Internet Research Task Force (IRTF)                D. Papadimitriou, Ed.
Request for Comments: 6077                                Alcatel-Lucent
Category: Informational                                         M. Welzl
ISSN: 2070-1721                                       University of Oslo
                                                               M. Scharf
                                                 University of Stuttgart
                                                              B. Briscoe
                                                                BT & UCL
                                                           February 2011

Open Research Issues in Internet Congestion Control

Abstract

   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.

Status of This Memo

   This document is not an Internet Standards Track specification; it is
   published for informational purposes.

   This document is a product of the Internet Research Task Force
   (IRTF).  The IRTF publishes the results of Internet-related research
   and development activities.  These results might not be suitable for
   deployment.  This RFC represents the consensus of the Internet
   Congestion Control Research Group (ICCRG) of the Internet Research
   Task Force (IRTF).  Documents approved for publication by the IRSG
   are not a candidate for any level of Internet Standard; see Section 2
   of RFC 5741.

   Information about the current status of this document, any errata,
   and how to provide feedback on it may be obtained at
   http://www.rfc-editor.org/info/rfc6077.

Copyright Notice

   Copyright (c) 2011 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (http://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents
   carefully, as they describe your rights and restrictions with respect
   to this document.

   1. Introduction ....................................................3
   2. Global Challenges ...............................................5
      2.1. Heterogeneity ..............................................5
      2.2. Stability ..................................................7
      2.3. Fairness ...................................................8
   3. Detailed Challenges ............................................10
      3.1. Challenge 1: Network Support ..............................10
           3.1.1. Performance and Robustness .........................14
           3.1.2. Granularity of Network Component Functions .........15
           3.1.3. Information Acquisition ............................16
           3.1.4. Feedback Signaling .................................17
      3.2. Challenge 2: Corruption Loss ..............................17
      3.3. Challenge 3: Packet Size ..................................19
      3.4. Challenge 4: Flow Startup .................................24
      3.5. Challenge 5: Multi-Domain Congestion Control ..............26
           3.5.1. Multi-Domain Transport of Explicit
                  Congestion Notification ............................26
           3.5.2. Multi-Domain Exchange of Topology or
                  Explicit Rate Information ..........................27
           3.5.3. Multi-Domain Pseudowires ...........................28
      3.6. Challenge 6: Precedence for Elastic Traffic ...............30
      3.7. Challenge 7: Misbehaving Senders and Receivers ............31
      3.8. Other Challenges ..........................................33
           3.8.1. RTT Estimation .....................................33
           3.8.2. Malfunctioning Devices .............................35
           3.8.3. Dependence on RTT ..................................36
           3.8.4. Congestion Control in Multi-Layered Networks .......36
           3.8.5. Multipath End-to-End Congestion Control and
                  Traffic Engineering ................................37
           3.8.6. ALGs and Middleboxes ...............................37
   4. Security Considerations ........................................38
   5. References .....................................................39
      5.1. Informative References ....................................39
   6. Acknowledgments ................................................50
   7. Contributors ...................................................50

1. Introduction

   This document, the result of the Internet Congestion Control Research
   Group (ICCRG), describes some of the open research topics in the
   domain of Internet congestion control that are known today.  We begin
   by reviewing some proposed definitions of congestion and congestion
   control based on current understandings.

   Congestion can be defined as 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 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 the data link and physical layer on
   the one hand, and of applications on the other, are pulling TCP
   congestion control beyond its natural operating regime.  This is
   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 mechanism).

   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] are for quality
   enforcement, whereas Stabilized RED (SRED), and CHOKe [Pan00] and its
   extensions such as XCHOKe [Chhabra02], 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 for acceleration of this information flow.
   Primal-dual TCP/AQM distributed algorithm stability and equilibrium
   properties have been extensively studied (cf. [Low02], [Low03.1],
   [Low03.2], [Kelly98], and [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 the future.

   Additionally, neither the available bandwidth nor the end-to-end
   delay is constant.  At the IP layer, competing cross-traffic, traffic
   management in routers, and dynamic routing can result in sudden
   changes in 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 to new 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.

