shared wireless links or bandwidth-on-
      demand links). Depending on the network technology, there can be
      queues or bottlenecks that are not directly visible at the IP
      networking layer.

      Difficulties also arise when using IP-in-IP tunnels [RFC2003]
      IPsec tunnels [RFC4301], IP encapsulated in L2TP [RFC2661], GRE
      [RFC1701] [RFC2784], PPTP [RFC2637] or MPLS [RFC3031] [RFC3032].
      In these cases, link information could be determined by cross-
          layer information exchange, but this requires interfaces capable
          of processing link layer technology specific information. An
          alternative could be online measurements, but this can cause
      significant additional network overhead. It is an open research
      question as how much, if any, online traffic measurement would
      be acceptable (at run-time). General guidelines for encapsulation
      and decapsulation of explicit congestion information are currently
      in preparation [ECN-tunnel].

   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, network components 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?

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   - Which information is so important to higher layer controllers that
     machine architecture research should focus on designing to provide

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 [Sarola07]. The latter case requires
   additional protocols and a secure binding between the signals and the
   packets they refer to. Out-of-band signaling 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.)? Should the
     feedback signaling be path-coupled or path-decoupled?

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

   - What direction should feedback take (from network resource via
     receiver to sender, or directly back to sender)?

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
   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 loss,
   rendering the typical reaction of a congestion control mechanism
   inappropriate. As a result, non-congestive loss may be more
   prevalent in these networks due to corruption loss (when the wireless
   link cannot be conditioned to properly control its error rate or due
   to transient wireless links interruption in areas of poor coverage).

   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 (see for instance, the discussion
   that occurred in April 2003 on the DCCP WG list http://www.ietf.org
   /mail-archive/web/dccp/current/mail6.html) 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 because it has lost the integrity of the
   feedback loop.

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   Generally, two questions must be addressed when designing congestion
   control mechanism that takes corruption loss 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

   The idea of having a transport end-point detecting and accordingly
   reacting (or not) to corruption poses a number of interesting
   questions regarding cross-layer interactions. As IP is designed to
   operate over arbitrary link layers, it is therefore difficult to
   design a congestion control mechanism on top of it that 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, e.g., [Tickoo05]. For instance, TCP Westwood sets the
   congestion window equal to the measured bandwidth at the 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].

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   Open questions concerning corruption loss include:

   - How should corruption loss be detected?

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

   - Can an ECN-capable flow infer that loss must be due to corruption
     just from lack of explicit congestion notifications around a loss
     episode [LT-TCP]? Or could this inference be dangerous given the
     transport does not know whether all queues on the path are ECN-
     capable or not?

3.3 Challenge 3: Packet Size

   TCP does not take packet size into account when responding to losses
   or ECN. 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 B is proportional to the segment size
   and inversely proportional to the RTT and the square root of the
   drop probability:

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

    where,  S     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

   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 packet latency (the smaller a packet, the shorter the time that
   is needed until it is filled with data). Observing that TCP is not
   optimal for applications with 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. Therefore, the influence of the packet size on the
   sending rate has not typically been seen as a significant issue,
   given there are still few paths through the Internet that support
   packets larger than the 1500Bytes common with Ethernet.

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   The situation is already 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 approximation
     of TCP's congestion control, incorporating a variant of SACK
     [RFC2018], [RFC3517]. CCID 2 is suitable for senders which can
     adapt to the abrupt changes in congestion window typical of TCP's
     AIMD congestion control, and particularly useful for senders which
     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) [RFC5348] is a congestion control mechanism
     designed for unicast flows operating in a best-effort Internet
     environment. When competing for bandwidth its window is similar to
     TCP flows, but has a much lower variation of throughput over time
     than 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 [RFC5622]: TFRC Small Packets (TFRC-
     SP) [RFC4828], a variant of the TFRC mechanism has been designed
     for applications that exchange small packets. The objective of
     TFRC-SP is to achieve the same bandwidth in 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. 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 the packet header size, as specified in

   The resulting open questions are:

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   - 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)?

   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. frame,
   packet and transport 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, packet-congestion is
   not impossible for runs of small packets (e.g. TCP ACKs or DoS
   attacks with TCP SYNs or small UDP datagrams). But absent such
   anomalous workloads, equipment vendors in at the 2008 ICCRG meeting
   believed that equipment could still be designed so that any
   congestion should be due to bit-overload not packet-overload.

   This observation raises additional open issues:

   - Can 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 for the future? An alternative view is that in-network
     processing will become commonplace, so that per-packet processing
     will be as likely to be the bottleneck as per-bit transmission

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

     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.

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   - Confusable Causes of Loss

     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 loss is
     longer and includes transmission corruption 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 packet 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 does not necessarily reduce the bit rate.

     Given distinguishing between corruption 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 loss, not just
     two of them.

     Nonetheless, even if we find a way for network equipment to
     explicitly distinguish which sort of loss 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 Hubs, DSLAMs, WLAN cards, cellular
     base-stations and so on. Thus, transport protocols will always
     have to cope with packet drops due to unpredictable causes, so we
     should always treat, e.g., AQM as an optimization because as long
     as it is not ubiquitous throughout the public Internet.

