One document matched: draft-irtf-iccrg-welzl-congestion-control-open-research-01.txt
Differences from draft-irtf-iccrg-welzl-congestion-control-open-research-00.txt
Network Working Group Michael Welzl
Internet Draft Dimitri Papadimitriou
Document: draft-irtf-iccrg-wetzl- Editors
congestion-control-open-research-01.txt
Michael Scharf
Bob Briscoe
Expires: October 2008 April 2008
Open Research Issues in Internet Congestion Control
draft-irtf-iccrg-welzl-congestion-control-open-research-01.txt
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Abstract
This document describes some of the open problems in Internet
congestion control that are known today. This includes several new
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challenges that are becoming important as the network grows, as well
as some issues that have been known for many years. These challenges
are generally considered to be open research topics that may require
more study or application of innovative techniques before Internet-
scale solutions can be confidently engineered and deployed.
Conventions used in this document
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC-2119 [i].
Table of Contents
1. Introduction...................................................3
2. Global Challenges - Overview...................................4
2.1 Heterogeneity..............................................4
3. Detailed Challenges............................................8
3.1 Challenge 1: Router Support................................8
3.2 Challenge 2: Corruption Loss..............................11
3.3 Challenge 3: Small Packets................................13
3.4 Challenge 4: Pseudo-Wires.................................17
3.5 Challenge 5: Multi-domain Congestion Control..............18
3.6 Challenge 6: Precedence for Elastic Traffic...............19
3.7 Challenge 7: Misbehaving Senders and Receivers............20
3.8 Other challenges..........................................21
4. Security Considerations.......................................24
5. Contributors..................................................24
6. References....................................................24
7.1 Normative References.........................................24
Acknowledgments...............................................30
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1. Introduction
This document describes some of the open research topics in the
domain of Internet congestion control that are known today. We begin
by reviewing some proposed definitions of congestion and congestion
control based on current understandings.
Congestion can be defined as the reduction in utility due to overload
in networks that support both spatial and temporal multiplexing, but
no reservation [Keshav]. Congestion control is a (typically
distributed) algorithm to share network resources among competing
traffic sources. Two components of distributed congestion control
have been defined: the primal and the dual [Kelly98]. Primal
congestion control refers to the algorithm executed by the traffic
sources algorithm for controlling their sending rates or window
sizes. This normally a closed-loop control, where this operation
depends on feedback. TCP algorithms fall in the "primal" category.
Dual congestion control is implemented by the routers through
gathering information about the traffic traversing them. A dual
congestion control algorithm updates, implicitly or explicitly, a
congestion measure and sends it back, implicitly or explicitly, to
the traffic sources that use that link. Queue management algorithms
such as Random Early Detection (RED) [Floyd93] or Random Exponential
Marking (REM) [Ath01] fall in the "dual" category.
Congestion control provides for a fundamental set of mechanisms for
maintaining the stability and efficiency of the Internet. Congestion
control has been associated with TCP since Van Jacobson's work in
1988, but there is also congestion control outside of TCP (e.g. for
real-time multimedia applications, multicast, and router-based
mechanisms). The Van Jacobson end-to-end congestion control
algorithms [Jacobson88] [RFC2581] are used by the Internet transport
protocol TCP [RFC4614]. They have been proven to be highly successful
over many years but have begun to reach their limits, as the
heterogeneity of both the data link and physical layer and
applications are pulling TCP congestion control (which performs
poorly as the bandwidth or delay increases) outside of its natural
operating regime. A side effect of these deficits is that there is an
increasing share of hosts that use non-standardized congestion
control enhancements (for instance, many Linux distributions have
been shipped with "CUBIC" as default TCP congestion control
mechanism.)
While the original Jacobson algorithm requires no congestion-related
state in routers, more recent modifications have departed from the
strict application of the end-to-end / transparency principle. Active
Queue Management (AQM) in routers, e.g., RED and all its variants,
xCHOKE [Pan00], RED with In/Out (RIO) [Clark98], improves performance
by keeping queues small (implicit feedback via dropped packets),
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while Explicit Congestion Notification (ECN) [Floyd94] [RFC3168]
passes one bit of congestion information back to senders when an AQM
would normally drop a packet. These measures do improve performance,
but there is a limit to how much can be accomplished without more
information from routers. The requirement of extreme scalability
together with robustness has been a difficult hurdle to accelerating
information flow. Primal-Dual TCP/AQM distributed algorithm stability
and equilibrium properties have been extensively studied (cf. [Low02]
[Low03]).
