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

   Network Working Group Name                             Michael Welzl
   Internet Draft                                 Dimitri Papadimitriou
   Document: draft-irtf-iccrg-wetzl-                            Editors
   congestion-control-open-research-00.txt
                                                         Michael Scharf

   Expires: December 2007                                     July 2007


            Open Research Issues in Internet Congestion Control

      draft-irtf-iccrg-welzl-congestion-control-open-research-00.txt


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   This Internet-Draft will expire on December 31, 2007.

Copyright Notice

   Copyright (C) The IETF Trust (2007).


Abstract

   This document describes many 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


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


Conventions used in this document

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


Table of Contents

   1. Introduction...................................................2
   2. Global Challenges - Overview...................................4
   3. Detailed Challenges............................................4
      3.1 Challenge 1: Router Support................................4
      3.2 Challenge 2: Dynamic Range of Requirements.................7
      3.3 Challenge 3: Corruption Loss...............................8
      3.4 Challenge 4: Small Packets................................10
      3.5 Challenge 5: Pseudo-Wires.................................10
      3.6 Challenge 6: Multi-domain Congestion Control..............12
      3.7 Challenge 7: Precedence for Elastic Traffic...............13
      3.8 Challenge 8: Misbehaving Senders and Receivers............14
      3.9 Other challenges..........................................14
   4. Security Considerations.......................................14
   5. Contributors..................................................14
   6. References....................................................14
   7.1 Normative References.........................................14
      Acknowledgments...............................................17


1. Introduction

   This document describes many 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 is defined as the reduction in utility due to overload in
   networks that support both spatial and temporal multiplexing, but no
   reservation [Keshav]. Congestion control is a distributed algorithm
   to share network resources among competing traffic sources. Two
   components of congestion control have been defined: the primal and
   the dual [Kelly98]. Primal congestion control is based on the traffic
   sources algorithm controlling their sending rates or window sizes



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   depending on the congestion indication feedback signals they get from
   routers (dynamic feedback-based adjustment). TCP algorithms carry out
   the primal iteration. Dual congestion control is implemented by the
   routers through gathering information from the traffic flows that are
   using them. Routers congestion control algorithm updates, implicitly
   or explicitly, a congestion measure and sends it back, implicitly or
   explicitly, to the traffic sources that use that link. Queue
   management algorithms such as Random Early Detection (RED) [Floyd93]
   or Random Exponential Marking (REM) [Ath01] carry out the dual
   iteration.

   Congestion control provides for a fundamental set of mechanisms for
   maintaining the stability and efficiency of the Internet operations.
   Congestion control has been associated with TCP since Van Jacobson's
   work in 1988, but also outside of TCP (e.g. for real-time multimedia
   applications, multicast, and router-based mechanisms). The Van
   Jacobson end-to-end congestion control algorithms [Jacobson88]
   [RFC2581] are used by the Internet transport protocols TCP [RFC793].
   They have been proven to be highly successful over many years but
   have begun to reach their limits. Indeed, heterogeneity of both data
   link/physical layer and applications are pulling TCP congestion
   control (that performs poorly as bandwidth or delay increases)
   outside of its natural operating regime. A side effect of these
   deficits is that there is an increasing share of hosts that use non-
   standardized congestion control enhancements (for instance, many
   Linux distributions are shipped with "CUBIC" as default TCP
   congestion control.)

   From the original Jacobson algorithm requiring no congestion-related
   state in routers, more recent modifications have backed off from this
   purity. Active Queue Management (AQM) in routers, e.g., RED and all
   its variants, xCHOKE [Pan00], RED with In/Out (RIO) [Clark98], etc.
   improves performance by keeping queues small (implicit feedback),
   while Explicit Congestion Notification (ECN) [Floyd94] [RFC3168]
   passes one bit of congestion information back to senders. These
   measures do improve performance, but there is a limit to how much can
   be accomplished without more information from routers. The
   requirement of extreme scalability together with robustness has been
   a difficult hurdle to accelerating information flow. Primal-Dual
   TCP/AQM distributed algorithm stability and equilibrium properties
   have been extensively studied in [Low02] [Low03].