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

     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 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.

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   - 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 incidence of alternative maximum 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 to be sensitive to packet size, 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. It is still an open research issue to establish whether TCP
     was correct in not taking packet size into account. If it is
     determined that TCP was wrong in this respect, we should discourage
     future protocol designs from following TCP's example. 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 thorough review of what it means for a
     congestion control to scale with packet size.

3.4 Challenge 4: Flow Startup

   The beginning of data transmissions imposes some further, unique
   challenges: when a connection to a new destination is established,
   the end-systems have hardly any information about the characteristics
   of the path in between and the available bandwidth. In this flow
   startup situation there is no obvious choice how to start to send. A
   similar problem also occurs after relatively long idle times, since
   the congestion control state then no longer reflects current
   information about the state of the network (flow restart problem).

   Van Jacobson [Jacobson88] suggested using the slow-start mechanism
   both for the flow startup and the flow restart, and this is today's
   standard solution [RFC2581] [RFC5681]. Per [RFC5681], the slow-start
   algorithm is used when the congestion window (cwnd) < slow start
   threshold (ssthresh), whose initial value is set arbitrarily high
   (e.g., to the size of the largest possible advertised window) and
   reduced in response to congestion. During slow start, TCP increments
   the cwnd by at most Sender MSS bytes for each ACK received that

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   cumulatively acknowledges new data. Slow start ends when cwnd
   exceeds ssthresh or when congestion is observed. However, the slow-
   start is not optimal in many situations. First, it can take quite a
   long time until a sender can fully utilize the available bandwidth
   on a path. Second, the exponential increase may be too aggressive
   and cause multiple packet loss if large congestion windows are
   reached (slow-start overshooting). Finally, the slow-start does not
   ensure that new flows converge quickly to a reasonable share of
   resources, in particular, when the new flows compete with long-
   lived flows and comes out of slow-start early (slow-start vs
   overshoot trade-off). This convergence problem may even worsen if
   more aggressive congestion control variants get widely used.

   The slow-start and its interaction with the congestion avoidance
   phase was largely designed by intuition [Jacobson88]. So far, little
   theory has been developed to understand the flow startup problem and
   its implication on congestion control stability and fairness. There
   is also no established methodology to evaluate whether new flow
   startup mechanisms are appropriate or not.

   As a consequence, it is a non-trivial task to address the
   shortcomings of the slow-start algorithm. Several experimental
   enhancements have been proposed, such as congestion window validation
   [RFC2861] and limited slow-start [RFC3742]. There are also ongoing
   research activities, focusing e.g. on bandwidth estimation
   techniques, delay-based congestion control, or rate pacing
   mechanisms. However, any alternative end-to-end flow startup approach
   has to cope with the inherent problem that there is no or only little
   information about the path at the beginning of a data transfer. This
   uncertainty could be reduced by more expressive feedback signaling
   (cf. Section 3.1). For instance, a source could learn the path
   characteristics faster with the Quick-Start mechanism [RFC4782]. But,
   even if the source knew exactly what rate it should aim for, it would
   still not necessarily be safe to jump straight to that rate. The end-
   system still does not know how a change in its own rate will affect
   the path, which also might become congested in less than one RTT.
   Further research would be useful to understand the effect of
   decreasing the uncertainty by explicit feedback separately from
   control theoretic stability questions. Furthermore, flow startup
   also raises fairness questions. For instance, it is unclear whether
   it could be reasonable to use a faster startup when an end-system
   detects that a path is currently not congested.

   In summary, there are several topics for further research concerning
   flow startup:

   - Better theoretical understanding of the design and evaluation of
     flow startup mechanisms, concerning their impact on congestion
     risk, stability, and fairness.

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   - Evaluate whether it may be appropriate to allow alternative
     starting schemes, e.g., to allow higher initial rates under certain
     constraints; this also requires refining the definition of fairness
     for startup situations.

   - Better theoretical models for the effects of decreasing
     uncertainty by additional network feedback, in particular if the
     path characteristics are very dynamic.

3.5 Challenge 5: Multi-domain Congestion Control

   Transport protocols such as TCP operate over the Internet, which is
   divided into autonomous systems. These systems are characterized by
   their heterogeneity as IP networks are realized by a multitude of

3.5.1 Multi-domain Transport of Explicit Congestion Notification

   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, encourages them to efficiently use the
   network. In IP, ECN uses the two unused bits of the Type Of Service
   (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. The congestion-based feedback scheme
   however has limitations when applied on an inter-domain basis.
   Indeed, Sections 8 and 19 of [RFC3168] details the implications
   of two possible attacks:

      i) non-compliance: a network erasing CE introduced earlier on the
         path, and

      ii) subversion: a network changing Not-ECN Capable Transport (Not-
          ECT) to ECT.

   Both of which could allow an attacking network to cause excess
   congestion in an upstream network, even if the transports were
   behaving correctly. There are to date two possible solutions to the

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   non-compliance problem (number i above): the ECN-nonce [RFC3540] and
   the re-ECN incentive system [Bri09]. Nevertheless, accidental rather
   than malicious erasure of ECN is an issue for IPv6 where the absence
   of an IPv6 header checksum implies that corruption of ECN could be
   more impacting than in the IPv4 case.