   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 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, important research questions are 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 to single functions in end-systems or optimizations of
   network components.  However, new mechanism(s) might also require a
   fundamental redesign of the overall network architecture, and they
   may even affect the design of Internet applications.  This can imply
   significant interoperability and backward compatibility challenges
   and/or create network accessibility obstacles.  In particular,

   networks and/or applications that do not use or support a new
   congestion control mechanism could be penalized by a significantly
   worse performance compared to what they would get if everybody used
   the existing mechanisms (cf. the discussion on fairness in
   Section 2.3).  [RFC5033] defines several criteria to evaluate the
   appropriateness of a new congestion control mechanism.  However, 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 [Vinnic02].  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 toward 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 to the controller only states 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 more slowly makes the
   system tardy [Jain90].

   Control theoretic modeling of a realistic network can be quite
   difficult, especially when taking distinct packet sizes and
   heterogeneous round-trip times (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 an early stage.  However, a mechanism that is

   found to be stable in simulations can still not be safely deployed in
   real networks, since simulation scenarios make simplifying
   assumptions.

   TCP stability can be attributed to two key aspects that 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
   clear.

2.3. Fairness

   Recently, the way the Internet community reasons about fairness has
   been called deeply into question [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 TCP-Friendly Rate Control (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
   [MacK95], the set of rates that everyone chooses is deemed fair (cost
   fairness) -- because with any other set of choices people would lose
   more value than they gained overall.

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

   The two traditional approaches are not protocol options that can each
   be followed in different parts of an internetwork.  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
      fairness?

   -  Should we introduce some degree of fairness that takes into
      account 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
      applications?

   -  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
      enforcement?

   -  How should we 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 prerequisite 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 convincing 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 operating-system
   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 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 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 in
   identifying correct values of the parameters that affect 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, and 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 signaling.  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
      [Xia05];

   -  Explicit per-datagram rate feedback: the eXplicit Control Protocol
      (XCP) [Katabi02] [Falk07], or the Rate Control Protocol (RCP)
      [Dukki05];

   -  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.,
      Congestion Avoidance with Distributed Proportional
      Control/Performance Transparency Protocol (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 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 rates among different flows.
   [Low05] provides for a complete characterization of the XCP
   equilibrium properties.

   Several other explicit router feedback schemes have been developed
   with different design objectives.  For instance, RCP uses 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
   endpoints/hosts [RFC1958].

   However, as discussed in [Moors02] for instance, 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 a
   design would clearly be at odds with the end-to-end and fate-sharing
   design principles.

   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 and then switch to a whitewashed identifier
   [Feldman04], 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 that
   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 integrated services
   (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 standardize 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.

   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 avoid, e.g., oscillations.  A further challenge is the
   fact that feedback 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 exist 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
      Internet traffic patterns will change in the 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, or 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 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 to a minimum?

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 the 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 the Layer Two
      Tunneling Protocol (L2TP) [RFC2661], Generic Routing Encapsulation
      (GRE) [RFC1701] [RFC2784], the Point-to-Point Tunneling Protocol
      (PPTP) [RFC2637], or Multiprotocol Label Switching (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 to how much, if any, online traffic
      measurement would be acceptable (at run-time).  Encapsulation and
      decapsulation of explicit congestion information have been
      specified for IP-in-IP tunnelling [RFC6040] and for MPLS-in-MPLS
      or MPLS-in-IP [RFC5129].

   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?

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

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 link 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 Datagram Congestion Control
   Protocol (DCCP) working group 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.

   Generally, two questions must be addressed when designing a
   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
   General Packet Radio Service (GPRS) network in a study [Chester04]
   and poorly over a WiFi network in another study [Rossi06] [Welzl08].
   Note that while UDP-Lite and DCCP enable the detection of corruption,
   the specifications of these protocols do not foresee any specific
   reaction to it for the time being.

   The idea of having a transport endpoint 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 [Mascolo01]
   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.

   Open questions concerning corruption loss include:

   -  How should corruption loss be detected?

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

   -  Can an ECN-capable flow infer that loss must be due to corruption
      just from lack of explicit congestion notifications around a loss
      episode [Tickoo05]?  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 an estimation of 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

         C     is a constant
         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 tradeoff 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 tradeoff 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 1500 bytes common with Ethernet.

   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 as well as incorporating a variant of
      Selective Acknowledgment (SACK) [RFC2018] [RFC3517].  CCID 2 is
      suitable for senders that can adapt to the abrupt changes in the
      congestion window typical of TCP's AIMD congestion control, and
      particularly useful for senders that 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 (CCID 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 [RFC4828].

   The resulting open questions are:

   -  How does TFRC-SP operate under various network conditions?