Congestion control includes many new challenges that are becoming
important as the network grows in addition to the issues that have
been known for many years. These are generally considered to be open
research topics that may require more study or application of
innovative techniques before Internet-scale solutions can be
confidently engineered and deployed. In what follows, an overview of
some of these challenges is given.
2. Global Challenges
This section describes the global challenges to be addressed in the
domain of Internet congestion control.
2.1 Heterogeneity
The Internet encompasses a large variety of heterogeneous IP networks
that are realized by a multitude of technologies, which result in a
tremendous variety of link and path characteristics: capacity can be
either scarce in very slow speed radio links (several kbps), or there
may be an abundant supply in high-speed optical links (several
gigabit per second). Concerning latency, scenarios range from local
interconnects (much less than a millisecond) to certain wireless and
satellite links with very large latencies (up to a second). Even
higher latencies can occur in interstellar communication. As a
consequence, both the available bandwidth and the end-to-end delay in
the Internet may vary over many orders of magnitude, and it is likely
that the range of parameters will further increase in future.
Additionally, neither the available bandwidth nor the end-to-end
delay is constant. At the IP layer, competing cross-traffic, traffic
management in routers, and dynamic routing can result in sudden
changes of the characteristics of an end-to-end path. Additional
dynamics can be caused by link layer mechanisms, such as shared media
access (e.g., in wireless networks), changes of links
(horizontal/vertical handovers), topology modifications (e. g., in
ad-hoc networks), link layer error correction and dynamic bandwidth
provisioning schemes. From this follows that path characteristics can
be subject to substantial changes within short time frames.
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The congestion control algorithms have to deal with this variety in
an efficient way. The congestion control principles introduced by Van
Jacobson assume a rather static scenario and implicitly target
configurations where the bandwidth-delay product is of the order of
some dozens of packets at most. While these principles have proved to
work well in the Internet for almost two decades, much larger
bandwidth-delay products and increased dynamics challenge them more
and more. There are many situations where today's congestion control
algorithms react in a suboptimal way, resulting in low resource
utilization, non-optimal congestion avoidance, or unfairness.
This gave rise to a multitude of new proposals for congestion control
algorithms. For instance, since the Additive-Increase Multiplicative
Decrease (AIMD) behavior of TCP is too conservative in practical
environments when then congestion window is large, several high-speed
congestion control extensions have been developed. However, these new
algorithms raise fairness issues, and they may be less robust in
certain situations for which they have not been designed. Up to now,
there is still no common agreement in the IETF on which algorithm and
protocol to choose.
It is always possible to tune congestion control parameters based on
some knowledge about the environment and the application scenario.
However, the fundamental question is whether it is possible to define
one congestion control mechanism that operates reasonable well in the
whole range of scenarios that exist in the Internet. Hence, it is an
important research question how such a "unified" congestion control
would have to be designed, and which maximum degree of dynamics it
could efficiently handle.
2.2 Stability
Control theory, which is a mathematical tool for describing dynamic
systems, lends itself to modeling congestion control - TCP is a
perfect example of a typical "closed loop" system that can be
described in control theoretic terms. In control theory, there is a
mathematically defined notion of system stability. In a stable
system, for any bounded input over any amount of time, the output
will also be bounded. For congestion control, what is actually meant
with stability is typically asymptotic stability: a mechanism should
converge to a certain state irrespective of the initial state of the
network.
Control theoretic modeling of a realistic network can be quite
difficult, especially when taking distinct packet sizes and
heterogeneous RTTs into account. It has therefore become common
practice to model simpler cases and leave the more complicated
(realistic) situations for simulations. Clearly, if a mechanism is
not stable in a simple scenario, it is generally useless; this method
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therefore helps to eliminate faulty congestion control candidates at
an early stage.
Some fundamental facts, which are known from control theory are
useful as guidelines when designing a congestion control mechanism.
For instance, a controller should only be fed a system state that
reflects its output. A (low-pass) filter function should be
used in order to pass only states to the controller that are
expected to last long enough for its action to be meaningful
[Jain88]. Action should be carried out whenever such feedback
arrives, as it is a fundamental principle of control that the control
frequency should be equal to the feedback frequency. Reacting faster
leads to oscillations and instability while reacting slower makes the
system tardy [Jain90].
TCP stability can be attributed to two key aspects which were
introduced in [Jacobson88]: the AIMD control law during congestion
avoidance, which is based on a simple, vector based analysis of two
controllers sharing one resource with synchronous RTTs [Chiu89], and
the "conservation of packets principle", which, once the control has
reached "steady state", tries to maintain an equal amount of packets
in flight at any time by only sending a packet into the network when
a packet has left the network (as indicated by an ACK arriving at the
sender). The latter aspect has guided many decisions regarding
changes that were made to TCP over the years.