   Fragmentation is another issue: the ECN-nonce cannot protect against
   misbehaving receivers that conceal marked fragments; thus, some
   protection is lost in situations where Path MTU discovery is
   disabled. Note also that ECN-nonce wouldn't protect against the
   subversion issue (number ii above) because, by definition, a Not-ECT
   packet comes from a source without ECN enabled, and therefore,
   without the ECN-nonce enabled. So, there is still room for
   improvement on the ECN mechanism when operating in multi-domain

   Operational/deployment experience is nevertheless required to
   determine the extent of these problems. The second problem is mainly
   related to deployment and usage practices and does not seem to result
   in any specific research challenge.

   Another controversial 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 delays 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 interferes with the end-to-
   end TCP model, and thus it would interfere with past and future
   diversity of TCP implementations (violating the end-to-end
   principle). In particular, the TRC embeds the flow rate equality view
   of fairness in the network, and would prevent evolution to forms of
   fairness based on congestion-volume (Section 2.3).

3.5.2 Multi-domain Exchange of Topology or Explicit Rate Information

   Security is a challenge for multi-domain exchange of explicit rate
   signals, whether in-band or out-of-band. At domain boundaries,
   authentication and authorization issues can arise whenever congestion
   control information is exchanged. From this perspective, the Internet
   does not so far have any security architecture for this problem.

   The future evolution of the Internet inter-domain operation has to
   show whether more multi-domain information exchange can be
   effectively realized. This is of particular importance for congestion
   control schemes that make use of explicit per-datagram rate feedback
   (e.g. RCP or XCP) or explicit rate feedback that use in-band
   congestion signaling (e.g. QuickStart) or out-of-band signaling (e.g.
   CADPC/PTP). Explicit signaling exchanges at the inter-domain level
   that result in local domain triggers are currently absent from the
   Internet. From this perspective, security means resulting from

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   limited trust between different administrative units result in policy
   enforcement that exacerbates the difficulty encountered when explicit
   feedback congestion control information is exchanged between domains.
   Note that even though authentication mechanisms could be extended for
   this purpose (by recognizing that explicit rate schemes such as RCP
   or XCP have the same inter-domain security requirements and structure
   as IntServ), they suffer from the same scalability problems as
   identified in [RFC2208]. Indeed, in-band rate signaling or out-of-
   band per-flow traffic specification signaling (like in RSVP) results
   in similar scalability issues.

   Also, many autonomous systems also only 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. Indeed, revealing the
   internal network structure is highly sensitive in multi-domain
   network operations and thus, also a concern when it comes to the
   deployability of congestion control schemes. For instance, a network-
   assisted congestion control scheme with explicit signaling could
   reveal more information about the internal network dimensioning than
   TCP does today.

3.5.3 Multi-domain Pseudowires

   Extending pseudo-wires across multiple domains poses specific issues.
   Pseudowires (PW) [RFC3985] may carry non-TCP data flows (e.g. TDM
   traffic) over a multi-domain IP network. Structure Agnostic TDM over
   Packet (SATOP) [RFC4553], Circuit Emulation over Packet Switched
   Networks (CESoPSN), TDM over IP, are not responsive to congestion
   control as discussed by [RFC2914] (see also [RFC5033]).

   Moreover, it is not possible to simply reduce the flow rate of a TDM
   PW when facing packet loss. Providers can rate control corresponding
   incoming traffic but they may not be able to detect that PWs carry
   TDM traffic (mechanisms for characterizing the traffic temporal
   properties may not necessarily be supported). This can be illustrated
   with the following example.

                ...........       ............
               .           .     .
        S1 --- E1 ---      .     .
               .     |     .     .
               .      === E5 === E7 ---
               .     |     .     .     |
        S2 --- E2 ---      .     .     |
               .           .     .     |      |
                ...........      .     |      v
   .                                    ----- R --->
                ...........      .     |      ^

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               .           .     .     |      |
        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-

   The problem arises for transit provider P2 that is not able to detect
   that IP packets are carrying constant-bit rate service traffic for
   which the only useful congestion control mechanism would rely on
   implicit or explicit admission control, meaning self-blocking or
   enforced blocking respectively.

   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 (e.g. each flow within
   the PW) would follow TCP's AIMD algorithm of reducing the sending
   rate in half in response to each packet drop. Nevertheless, the PWs
   carrying TDM traffic could take all the available capacity while
   other more TCP-friendly or generally congestion-responsive traffic
   reduced itself to nothing. Note here that the situation may simply
   occur because S4 suddenly turns on additional TDM channels.

   It is neither possible nor desirable to assume that edge routers will
   soon have the ability to detect the responsiveness of the carried
   traffic, but it is still important for transit providers to be able
   to police a fair, robust, responsive and efficient congestion control
   technique in order to avoid impacting congestion responsive Internet
   traffic. However, we must not require only certain specific responses
   to congestion to be embedded within the network, which would harm
   evolvability. So designing the corresponding mechanisms in the data
   and control planes still requires further investigation.