   In addition, 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.



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2. Global Challenges - Overview

3. Detailed Challenges

3.1 Challenge 1: Router Support

   Routers can be involved in congestion control in two ways: First,
   they can implicitly optimize their functions, such as queue
   management and scheduling strategies, in order to support the
   operation of an end-to-end congestion control.

   Various approaches have been proposed and also deployed, such as
   different AQM techniques. Even though these implicit techniques are
   known to improve network performance during congestion phases, they
   are still only partly deployed in the Internet. This may be due to
   the fact that finding optimal and robust parameterizations for these
   mechanisms is a non-trivial problem. Indeed, the problem with various
   AQM schemes is the difficulty to identify correct values of the
   parameter set that affects the performance of the queuing scheme (due
   to variation in the number of sources, the capacity and the feedback
   delay) [Fioriu00] [Hollot01] [Zhang03]. None of the AQM schemes (RED,
   REM, BLUE, PI-Controller but also Adaptive Virtual Queue (AVQ) define
   a systematic rule for setting its parameters.

   Second, routers can participate in congestion control by explicit
   notification mechanisms. By such feedback from the network,
   connection endpoints can obtain more accurate information about the
   current network characteristics on the path. This allows endpoints to
   make more precise decisions that can better prevent packet loss and
   that can also improve fairness among different flows. Examples for
   explicit router feedback include Explicit Congestion Notification
   (ECN) [RFC3168], Quick-Start [RFC4782], and eXplicit Control Protocol
   (XCP) [Katabi02] [Falk07].

   With increasing the per-flow bandwidth-delay product increases, TCP
   becomes inefficient and prone to instability, regardless of the
   queuing scheme. XCP, which generalizes ECN, has been developed to
   address these issues, using per-packet feedback. By decoupling
   resource utilization/congestion control from fairness control, XCP
   outperforms TCP in conventional and high bandwidth-delay
   environments, and remains efficient, fair, scalable, and stable
   regardless of the link capacity, the round trip delay, and the number
   of sources. XCP aims at achieving fair bandwidth allocation, high
   utilization, 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 portable to high-speed routers. However, XCP
   is still subject to research efforts: [Andrew05] has recently pointed
   out cases where in which XCP is stable locally but unstable globally


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   (when the maximum RTT of a flow is much larger than the mean RTT).
   This instability can be removed by setting the estimation interval to
   be the maximum observed RTT, rather than the mean RTT. Nevertheless,
   this makes the system vulnerable to erroneous RTT advertisements.
   [PAP02] shows that when flows with different RTTs are applied, XCP
   sometimes discriminates among heterogeneous traffic flows, even if
   XCP is generally fair to different flows even if they belong to
   significantly heterogeneous flows. [Low05] provides for a complete
   characterization of the XCP equilibrium properties.

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

3.1.1 Performance and robustness

   Congestion control requires 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 and conservative in
   particular during congestion phases.

   Router support can help to improve performance and fairness, but it
   can also result in additional complexity and more control loops. This
   requires a careful design of the algorithms in order to ensure
   stability and avoid e.g. oscillations. A further challenge is the
   fact that information may be imprecise. For instance, severe
   congestion can delay feedback signals. Also, the measurement of
   parameters such as round-trip times (RTT) 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 routers theoretically improve performance in the
     complete range of communication scenarios that exists in the
     Internet?

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

3.1.2 Granularity of router functions

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



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   router processing a challenge for Internet scalability and could also
   increase the end-to-end latencies.