The reasoning in [Jacobson88] assumes all senders to be acting at the
same time. The stability of TCP under more realistic network
conditions has been investigated in a large number of ensuing works,
leading to no clear conclusion that TCP would also be asymptotically
stable under arbitrary network conditions.
2.3 Fairness
Recently, the way the Internet community reasons about fairness has
been called into deep questioning [Bri07]. Much of the community has
taken fairness to mean approximate equality between the rates of
flows (flow rate fairness) that experience equivalent path congestion
as with TCP [RFC2581] and TFRC [RFC3448]. [RFC3714] depicts the
resulting situation as "The Amorphous Problem of Fairness".
A parallel tradition has been built on [Kelly98] where, as long as
each user is accountable for the cost their rate causes to others
[MKMV95], the set of rates that everyone chooses is deemed fair (cost
fairness)---because with any other set of choices people would lose
more value than they gained overall.
In comparison, the debate between max-min, proportional and TCP
fairness is about mere details. These three all share the assumption
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that equal flow rates are desirable; they merely differ in the second
order issue of how to share out excess capacity in a network of many
bottlenecks. In contrast, cost fairness should lead to extremely
unequal flow rates by design. Equivalently, equal flow rates would
typically be considered extremely unfair.
The two traditional approaches are not protocol options that can each
be followed in different parts of a network. They result in research
agendas and issues that are different in their respective objectives
resulting in different set of open issues.
If we assume TCP-friendliness as a goal with flow rate as the metric,
open issues would be:
- Should rate fairness depend on the packet rate or the bit rate?
- Should flow rate depend on RTT (as in TCP) or whether only flow
dynamics should depend on RTT (e.g. as in Fast TCP [Jin04])?
- How to estimate whether a particular flow start strategy is fair?
Whether a particular fast recovery strategy after a reduction in
rate due to congestion is fair?
- If an application needs still smoother flows than TFRC, or it needs
to burst occasionally, or any other application behavior, how
should to judge what is reasonably fair?
- During brief congestion bursts (e.g. due to new flow arrivals) how
to judge at what point it becomes unfair for some flows to continue
at a smooth rate while others reduce their rate?
- Which mechanism(s) to enforce approximate flow rate fairness?
- How can we introduce some degree of fairness that takes account of
flow duration? 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) to give accountability for causing
congestion?
- How to design one or two generic transport protocols (such as to
TCP, UDP, etc.) with the addition of application policy control?
- Which policy enforcement by networks and interactions between
application policy and network policy enforcement?
- Competition with flows aiming for rate equality (e.g. TCP);
The question of how to reason about fairness is a pre-requisite to
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agreeing the research agenda. However, that question does not require
more research in itself, it is merely a debate that needs to be
resolved by studying existing research and by assessing how bad
fairness problems could become if they are not addressed rigorously.
3. Detailed Challenges
3.1 Challenge 1: Router Support
Routers can be involved in congestion control in two ways: first,
they can implicitly optimize their functions, such as queue
management and scheduling strategies, in order to support the
operation of an end-to-end congestion control.
Various approaches have been proposed and also deployed, such as
different AQM techniques. Even though these implicit techniques are
known to improve network performance during congestion phases, they
are still only partly deployed in the Internet. This may be due to
the fact that finding optimal and robust parameterizations for these
mechanisms is a non-trivial problem. Indeed, the problem with various
AQM schemes is the difficulty to identify correct values of the
parameter set that affects the performance of the queuing scheme (due
to variation in the number of sources, the capacity and the feedback
delay) [Fioriu00] [Hollot01] [Zhang03]. Many AQM schemes (RED, REM,
BLUE, PI-Controller but also Adaptive Virtual Queue (AVQ)) do not
define a systematic rule for setting their parameters.
Second, routers can participate in congestion control via 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].
As the per-flow bandwidth-delay product increases, TCP becomes
inefficient and prone to instability, regardless of the queuing
scheme. XCP is a well-known scheme that has been developed to address
these issues with 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 time (RTT), and the number of sources. XCP
aims at achieving fair bandwidth allocation, high utilization, a
small standing queue size, and near-zero packet drops, with both
steady and highly varying traffic. Importantly, XCP does not maintain
any per-flow state in routers and requires few CPU cycles per packet,
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hence making it potentially applicable in high-speed routers.