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.

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   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 combined 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 the interactions between congestion control
   and DiffServ [RFC2475] remained unaddressed up to recently.

   Recently, a study and a potential solution have been proposed that
   introduce Guaranteed TFRC (gTFRC) [Lochin06]. gTFRC is an adaptation
   of TCP-Friendly Rate Control providing throughput guarantee for
   unicast flows over the DiffServ/AF class. The purpose of gTFRC is to
   distinguish the guaranteed part from the best-effort part of the
   traffic resulting from AF conditioning. The proposed congestion
   control has been specified and tested inside DCCP/CCID3 for
   DiffServ/AF networks [Lochin07]. A complete reliable transport
   protocol based-on gTFRC and SACK appears to be the first reliable
   DiffServ/AF compliant transport protocol [Jourjon08].

   Nevertheless, there is still work to be performed regarding lower
   precedence traffic - data transfers which are useful, yet not
   important enough to warrant significantly impairing 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 network-based lower precedence mechanisms are
   not yet a common service on the Internet. There is an expectation
   that end-to-end mechanisms for lower precedence e.g. [LEDBAT] could
   become common --at least when competing with other traffic as part of
   its own queues (e.g. in a home router). But it is less clear whether
   user will be willing to make their background traffic yield to other
   people's foreground traffic unless the appropriate incentives are

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   There is an issue over how to reconcile two divergent views of the
   relation between traffic class precedence and congestion control. One
   view considers that congestion signals (losses or explicit
   notifications) in one traffic class are independent of those in
   another. The other relates marking of the classes together within the
   active queue management (AQM) mechanism [Gibbens02]. In the
   independent case, using a higher precedence class of traffic gives a
   higher scheduling precedence and generally lower congestion level. In
   the linked case, higher precedence still gives higher scheduling
   precedence, but results in a higher level of congestion. This higher
   congestion level reflects the extra congestion higher precedence
   traffic causes to both classes combined. The linked case separates
   scheduling precedence from rate control. The end-to-end congestion
   control algorithm can separately choose to take a higher rate by
   responding less to the higher level of congestion. This second
   approach could become prevalent if weighted congestion controls were
   common. However, it is an open issue how the two approaches might co-
   exist or how one might evolve into the other.

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 improve 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.
   [Savage99], [RFC3540]). But, although such strategies are worryingly
   powerful, they do not yet seem common (however, evidence of attack
   prevalence is itself a research requirement).

   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]. Also, Internet video on

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   demand services are becoming popular that transfer much greater data
   rates without congestion control. In general, it is recommended that
   such UDP applications use some form of congestion control [RFC5405].

   Note that the problem is not just misbehavior driven by a self-
   interested desire for more bandwidth. Indeed, congestion control may
   be attacked by someone who makes no gain for themselves, other than
   the satisfaction of harming others (see Security Considerations in
   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. Still, it is unclear
     how such mechanisms would have to be designed.

   - Which congestion control primitives could safely 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 could disappear from the Internet.
   So, it may no longer be sufficient to rely on developers/users
   voluntarily submitting themselves to congestion control. As a
   consequence, mechanisms to enforce fairness (see Sections 2.3, 3.4,
   and 3.5) 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 so far.

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, if echoed back
   immediately by the 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

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   determine the RTT from ongoing communication processes, without
   sending additional packets.

   The connection endpoints of 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) or the rate control equation. 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.

   Sometimes it would be beneficial to know the RTT not only at the
   sender, but also at the receiver, e.g., to find the one-way variation
   in delay due to one-way congestion. 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. The more fundamental question being
   to determine whether it is necessary or not for network elements to
   measure or know the RTT.

   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 flapping, hand-over 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

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   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

   - Optimal measurement frequency: Currently, there is no theory of
     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].

   - 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

   - 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
     (particularly on low cost devices) 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 or even crash
   completely when a certain mechanism is used, causing users to opt for

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   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 and is still hindered by malfunctioning home-hubs, 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.

   A possible way to reduce such problems in the future would be
   guidelines for standards authors to ensure `forward compatibility' is
   considered in all IETF work. That is, the default behavior of a
   device should be precisely defined for all possible values and
   combinations of protocol fields, not just the minimum necessary for
   the protocol being defined. Then when previously unused or reserved
   fields start to be used by newer devices to comply with a new
   standard, older devices encountering unusual fields should at least
   behave predictably.

3.8.3 Dependence on RTT

   AIMD window algorithms that have the goal of packet conservation end
   up converging on a rate that is inversely proportional to RTT.
   However, control theoretic approaches to stability have shown that
   only the increase in rate (acceleration) not the target rate needs to
   be inversely proportional to RTT.

   It is possible to have more aggressive behaviors for some demanding
   applications as long as they are part of a mix with less aggressive
   transports [Key04]. This beneficial effect of transport type mixing
   is probably how the Internet currently manages to remain stable even
   in the presence of TCP slow start, which is more aggressive than the
   theory allows for stability. Research giving deeper insight into
   these aspects would be very useful.