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

   Open questions are:

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

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

3.1.3 Information acquisition

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

   1. Capacity of (outgoing) links

   Link characteristics depend on the realization of lower protocol
   layers. Routers do not necessarily know the link layer network
   topology and link capacities, and these are not necessarily constant
   (e. g., on shared wireless links). Difficulties also arise when using
   IP-in-IP tunnels [RFC 2003] or MPLS [RFC3031] [RFC3032]. In these
   cases, link information could be determined by cross-layer
   information exchange, but this requires link layer technology
   specific interfaces. An alternative could be online measurements, but
   this can cause significant additional network overhead.

   2. Traffic carried over (outgoing) links

   Accurate online measurement of data rates is challenging when traffic
   is bursty. For instance, it is impossible to define and measure a
   current link load. This is a challenge for proposals that require
   knowledge e.g. about the current link utilization.

   3. Internal buffer statistics

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




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

3.1.4 Feedback signaling

   Explicit notification mechanisms can be realized either by in-band
   signaling or by out-of-band signaling. The latter case requires
   additional protocols and can be further subdivided into path-coupled
   and path-decoupled approaches.

   In-band signaling can be considered to be an appropriate choice:
   Since notifications are piggy-packet along with data traffic, there
   is less overhead and implementation complexity remains limited. Path-
   coupled out-of-band signaling could however be possible, too.

   Open questions concerning feedback signaling include:

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

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

3.2 Challenge 2: Dynamic Range of Requirements

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

   Additionally, neither available bandwidth nor end-to-end delays are
   constant. At the IP layer, competing cross-traffic, traffic
   management in routers, and dynamic routing can result in sudden
   changes of the characteristics of the path followed from the source
   to the destination. Additional dynamics can be caused by link layer
   mechanisms, such as shared media access (e.g., in wireless networks),
   changes of links (horizontal/vertical handovers), topology
   modifications (e. g., in ad-hoc networks), link layer error
   correction, dynamic bandwidth provisioning schemes, etc. From this



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   follows that path characteristics can be subject to substantial
   changes within short time frames.

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

   This gave rise to a multitude of new proposals for congestion control
   algorithms. For instance, since the additive-increase multiplicative
   decrease (AIMD) principle of TCP does not scale well to large
   congestion window sizes, several high-speed congestion control
   extensions have been developed recently, such as High-Speed TCP,
   Scalable TCP, Fast TCP and BIC/CUBIC. However, these new algorithms
   raise fairness issues, and they may be less robust in certain
   situations for which they have not been designed.

   However, there is still no common agreement in the IETF on which
   algorithm and protocol to choose. For instance, XCP could solve some
   problems caused by high bandwidth-delay products, at the cost of some
   additional complexity in routers. Also note that XCP may have some
   problems with dynamic changes of link layer characteristics as they
   are discussed in this section (shared media etc.). Similarly,
   proprietary congestion control mechanisms have been proposed for
   other specific environments, e.g., to cope with highly variable data
   rates.

   It is always possible to tune congestion control parameters based on
   some knowledge about the environment and the application scenario.
   However, the fundamental question is whether it is possible to define
   one congestion control mechanism that operates reasonable well in the
   whole range of scenarios that exist in the Internet. Hence, it is an
   open research question how such a "unified" congestion control would
   have to be designed, and which maximum degree of dynamics it could
   efficiently handle.

3.3 Challenge 3: 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



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   networks, packets can be dropped because of corruption, rendering the
   typical reaction of a congestion control mechanism inappropriate.

   TCP over wireless and satellite is a topic that has been investigated
   for a long time [Krishnan04]. There are some proposals where the
   congestion control mechanism would react as if a packet had not been
   dropped in the presence of corruption (cf. TCP HACK [MW1]), but
   discussions in the IETF have shown that there is no agreement that
   this type of reaction is appropriate. It has been said that
   congestion can manifest itself as corruption on shared wireless
   links, and in any case it is questionable whether a source that sends
   packets that are continuously impaired by link noise should keep
   sending at a high rate.

