One document matched: draft-irtf-iccrg-welzl-congestion-control-open-research-00.txt
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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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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[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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