3.8.4 Congestion Control in Multi-layered Networks

   A network of IP nodes is just as vulnerable to congestion in the
   lower layers between IP-capable nodes as it is to congestion on the

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   IP-capable nodes themselves. If network elements take a greater part
   in congestion control (ECN, XCP, RCP, etc. - see Section 3.1), these
   techniques will either need to be deployed at lower layers as well,
   or they will need to interwork with lower layer mechanisms.

   [ECN-tunnel] gives guidelines on propagating ECN from lower layers
   upwards, but to the authors' knowledge the layering problem has not
   been addressed for explicit rate protocol proposals such as XCP and
   RCP. Some issues are straightforward matters of interoperability
   (e.g. how exactly to copy fields up the layers) while others are
   less obvious (e.g. re-framing issues: if RCP were deployed in a lower
   layer, how might multiple small RCP frames all with different rates
   in their headers be assembled into a larger IP-layer datagram?).

   Multi-layer considerations also confound many mechanisms that aim to
   discover whether every node on the path supports the new congestion
   control protocol. For instance, some proposals maintain a secondary
   TTL field parallel to that in the IP header. Any nodes that support
   the new behavior update both TTL fields, whereas legacy IP nodes will
   only update the IP TTL field. This allows the endpoints to check
   whether all IP nodes on the path support the new behavior, in which
   case both TTLs will be equal at the receiver. But mechanisms like
   these overlook nodes at lower layers that might not support the new

   A further related issue is congestion control across overlay networks
   of relays [Hilt08, Noel07, Shen08]].

3.8.5 Multipath End-to-end Congestion Control and Traffic Engineering

   Recent work has shown that multipath endpoint congestion control
   [Kelly05] offers considerable benefits in terms of resilience and
   resource usage efficiency. By pooling the resources on all paths,
   even nodes not using multiple paths benefit from those that are.

   There is considerable further research to do in this area,
   particularly to understand interactions with network operator
   controlled route provision and traffic engineering, and indeed
   whether multipath congestion control can perform better traffic
   engineering than the network itself, given the right incentives.

3.8.6 ALGs and Middleboxes

   An increasing number of application layer gateways (ALG),
   middleboxes, and proxies (see Section 3.6 of [RFC2775]) is deployed
   at domain boundaries to verify conformance but also filter traffic
   and control flows. One motivation is to prevent information beyond
   routing data leaking between autonomous systems. These systems split

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   up end-to-end TCP connections and disrupt end-to-end congestion
   control. Furthermore, transport over encrypted tunnels may not allow
   other network entities to participate in congestion control.

   Basically, such systems disrupt the primal and dual congestion
   control components. In particular, end-to-end congestion control may
   be replaced by flow-control backpressure mechanisms on the split
   connections. A large variety of ALGs and middleboxes use such
   mechanisms to improve the performance of applications (Performance
   Enhancing Proxies, Application Accelerators, etc.). However, the
   implications of such mechanisms, which are often proprietary and not
   documented, have not been studied systematically so far.

   There are two levels of interference:

   - The "transparent" case, i.e. the end-point address from the sender
     perspective is still visible to the receiver (the destination IP
     address). An example are relay systems that intercept payload but
     do not relay congestion control information. Such middleboxes can
     prevent the operation of end-to-end congestion control.

   - The "non-transparent" case, which causes less problems. Although
     these devices interfere with end-to-end network transparency, they
     correctly terminate network, transport and application layer
     protocols on both sides, which individually can be congestion

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
   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 against the network, which can
   be considered (from the perspective of well-behaving Internet user)
   as a congestion control enforcement problem. Even some denial of

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   service attacks on hosts (rather than the network) could be
   considered as a congestion control enforcement issue at the higher
   layer. But clearly there are also denial of service attacks that
   would not be solved by enforcing congestion control.

   Sections 3.5 and 3.7 on multi-domain issues and misbehaving senders
   and receivers also discuss some information security issues suffered
   by various congestion control approaches.

5. IANA Considerations

   This document has no IANA actions.

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6. References

6.1 Informative References

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               Network Path Properties", Proceedings of ACM SIGCOMM'99,
               September 1999.

   [Andrew05]  Andrew, L., Wydrowski, B., and S. Low, "An Example of
               Instability in XCP", Manuscript available at

   [Ath01]     Athuraliya, S., Low, S., Li, V., 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,
               W.K.G., and Ananda, A.L., "TCP HACK: TCP Header Checksum
               Option to Improve Performance over Lossy Links",
               Proceedings of IEEE INFOCOM'01, Anchorage (Alaska), USA,
               April 2001.

   [Bonald00]  Bonald, T., May, M., and J.-C. Bolot, "Analytic
               Evaluation of RED Performance," Proceedings of IEEE
               INFOCOM'00, Tel Aviv, Israel, March 2000.

   [Bri08]     Briscoe, B., Moncaster, T. and L. Burness, "Problem
               Statement: Transport Protocols Don't Have To Do
               Fairness", Work in progress, draft-briscoe-tsvwg-relax-
               fairness-01, July 2008.

   [Bri06]     Briscoe, B., "Using Self-interest to Prevent Malice;
               Fixing the Denial of Service Flaw of the Internet,"
               Workshop on the Economics of Securing the Information
               Infrastructure, October 2006.