   -  How can congestion control be designed 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-case scenario 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 denial-of-service (DoS) attacks with TCP SYNs or small UDP
   datagrams).  But absent such anomalous workloads, equipment vendors
   at the 2008 ICCRG meeting believed that equipment could still be
   designed so that any congestion should be due to bit overload and 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 and 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 as likely be the bottleneck as per-bit transmission [Shin08].

      Over the last three decades, performance gains have mainly been
      achieved through increased packet rates and 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.

   -  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, and 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, and servers; and at
      every lower-layer device like low-end hubs, DSLAMs, Wireless LAN
      (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 AQM as an
      optimization, given it will never be ubiquitous throughout the
      public Internet.

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

      The open issue here is whether a loss or explicit congestion mark
      should be interpreted as a single congestion event irrespective of
      the size of the packet lost or marked, or whether the strength of
      the congestion notification is weighted by the size of the packet.
      This issue is discussed at length in [Bri10], along with other
      aspects of packet size and congestion control.

      [Bri10] 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.

   -  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, and not when the network writes, the notification
      -- it opens up a significant problem 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 1500 byte 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 account for 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 above, the small-packet variant of TCP-
      friendly rate control (TFRC-SP [RFC4828]) is an experimental
      protocol that aims to take packet size into account.  Whatever
      packet size it uses, it ensures that its rate approximately equals
      that of a TCP using 1500 byte segments.  This raises the further
      question of whether TCP with 1500 byte 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 mechanism 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 as to 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 Maximum Segment Size (MSS) bytes for each
   ACK received that 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, particularly when the new flows compete with
   long-lived flows and come out of slow-start early (slow-start vs
   overshoot tradeoff).  This convergence problem may even worsen if
   more aggressive congestion control variants are 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.

   -  Evaluating whether it may be appropriate to allow alternative
      starting schemes, e.g., to allow higher initial rates under
      certain constraints [Chu10]; this also requires refining the
      definition of fairness for startup situations.

   -  Better theoretical models for the effects of decreasing
      uncertainty by additional network feedback, particularly 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
   technologies.

3.5.1. Multi-Domain Transport of Explicit Congestion Notification

   Different conditions and their variations lead 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 least
   significant bits of the (former) IPv4 Type of Service (TOS) octet or
   the (former) IPv6 Traffic Class octet [RFC2474] [RFC3260].  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] detail the implications of two
   possible attacks:

   1. non-compliance: a network erasing a Congestion Experienced (CE)
      codepoint introduced earlier on the path, and

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

   Both of these problems 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
   non-compliance problem (number 1 above): the ECN-nonce [RFC3540] and
   the [CONEX] work item inspired by 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 2 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 networks.

   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 that 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 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 uses in-band congestion
   signaling (e.g., Quick-Start) 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 issues resulting from
   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 the Resource
   Reservation Protocol (RSVP)) results in similar scalability issues
   (see Section 3.1).

   Also, many autonomous systems 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 pseudowires across multiple domains poses specific issues.
   Pseudowires (PWs) [RFC3985] may carry non-TCP data flows (e.g., Time-
   Division Multiplexing (TDM) traffic or Constant Bit Rate (CBR) ATM
   traffic) over a multi-domain IP network.  Structure-Agnostic TDM over
   Packet (SAToP) [RFC4553], Circuit Emulation Service over Packet
   Switched Network (CESoPSN) [RFC5086], and TDM over IP (TDMoIP)
   [RFC5087] are not responsive to congestion control as discussed in
   [RFC2914] (see also [RFC5033]).  The same observation applies to ATM
   circuit emulating services (CESs) interconnecting CBR equipment
   (e.g., Private Branch Exchanges (PBX)) across a Packet Switched
   Network (PSN).

   Moreover, it is not possible to simply reduce the flow rate of a TDM
   PW or an ATM PW when facing packet loss.  Providers can rate-control
   corresponding incoming traffic, but they may not be able to detect
   that PWs carry TDM or CBR ATM traffic (mechanisms for characterizing
   the traffic's temporal properties may not necessarily be supported).

   This can be illustrated with the following example.

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

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

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

   The problem arises for transit provider P2 because it 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
   another PW) would follow TCP's AIMD algorithm of reducing the sending
   rate by 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 adapts to the
   available bandwidth using feedback about the state of the network.