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

   1. How is corruption detected?

   2. What should be the reaction?

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

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

   Header checksums provide another implicit detection possibility: if a
   checksum covers all necessary headers only 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, and it was found to work well over a GPRS network in a
   study [MW2] and poorly over a WiFi network in another study [MW3].
   Note that, while UDP-Lite and DCCP enable the detection of
   corruption, the specifications of these protocols do not foresee any
   specific reaction to it for the time being.

   The idea of having a transport endpoint detect and accordingly react
   to corruption poses a number of interesting questions regarding
   cross-layer interactions. As IP is designed to operate over arbitrary
   link layers, it is therefore difficult to design a congestion control
   mechanism on top of it, which appropriately reacts to corruption -
   especially as the specific data link layers that are in use along an


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   end-to-end path are typically unknown to entities at the transport
   layer.

   The IETF has not yet specified how a congestion control mechanism
   should react to corruption.

3.4 Challenge 4: Small Packets

   With multimedia streaming flows becoming common, an increasingly
   large fraction of the bytes transmitted belong to control traffic.
   Compounding the congestion control, small packets may excessively
   contribute to lower network efficiency in terms of full-size packet
   transfer performance.

   For small packets, the Nagle algorithm allows to avoid congestion
   collapse and pathological congestion [RFC896]. The Nagle algorithm
   can dramatically reduce the number of small packets. However,
   aggregation implies delay for packets. Applications that are jitter-
   sensitive typically disable the Nagle algorithm. For applications
   that exchange small packets, variants for the small packet to the
   TCP-friendly rate control (TFRC) [RFC3448] in the Datagram Congestion
   Control Protocol (DCCP) [RFC4340] have been designed. DCCP enables
   unreliable but congestion-controlled data transmission. TFRC is a
   congestion control mechanism for unicast flows operating in a best-
   effort Internet environment, and is designed for DCCP that controls
   the sending rate based on a stochastic Markov model for TCP Reno.
   Consistent with the use of end-to-end congestion control, versions of
   the Congestion Control Identifier (CCID) have dealt with DCCP flows
   that would like to receive as much bandwidth as possible over the
   long term (CCID 2) [RFC4241], or flows that minimize the abrupt rate
   changes in the sending rate (CCID 3) [RFC4242].

   In its version number 4 [draft-floyd-ccid4-00.txt], CCID is being
   designed either to applications programs that use a small fixed
   segment size, or to application programs that change their sending
   rate by varying the segment size.

   In some stable and unstable conditions, it appears that the
   congestion control mechanisms for small packets must be further
   enhanced, tightly coordinated, and controlled over wide-area
   networks.

3.5 Challenge 5: Pseudo-Wires

   Pseudowires (PW) may carry non-TCP data flows e.g. TDM traffic.
   Structure Agnostic TDM over Packet (SATOP) [RFC4553], Circuit
   Emulation over Packet Switched Networks (CESoPSN), TDM over IP, are
   not responsive to congestion control in a TCP-friendly manner as



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   prescribed by [RFC2914]. Moreover, it is not possible to simply
   reduce the flow rate of a TDM PW when facing packet loss.

   Carrying TDM PW over an IP network poses a real problem. Indeed,
   providers can rate control corresponding incoming traffic but it may
   not be able to detect that a PW carries TDM traffic. This can be
   illustrated with the following example.

   Sources S1, S2, S3 and S4 are originating TDM over IP traffic. P1
   provider edges E1, E2, E3, and E4 are respectively rate limiting such
   traffic. Provider P1 SLA 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 rises for transit provider P2 that is not able to detect
   that IP packets are carrying constant-bit rate service traffic that
   is by definition unresponsive to any congestion control mechanisms.


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

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


   Assuming P1 providers are rate limiting BE traffic, a transit P2
   provider router R may be subject to serious congestion as all TDM PWs
   cross the same router. TCP-friendly traffic would follow existing
   TCP's Additive-Increase Multiplicative-Decrease (AIMD) algorithm of
   reducing the sending rate in half in response to each packet drop.
   Nevertheless, the TDM PWs will take all available capacity leaving no


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   room for any other type of traffic. Note that the situation may
   simply occur because S4 suddenly turns up a TDM PW.