   [Bri07]     Briscoe, B., "Flow Rate Fairness: Dismantling a
               Religion", ACM SIGCOMM Computer Communication Review,
               Vol.37, No.2, pp.63-74, April 2007.

   [Bri09]     Briscoe, B., "Re-feedback: Freedom with Accountability
               for Causing Congestion in a Connectionless Internetwork,"
               UCL PhD Thesis (2009).

   [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.

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   [Chiu89]    Chiu, D.M., and R. Jain, "Analysis of the increase and
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   [Clark88]   Clark, D., "The design philosophy of the DARPA internet
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   [Clark98]   Clark, D., 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.

   [Dukki05]   Dukkipati, N., Kobayashi, M., Zhang-Shen, R. and N.,
               McKeown, "Processor Sharing Flows in the Internet",
               Proceedings of International Workshop on QoS (IWQoS'05),
               Passau, Germany, June 2005.

   [Dukki06]   Dukkipati, N. and N. McKeown, "Why Flow-Completion Time
               is the Right Metric for Congestion Control", ACM SIGCOMM
               Computer Communication Review, Vol.36, No.1, January

   [ECN-tunnel] Briscoe, B., "Layered Encapsulation of Congestion
               Notification", Internet Draft, Work in progress, draft-

   [ECODE]     "ECODE Project", European Commission Seventh Framework
               Program, Grant No. 223936, <http://www.ecode-project.eu>

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

   [Feldmann04] Feldmann, M., Papadimitriou, C., Chuang, J., I. Stoica,
               "FreeRiding and Whitewashing in Peer-to-Peer Systems"
               Proceedings of ACM SIGCOMM Workshop on Practice and
               Theory of Incentives in Networked Systems (PINS'04) 2004.

   [Firoiu00]  Firoiu, V., and M. Borden, "A Study of Active Queue
               Management for Congestion Control," Proceedings of IEEE
               INFOCOM'00, Tel Aviv, Israel, March 2000.

   [Floyd93]   Floyd, S., and V. Jacobson, "Random early detection
               gateways for congestion avoidance," IEEE/ACM Transactions
               on Networking, Vol.1, No.4, pp.397-413, August 1993.

   [Floyd94]   Floyd, S., "TCP and Explicit Congestion Notification",

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               ACM Computer Communication Review, Vol.24, No.5, pp.10-
               23, October 1994.

   [Gibbens02] Gibbens, R. and Kelly, F., "On Packet Marking at Priority
               Queues," IEEE Transactions on Automatic Control, Vol.47,
               No.6, pp.1016-1020, 2002.

   [Ha08]      Ha, S., Rhee, I., and L. Xu, "CUBIC: A new TCP-friendly
               high-speed TCP variant", ACM SIGOPS Operating System
               Review, Vol.42, No.5, pp.64-74, 2008.

   [Hollot01]  Hollot, C., Misra, V., Towsley, D., and W.-B. Gong, "A
               Control Theoretic Analysis of RED," Proceedings of IEEE
               INFOCOM'01, Anchorage (Alaska), USA, April 2001.

   [Jacobson88] Jacobson, V., "Congestion Avoidance and Control",
               Proceeding of ACM SIGCOMM'88 Symposium, August 1988.

   [Jain88]    Jain, R., and K. Ramakrishnan, "Congestion Avoidance in
               Computer Networks with a Connectionless Network Layer:
               Concepts, Goals, and Methodology", Proceedings of IEEE
               Computer Networking Symposium, Washington DC, USA, April

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

   [Jin04]     Jin, Ch., Wei, D.X., and S. Low, "FAST TCP: Motivation,
               Architecture, Algorithms, Performance," Proceedings of
               IEEE INFOCOM'04, Hong-Kong, China, March 2004.

   [Jourjon08] Jourjon, G., Emmanuel Lochin, E., and P. Senac, "Design,
               Implementation and Evaluation of a QoS-aware Transport
               Protocol", Elsevier, Computer Communications, Vol.31,
               No.9, pp.1713-1722, June 2008.

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

   [Katabi04]  Katabi, D., "XCP Performance in the Presence of Malicious
               Flows", Proceeding of PFLDnet'04 Workshop, Argonne
               (Illinois), USA, February 2004.

   [Kelly98]   Kelly, F., Maulloo, A., 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.

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   [Kelly05]   Kelly, F., and Th. Voice, "Stability of end-to-end
               algorithms for joint routing and rate control", ACM
               SIGCOMM Computer Communication Review, Vol.35, No.2, pp.
               5-12, April 2005.

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

   [Key04]     Key, P., Massoulie, L., Bain, A., and F. Kelly, "Fair
               Internet Traffic Integration: Network Flow Models and
               Analysis", Annales des Telecommunications, Vol.59, No.11-
               12, pp.1338-1352, November-December 2004.

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

   [Kuzmanovic03] Kuzmanovic, A., and E. W. Knightly, "TCP-LP: A
               Distributed Algorithm for Low Priority Data Transfer",
               Proceedings of IEEE INFOCOM'03, San Francisco
               (California), USA, April 2003.