   For elastic applications, the transport dynamically adjusts the data
   traffic sending rate to different network conditions.  Examples
   encompass short-lived elastic traffic including HTTP and instant-
   messaging traffic, as well as long file transfers with FTP and
   applications targeted by [LEDBAT].  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 until 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 guarantees for
   unicast flows over the DiffServ/Assured Forwarding (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/CCID 3 for DiffServ/AF networks [Lochin07] [Jourjon08].

   Nevertheless, there is still work to be performed regarding lower-
   precedence traffic -- data transfers that 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 Low Priority (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.  Since early 2010, end-to-end mechanisms for lower
   precedence, e.g., [Shal10], have 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 users will be willing to
   make their background traffic yield to other people's foreground
   traffic, unless the appropriate incentives are created.

   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, using a higher-precedence class of traffic 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 as they experience more
   losses.  Some believe the Internet was designed to allow such
   freedom, so it can hardly be called misbehavior.  But others consider
   it 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 demand
   services that transfer much greater data rates without congestion
   control are becoming popular.  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.  How would such
      mechanisms 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 so significant, or they
   are of a 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 into the network and then measure the
   response time, using, e.g., ICMP.  In contrast, passive measurements
   determine the RTT from ongoing communication processes, without
   sending additional packets.

   The connection endpoints of transport protocols such as TCP, the
   Stream Control Transmission Protocol (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
   acknowledgments.  For TCP, this is the default RTT measurement
   procedure; it is used in combination with Karn's algorithm, which
   prohibits RTT measurements from retransmitted segments [RFC2988].
   Traditionally, TCP implementations take one RTT measurement at a time
   (i.e., about once per RTT).  As an 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 is 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 a
   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-media 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 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 more slowly to sudden changes in 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., [Sarola02]).  There
   are also proposals for more sophisticated estimators, such as Kalman
   filters or estimators that utilize mainly peak values.

   However, open questions related to RTT estimation in the Internet
   remain:

   -  Optimal measurement frequency: Currently, there is no theory or
      common understanding of the right time scale of RTT measurement.
      In particular, the necessity for 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 understood.

   -  Clock granularities: RTT estimation depends on the clock
      granularities of the protocol stacks.  Even though there is a
      trend toward higher-precision timers, 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
   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) [Thaler07].

   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.  Recommendations 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 to the maintainers of
   such devices.

   A possible way to reduce such problems in the future would be
   guidelines for standards authors to ensure that "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, and 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), and not the target rate,
   needs to be inversely proportional to RTT [Jin04].

   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
   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.

   [RFC5129] shows how to propagate ECN from lower layers upwards for
   the specific case of MPLS, 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 a new congestion
   control protocol.  For instance, some proposals maintain a secondary
   Time to Live (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 behavior.

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

   Section 3.5.3 deals with inelastic multi-domain pseudowires (PWs),
   where the identity of the pseudowire itself implies the
   characteristics of the traffic crossing the multi-domain PSN
   (independently of the actual characteristics of the traffic carried
   in the PW).  A more complex situation arises when inelastic traffic
   is carried as part of a pseudowire (e.g., inelastic traffic over
   Ethernet PW over PSN) whose edges do not have the means to
   characterize the properties of the traffic encapsulated in the
   Ethernet frames.  In this case, the problem explained in
   Section 3.5.3 is not limited to multi-domain pseudowires but more
   generally arises from a "pseudowire carrying inelastic traffic"
   (whether over a single- or multi-domain PSN).

   The problem becomes even more intricate when the Ethernet PW carries
   both inelastic and elastic traffic.  Addressing this issue further
   supports our observation that a general framework to efficiently deal
   with congestion control problems in multi-layer networks without
   harming evolvability is absolutely necessary.

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.  The IETF has since initiated a work item
   on multipath TCP [MPTCP].  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 provisioning and traffic engineering, and indeed
   whether multipath congestion control can perform better traffic
   engineering than the network itself, given the right incentives
   [Arkko09].

3.8.6. ALGs and Middleboxes

   An increasing number of application layer gateways (ALGs),
   middleboxes, and proxies (see Section 3.6 of [RFC2775]) are 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
   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 endpoint address from the sender
      perspective is still visible to the receiver (the destination IP
      address).  Relay systems that intercept payloads but do not relay
      congestion control information provide an example.  Such
      middleboxes can prevent the operation of end-to-end congestion
      control.

   -  The "non-transparent" case, which causes fewer problems for
      congestion control.  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 controlled.