However, XCP is still subject to research efforts: [Andrew05] has
recently pointed out cases where 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 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. The authors of [PAP02] have shown that, when flows
with different RTTs are applied, XCP sometimes discriminates among
heterogeneous traffic flows, even if XCP is generally fair to
different flows 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 is subject to some tradeoffs: on one hand, it must
allow high link utilizations and fair resource sharing but on the
other hand, the algorithms must also be robust and conservative in
particular during congestion phases.
Router support can help to improve performance and fairness, but it
can also result in additional complexity and more control loops. This
requires a careful design of the algorithms in order to ensure
stability and avoid e.g. oscillations. A further challenge is the
fact that information may be imprecise. For instance, severe
congestion can delay feedback signals. Also, the measurement of
parameters such as RTTs or data rates may contain estimation errors.
Even though there has been significant progress in providing
fundamental theoretical models for such effects, research has not
completely explored the whole problem space yet.
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?
- What is the minimum support that is needed from routers in order
to achieve significantly better performance than with end-to-end
mechanisms?
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3.1.2 Granularity of router functions
There are several degrees of freedom concerning router involvement,
ranging from some few additional functions in network management
procedures one the one end, and additional per packet processing on
the other end of the solution space. Furthermore, different amounts
of state can be kept in routers (no per-flow state, partial per-flow
state, soft state, hard state). The additional router processing is a
challenge for Internet scalability and could also increase end-to-end
latencies.
There are many solutions that do not require per-flow state and thus
do not cause a large processing overhead. However, scalability issues
could also be caused, for instance, by synchronization mechanisms for
state information among parallel processing entities, which are e. g.
used in high-speed router hardware designs.
Open questions are:
- What granularity of router processing can be realized without
affecting Internet scalability?
- How can additional processing efforts be kept at a minimum?
3.1.3 Information acquisition
In order to support congestion control, routers have to obtain at
least a subset of the following information. Obtaining that
information may result in complex tasks.
1. Capacity of (outgoing) links
Link characteristics depend on the realization of lower protocol
layers. Routers do not necessarily know the link layer network
topology and link capacities, and these are not always 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, 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.
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3. Internal buffer statistics
Some proposals use buffer statistics such as a virtual queue length
to trigger feedback. However, routers can include multiple
distributed buffer stages that make it difficult to obtain such
metrics.
Open questions are: Can and should this information be made
available, e.g., by additional interfaces or protocols?
3.1.4 Feedback signaling
Explicit notification mechanisms can be realized either by in-band
signaling (notifications piggybacked along with the data traffic) or
by out-of-band signaling. The latter case requires additional
protocols and can be further subdivided into path-coupled and path-
decoupled approaches.
Open questions concerning feedback signaling include:
- At which protocol layer should the feedback signaling occur
(IP/network layer assisted, transport layer assisted, hybrid
solutions, shim layer, intermediate sub-layer, etc.) ?
- What is the optimal frequency of feedback (only in case of
congestion events, per RTT, per packet, etc.)?
3.2 Challenge 2: Corruption Loss
It is common for congestion control mechanisms to interpret packet
loss as a sign of congestion. This is appropriate when packets are
dropped in routers because of a queue that overflows, but there are
other possible reasons for packet drops. In particular, in wireless
networks, packets can be dropped because of corruption, rendering the
typical reaction of a congestion control mechanism inappropriate.
TCP over wireless and satellite is a topic that has been investigated
for a long time [Krishnan04]. There are some proposals where the
congestion control mechanism would react as if a packet had not been
dropped in the presence of corruption (cf. TCP HACK [BALAN01]), but
discussions in the IETF have shown that there is no agreement that
this type of reaction is appropriate. For instance, it has been said
that congestion can manifest itself as corruption on shared wireless
links, and 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.
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Generally, two questions must be addressed when designing congestion
control mechanism that takes corruption into account:
1. How is corruption detected?
2. What should be the reaction?
In addition to question 1 above, it may be useful to consider
detecting the reason for corruption, but this has not yet been done
to the best of our knowledge.
Corruption detection can be done using an in-band or out-of-band
signaling mechanism, much in the same way as described for
Challenge 1. Additionally, implicit detection can be considered: link
layers sometimes retransmit erroneous frames, which can cause the
end-to-end delay to increase - but, from the perspective of a sender
at the transport layer, there are many other possible reasons for
such an effect.
Header checksums provide another implicit detection possibility: if a
checksum only covers all the necessary header fields and this
checksum does not show an error, it is possible for errors to be
found in the payload using a second checksum. Such error detection is
possible with UDP-Lite and DCCP; it was found to work well over a
GPRS network in a study [Chester04] and poorly over a WiFi network in
another study [Rossi06] [Welzl08]. Note that, while UDP-Lite and DCCP
enable the detection of corruption, the specifications of these
protocols do not foresee any specific reaction to it for the time
being.