   [LEDBAT]    Shalunov, S., "Low Extra Delay Background Transport
               (LEDBAT)", Internet Draft, Work in progress, draft-

   [Lochin06]  Lochin, E., Jourjon, G., and L. Dairaine, "Guaranteed TCP
               Friendly Rate Control (gTFRC) for DiffServ/AF Network"
               Internet Draft, Work in Progress, draft-lochin-ietf-

   [Lochin07]  Lochin, E., Jourjon, G., and L. Dairaine, "Study and
               enhancement of DCCP over DiffServ Assured Forwarding
               class", 4th Conference on Universal Multiservice Networks
               (ECUMN 2007), Toulouse, France, February, 2007

   [Low02]     Low, S., Paganini, F., Wang, J., Adlakha, S., and J.C.
               Doyle, "Dynamics of TCP/RED and a Scalable Control",
               Proceedings of IEEE INFOCOM'02, New York (New-Jersey),

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

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

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   [Low05]     Low, S., Andrew, L., and B. Wydrowski, "Understanding
               XCP: equilibrium and fairness", Proceedings of IEEE
               INFOCOM'05, Miami (Florida), USA, March 2005.

   [LT-TCP]    Tickoo, O., Subramanian, V., Kalyanaraman, S., and K.K.
               Ramakrishnan, "LT-TCP: End-to-End Framework to Improve
               TCP Performance over Networks with Lossy Channels",
               Proceedings of International Workshop on QoS (IWQoS)
               2005, Passau, Germany, June 2005.

   [Mascolo01] Mascolo, S., Casetti, Cl., Gerla M., Sanadidi, M.Y., and
               R. Wang, "TCP Westwood: Bandwidth estimation for enhanced
               transport over wireless links", Proceedings of MOBICOM

   [Moors02]   Moors, T., "A critical review of "End-to-end arguments in
               system design", Proceedings of IEEE International
               Conference on Communications (ICC) 2002, New-York City
               (New Jersey), USA, April/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, Vol.13, No.7, pp.1141-1149, 1995.

   [Padhye98]  Padhye, J., Firoiu, V., Towsley, D., and J. Kurose,
               "Modeling TCP Throughput: A Simple Model and Its
               Empirical Validation", University of Massachusetts
               (UMass), CMPSCI Tech. Report TR98-008, February 1998.

   [Pan00]     Pan, R., Prabhakar, B., and K. Psounis, "CHOKe: a
               stateless AQM scheme for approximating fair bandwidth
               allocation", Proceedings of IEEE INFOCOM'00, Tel Aviv,
               Israel, March 2000.

   [PAP02]     Papadimitriou, I., and Mavromatis, G., "Stability of
               Congestion Control Algorithms using Control Theory with
               an application to XCP", Technical Report, 2002.

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

   [RFC793]    Postel, J., "Transmission Control Protocol", STD 7,
               RFC 793, September 1981.

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

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   [RFC1701]   Hanks, S., Li, T, Farinacci, D., and P. Traina, "Generic
               Routing Encapsulation", RFC 1701, October 1994.

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

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

   [RFC2018]   Mathis, M., Mahdavi, J., Floyd, S. and A. Romanow, "TCP
               Selective Acknowledgement Options", RFC 2018, October

   [RFC2208]   Mankin, A., Ed., et al. "Resource ReSerVation Protocol
               (RSVP) -- Version 1 Applicability Statement Some
               Guidelines on Deployment", RFC 2208, September 1997.

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

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

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

   [RFC2637]   Hamzeh, K., et al., "Point-to-Point Tunneling Protocol
               (PPTP)", RFC 2637, July, 1999.

   [RFC2661]   Townsley, W., Valencia, A., Rubens, A., Pall, G., Zorn,
               G., and B. Palter, "Layer Two Tunneling Protocol (L2TP)",
               RFC 2661, August 1999.

   [RFC2775]   Carpenter, B., "Internet Transparency", RFC 2775,
               February 2000.

   [RFC2784]   Farinacci, D., Li, T., Hanks, S., Meyer, D. and P.
               Traina, "Generic Routing Encapsulation (GRE)", RFC 2784,
               March 2000.

   [RFC2861]   Handley, M., J. Padhye, J., and S., Floyd, "TCP
               Congestion Window Validation", RFC 2861, June 2000.

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

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

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

   [RFC3031]   Rosen, E., Viswanathan, A. and R. Callon, "Multiprotocol
               Label Switching Architecture", RFC 3031, January 2001.

   [RFC3032]   Rosen, E., Tappan, D., Fedorkow, G., Rekhter, Y.,
               Farinacci, D., Li, T. and A. Conta, "MPLS Label Stack
               Encoding", RFC 3032, January 2001.

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

   [RFC3517]   Blanton, E., Allman, M., Fall, K., et L. Wang, "A
               Conservative Selective Acknowledgment (SACK)-based
               Loss Recovery Algorithm for TCP", RFC 3517, April 2003.

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

   [RFC3662]   Bless, R., Nichols, K., and K. Wehrle, "A Lower Effort
               Per-Domain Behavior for Differentiated Services", RFC
               3662, December 2003.