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
   require endpoints 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 that attack machines and the
   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 a well-behaved
   Internet user) as a congestion control enforcement problem.  Even
   some denial-of-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. References

5.1. Informative References

   [Allman99]  Allman, M. and V. Paxson, "On Estimating End-to-End
               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
               <http://netlab.caltech.edu/maxnet/XCP_instability.pdf>.

   [Arkko09]   Arkko, J., Briscoe, B., Eggert, L., Feldmann, A., and M.
               Handley, "Dagstuhl Perspectives Workshop on End-to-End
               Protocols for the Future Internet," ACM SIGCOMM Computer
               Communication Review, Vol. 39, No. 2, pp. 42-47, April
               2009.

   [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 A.L. Ananda, "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.

   [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,
               <http://wesii.econinfosec.org/draft.php?paper_id=19>.

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

   [Bri08]     Briscoe, B., Moncaster, T. and L. Burness, "Problem
               Statement: Transport Protocols Don't Have To Do
               Fairness", Work in Progress, July 2008.

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

   [Bri10]     Briscoe, B. and J. Manner, "Byte and Packet Congestion
               Notification," Work in Progress, October 2010.

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

   [Chhabra02] Chhabra, P., Chuig, S., Goel, A., John, A., Kumar, A.,
               Saran, H., and R. Shorey, "XCHOKe: Malicious Source
               Control for Congestion Avoidance at Internet Gateways,"
               Proceedings of IEEE International Conference on Network
               Protocols (ICNP'02), Paris, France, November 2002.

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

   [Clark88]   Clark, D., "The design philosophy of the DARPA internet
               protocols", ACM SIGCOMM Computer Communication Review,
               Vol. 18, No. 4, pp. 106-114, August 1988.

   [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.

   [Chu10]     Chu, J., Dukkipati, N., Cheng, Y., and M. Mathis,
               "Increasing TCP's Initial Window", Work in Progress,
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               McKeown, "Processor Sharing Flows in the Internet",
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   [Dukki06]   Dukkipati, N. and N. McKeown, "Why Flow-Completion Time
               is the Right Metric for Congestion Control", ACM SIGCOMM
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               Program, Grant No. 223936, <http://www.ecode-project.eu>.

   [Falk07]    Falk, A., Pryadkin, Y., and D. Katabi, "Specification for
               the Explicit Control Protocol (XCP)", Work in Progress,
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   [Feldman04]
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               Control Theoretic Analysis of RED", Proceedings of IEEE
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               Jacobson, V., "Congestion Avoidance and Control",
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   [Jain90]    Jain, R., "Congestion Control in Computer Networks:
               Trends and Issues", IEEE Network, pp. 24-30, May 1990.

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               Architecture, Algorithms, Performance", Proceedings of
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   [Jourjon08] Jourjon, G., Lochin, E., and P. Senac, "Design,
               Implementation and Evaluation of a QoS-aware Transport
               Protocol", Elsevier Computer Communications, Vol. 31,
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   [Katabi02]  Katabi, D., M. Handley, and C. Rohrs, "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", Proceedings of PFLDnet'04 Workshop, Argonne
               (Illinois), USA, February 2004.

   [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.

   [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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               control", Presentation at IRTF ICCRG Workshop, PFLDnet
               2007, Los Angeles (California), USA, February 2007.

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               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]    IETF WG Action: Low Extra Delay Background Transport
               (ledbat).

   [Lochin06]  Lochin, E., Dairaine, L., and G. Jourjon, "Guaranteed TCP
               Friendly Rate Control (gTFRC) for DiffServ/AF Network",
               Work in Progress, August 2006.

   [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),
               2002.

   [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
               2003.

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

   [MacK95]    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.

   [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
               2001, Rome, Italy, July 2001.

   [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.

   [MPTCP]     IETF WG Action: Multipath TCP (mptcp).

   [Noel07]    Noel, E. and C. Johnson, "Initial Simulation Results That
               Analyze SIP Based VoIP Networks Under Overload",
               International Teletraffic Congress (ITC'07), Ottawa,
               Canada, June 2007.

   [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 G. Mavromatis, "Stability of
               Congestion Control Algorithms using Control Theory with
               an application to XCP", Technical Report, 2002.
               <http://www.stanford.edu/class/ee384y/projects/
               reports/ionnis.pdf>.