   As it is not possible to assume that edge routers will soon have the
   ability to detect the type of the carried traffic, it is important
   for transit routers (P2 provider) to be able to apply a fair, robust,
   responsive and efficient congestion control technique such as to
   prevent impacting normal-behaving Internet traffic. However, it is
   still an open question how the corresponding mechanisms in data and
   control plane have to be designed.


3.6 Challenge 6: Multi-domain Congestion Control

   Transport protocols such as TCP operate over the Internet that is
   divided into autonomous systems. These systems are characterized by
   their heterogeneity as IP networks are realized by a multitude of
   technologies. Variety of conditions (see also Challenge 2) and their
   variations leads to correlation effects between policers that
   regulate traffic against certain conformance criteria.

   With the advent of techniques allowing for early detection of
   congestion, packet loss is no longer the solely 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 you an indication of the level of congestion). Using TCP
   ACKs to feed back that information allows the hosts to realign their
   transmission rate and thus encourage them to efficiently use of the
   network. In IP, ECN uses the two unused bits of the TOS field
   [RFC2474]. Further, ECN in TCP uses two bits in the TCP header that
   were previously defined as reserved [RFC793].

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

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




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   Another challenge in multi-domain operation is security. At some
   domain boundaries, an increasing number of application layer gateways
   (e. g., proxies) is deployed, which split up end-to-end connections
   and prevent end-to-end congestion control. Furthermore,
   authentication and authorization issues can arise at domain
   boundaries, whenever information is exchanged, and so far the
   Internet does not have a single general security architecture that
   could be used in all cases. Many autonomous systems also only
   exchange some limited amount of information about their internal
   state (topology hiding principle), even though having more precise
   information could be highly beneficial for congestion control. The
   future evolution of the Internet inter-domain operation has to show
   whether more multi-domain information exchange can be realized.

3.7 Challenge 7: Precedence for Elastic Traffic

   Elastic traffic initiated by so-called elastic data applications
   adapt to available bandwidth via a feedback control loop such as the
   TCP congestion control. There are two types of "as-soon-as-possible"
   traffic types: short-lived flows and flows with an expected average
   throughput. For all those flows the application dynamically adjusts
   the data generation rate. Examples of short-lived elastic traffic
   include HTTP and instant messaging traffic. Examples of average
   throughput requiring elastic traffic are FTP and emailing. In brief,
   elastic data applications can show extremely different requirements
   and traffic characteristics.

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

   For instance, low precedence traffic will experience lower average
   throughput than higher precedence traffic. Several questions arise,
   however. What is the meaning of "relative"? What is the role of the
   Transport Layer in providing the respective considerations for
   precedence wrt to serviced applicative traffic?

   The preferential treatment of higher precedence traffic with
   appropriate congestion control mechanisms is still an open issue that
   may, depending on the proposed solution, impact both the host and the
   network precedence awareness, and thereby the congestion control.

   DiffServ [RFC2474] [RFC2475] related aspects will be addressed in a
   future release of this document.




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3.8 Challenge 8: Misbehaving Senders and Receivers

   TBD.

3.9 Other challenges

   TBD.

4. Security Considerations

5. Contributors

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

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

6. References

7.1 Normative References


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

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

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

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

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

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



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

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

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

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

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

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

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

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

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

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

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

7.2 Informative References

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





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Open Research Issues in Internet Congestion Control          July 2007


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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

Acknowledgments

   The authors would like to thank Jan Vandenabeele for its comments on
   the document.

Author's Addresses

   Michael Welzl
   University of Innsbruck
   Technikerstr 21a
   A-6020 Innsbruck, Austria
   Phone: +43 (512) 507-6110
   Email: michael.welzl@uibk.ac.at

   Dimitri Papadimitriou
   Alcatel-Lucent


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

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







































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Full Copyright Statement

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