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

   [RFC3742]   Floyd, S., "Limited Slow-Start for TCP with Large
               Congestion Windows", RFC 3742, March 2004.

   [RFC3985]   Bryant, S., and P. Pate, "Pseudo Wire Emulation Edge-to-
               Edge (PWE3) Architecture", RFC 3985, March 2005.

 [RFC4301]   Kent, S., and K. Seo, "Security Architecture for the
             Internet Protocol", RFC 4301, December 2005.

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

   [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.

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   [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 E. Blanton, "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, January 2007.

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

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

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

   [RFC5290]   Floyd, S., and M. Allman, "Comments on the Usefulness of
               Simple Best-Effort Traffic", RFC 5290, July 2008.

   [RFC5348]   Floyd, S., Handley, M., Padhye, J., and J. Widmer, "TCP
               Friendly Rate Control (TFRC): Protocol Specification",
               RFC 5348, September 2008.

   [RFC5405]   Eggert, L., and G. Fairhurst, "Unicast UDP Usage
               Guidelines for Application Designers, RFC 5405, November

   [RFC5622]   Floyd, S., and E. Kohler, "Profile for Datagram
               Congestion Control Protocol (DCCP) Congestion ID 4: TCP-
               Friendly Rate Control for Small Packets (TFRC-SP)", RFC
               5622, August 2009.

   [RFC5681]   Allman, M., Paxson, V., and Blanton, E., "TCP Congestion
               Control", RFC 5681 (Obsoletes RFC 2581), IETF, September

   [RFC5783]   Welzl, M., and W. Eddy, "Congestion Control in the RFC
               Series", RFC 5783, February 2010.

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

   [Sarola07]  Sarolahti, P., Floyd, S., and M. Kojo, "Transport-layer
               Considerations for Explicit Cross-layer Indications",
               Work in Progress, draft-sarolahti-tsvwg-crosslayer-
               01.txt, March 2007.

   [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 D. Clark, "End-to-end
               arguments in system design", ACM Transactions on Computer
               Systems, Vol.2, No.4, November 1984.

   [Shin08]    Shin, M., Chong, S., and I. Rhee, "Dual-Resource TCP/AQM
               for Processing-Constrained Networks", IEEE/ACM
               Transactions on Networking, Vol.16, No.2, pp.435-449,
               April 2008.

   [SK02]      Sarolahti, P., and M. Kojo, "F-RTO: A TCP RTO Recovery
               Algorithm for Avoiding Unnecessary Retransmissions",
               Internet-Draft, draft-sarolahti-tsvwg-tcp-frto-02.txt,
               November 2002, Work in progress.

   [Tickoo05]  Subramanian, V., Kalyanaraman, S. and Ramakrishnan, K.,
               "LT-TCP: End-to-End Framework to Improve TCP Performance
               over Networks with Lossy Channels," Proc. International
               Workshop on QoS (IWQoS'05), June 2005.

   [Thaler07]  Thaler, D., Sridhara, M., and D. Bansal, "Implementation
               Report on Experiences with Various TCP RFCs",
               Presentation to the IETF Transport Area, March 2007.

   [TRILOGY]   "Trilogy Project", European Commission Seventh Framework
               Program (FP7), Grant No: 216372

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

   [Welzl08]   Welzl, M., Rossi, M., Fumagalli, A., and M. Tacca,
               "TCP/IP over IEEE 802.11b WLAN: the Challenge of
               Harnessing Known-Corrupt Data", Proceedings of IEEE

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               International Conference on Communications (ICC) 2008,
               Beijing, China, May 2008.

   [Xia05]     Xia, Y., Subramanian, L., Stoica, I., and S.Kalyanaraman,
               "One more bit is enough", ACM SIGCOMM Computer
               Communication Review, Vol.35, No.4, pp.37-48, 2005.

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

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7. Acknowledgments

   The authors would like to thank the following people whose feedback
   and comments contributed to this document: Keith Moore, Jan
   Vandenabeele, and Larry Dunn (his comments at the Manchester ICCRG
   and discussions with him helped with the section on packet-

   Dimitri Papadimitriou's contribution was partly funded by [ECODE], a
   Seventh Framework Program (FP7) research project sponsored by the
   European Commission.

   Bob Briscoe's contribution was partly funded by [TRILOGY], a research
   project supported by the European Commission.

   Michael Scharf is now with Alcatel-Lucent.

8. Author's Addresses

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

   Michael Welzl
   University of Oslo, Department of Informatics
   PO Box 1080 Blindern
   N-0316 Oslo, Norway
   Email: michawe@ifi.uio.no

   Michael Scharf
   University of Stuttgart
   Pfaffenwaldring 47
   70569 Stuttgart, Germany
   Email: michael.scharf@googlemail.com

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

9. Contributors

   The following additional people have contributed to this document:
   - Wesley Eddy <weddy@grc.nasa.gov>
   - Bela Berde <bela.berde@gmx.de>
   - Paulo Loureiro <loureiro.pjg@gmail.com>
   - Chris Christou <christou_chris@bah.com>

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D.Papadimitriou              Expires - March 2011              [Page 47]

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