   [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 D. Borman, "TCP Extensions
               for High Performance", RFC 1323, May 1992.

   [RFC1701]   Hanks, S., Li, T., Farinacci, D., and P. Traina, "Generic
               Routing Encapsulation (GRE)", 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 2003,
               October 1996.

   [RFC2018]   Mathis, M., Mahdavi, J., Floyd, S., and A. Romanow, "TCP
               Selective Acknowledgment Options", RFC 2018, October
               1996.

   [RFC2208]   Mankin, A., Ed., Baker, F., Braden, B., Bradner, S.,
               O'Dell, M., Romanow, A., Weinrib, A., and L. Zhang,
               "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 Field (DS
               Field) in the IPv4 and IPv6 Headers", RFC 2474, December
               1998.

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

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

   [RFC2637]   Hamzeh, K., Pall, G., Verthein, W., Taarud, J., Little,
               W., and G. Zorn, "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., 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.

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

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

   [RFC3260]   Grossman, D., "New Terminology and Clarifications for
               Diffserv", RFC 3260, April 2002.

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

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

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

   [RFC3714]   Floyd, S., Ed., and J. Kempf, Ed., "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., Ed., and P. Pate, Ed., "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
               2006.

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

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

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

   [RFC4614]   Duke, M., 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
               2007.

   [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", BCP 133, RFC 5033, August 2007.

   [RFC5086]   Vainshtein, A., Ed., Sasson, I., Metz, E., Frost, T., and
               P. Pate, "Structure-Aware Time Division Multiplexed (TDM)
               Circuit Emulation Service over Packet Switched Network
               (CESoPSN)", RFC 5086, December 2007.

   [RFC5087]   Stein, Y(J)., Shashoua, R., Insler, R., and M. Anavi,
               "Time Division Multiplexing over IP (TDMoIP)", RFC 5087,
               December 2007.

   [RFC5129]   Davie, B., Briscoe, B., and J. Tay, "Explicit Congestion
               Marking in MPLS", RFC 5129, January 2008.

   [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", BCP 145, RFC 5405,
               November 2008.

   [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 E. Blanton, "TCP Congestion
               Control", RFC 5681 (Obsoletes RFC 2581), September 2009.

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

   [RFC6040]   Briscoe, B., "Tunnelling of Explicit Congestion
               Notification", RFC 6040, November 2010.

   [Rossi06]   Rossi, M., "Evaluating TCP with Corruption Notification
               in an IEEE 802.11 Wireless LAN", Master Thesis,
               University of Innsbruck, November 2006.  Available from
               http://heim.ifi.uio.no/michawe/research/projects/
               corruption/.

   [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.

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

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

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

   [Shal10]    Shalunov, S., Hazel, G., and J. Iyengar, "Low Extra Delay
               Background Transport (LEDBAT)", Work in Progress, October
               2010.

   [Shen08]    Shen, C., Schulzrinne, H., and E. Nahum, "Session
               Initiation Protocol (SIP) Server Overload Control: Design
               and Evaluation, Principles", Systems and Applications of
               IP Telecommunications (IPTComm'08), Heidelberg, Germany,
               July 2008.

   [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.

   [Thaler07]  Thaler, D., Sridharan, M., and D. Bansal, "Implementation
               Report on Experiences with Various TCP RFCs",
               Presentation to the IETF Transport Area, March 2007.
               <http://www.ietf.org/proceedings/07mar/
               slides/tsvarea-3/>.

   [Tickoo05]  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),
               Passau, Germany, June 2005.

   [TRILOGY]   "Trilogy Project", European Commission Seventh Framework
               Program (FP7), Grant No: 216372, <http://www.trilogy-
               project.org>.

   [Vinnic02]  Vinnicombe, G., "On the stability of networks operating
               TCP-like congestion control," Proceedings of IFAC World
               Congress, Barcelona, Spain, 2002.

   [Welzl03]   Welzl, M., "Scalable Performance Signalling and
               Congestion Avoidance", Springer (ISBN 1-4020-7570-7),
               September 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
               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., Towsley, D., Hollot, C., 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.

6. 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-
   congestibility).

   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.

7. 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>

Authors' Addresses

   Dimitri Papadimitriou (editor)
   Alcatel-Lucent
   Copernicuslaan, 50
   2018 Antwerpen, Belgium

   Phone: +32 3 240 8491
   EMail: dimitri.papadimitriou@alcatel-lucent.com

   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