The idea of having a transport endpoint detect and accordingly react
to corruption poses a number of interesting questions regarding
cross-layer interactions. As IP is designed to operate over arbitrary
link layers, it is therefore difficult to design a congestion control
mechanism on top of it, which appropriately reacts to corruption -
especially as the specific data link layers that are in use along an
end-to-end path are typically unknown to entities at the transport
layer.
The IETF has not yet specified how a congestion control mechanism
should react to corruption.
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?
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3.3 Challenge 3: Small Packets
Over past years, the performance of TCP congestion avoidance
algorithms has been extensively studied. The square root formula of
[Padye98] provides the performance of the TCP congestion avoidance
algorithm for TCP Reno [RFC2581]. The PKFT model enhances the square
root formula to account for timeouts, receiver window, and delayed
ACKs. This formula validated by many experiments is insensitive to
the TCP flavor. However, large portion of TCP flows are short-lived
short-transfers, for which delay is dominated by slow-start.
For the sake of the present discussion, we will assume that the TCP
throughput is expressed using the simplified SQRT formula. Using this
formula, the TCP throughput is inversely proportional to the RTT and
the square root of the drop probability:
MSS 1
B ~ C --- -------
RTT sqrt(p)
where
MSS is the TCP segment size (in bytes)
RTT is the end-to-end round trip time of the TCP connection (in
seconds)
p is the packet drop probability
Observing that TCP is not suited for applications such as streaming
media (since reliable in-order delivery and congestion control can
cause arbitrarily long delays), the Datagram Congestion Control
Protocol (DCCP) [RFC4340] has been designed. DCCP enables unreliable
but congestion-controlled datagram flow transmission avoiding the
arbitrary delays associated with TCP. DCCP is intended for
applications such as streaming media that can benefit from control
over the tradeoffs between delay and reliable in-order delivery.
DCCP provides for a choice of modular congestion control mechanisms.
DCCP uses Congestion Control Identifiers (CCIDs) to specify the
congestion control mechanism. Three profiles are currently specified:
- DCCP Congestion Control ID 2 (CCID 2) [RFC4341]:
TCP-like Congestion Control. CCID 2 sends data using a close
variant of TCP's congestion control mechanisms, incorporating a
variant of SACK [RFC2018, RFC3517]. CCID 2 is suitable for senders
who can adapt to the abrupt changes in congestion window typical of
TCP's AIMD congestion control, and particularly useful for senders
who would like to take advantage of the available bandwidth in an
environment with rapidly changing conditions.
- DCCP Congestion Control ID 3 (CCID 3) [RFC4342]:
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TCP-Friendly Rate Control (TFRC) [RFC3448bis] is a congestion
control mechanism designed for unicast flows operating in a best-
effort Internet environment. It is reasonably fair when competing
for bandwidth with TCP flows, but has a much lower variation of
throughput over time compared with TCP, making it more suitable for
applications such as streaming media where a relatively smooth
sending rate is of importance. CCID 3 is appropriate for flows that
would prefer to minimize abrupt changes in the sending rate,
including streaming media applications with small or moderate
receiver buffering before playback.
- DCCP Congestion Control ID 4 [draft-ietf-ccid4-02.txt]:
TFRC Small Packets (TFRC-SP) [RFC4828], a variant of TFRC
mechanism has been designed for applications that exchange small
packets. The objective of TFRC-SP is to achieve the same
bandwidth in bps (bits per second) as a TCP flow using packets of
up to 1500 bytes. TFRC-SP enforces a minimum interval of 10 ms
between data packets to prevent a single flow from sending small
packets arbitrarily frequently. TFRC is a congestion control
mechanism for unicast flows operating in a best-effort Internet
environment, and is designed for DCCP that controls the sending
rate based on a stochastic Markov model for TCP Reno. CCID 4 has
been designed to be used either by applications that use a small
fixed segment size, or by applications that change their sending
rate by varying the segment size. Because CCID 4 is intended for
applications that use a fixed small segment size, or that vary
their segment size in response to congestion, the transmit rate
derived from the TCP throughput equation is reduced by a factor
that accounts for packet header size, as specified in [RFC4828].
The resulting open questions are:
- Assess and experiment TFRC-SP variant: 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.
- How to design congestion control so as to scale with packet
size (dependency of congestion algorithm on packet size)? Early
assessment shows that packet size dependency should remain at
the transport layer.
Today, many network resources are designed so that packet processing
cannot be overloaded even for incoming loads at the maximum bit-rate
of the line. If packet processing can handle sustained load r [packet
per second] and the minimum packet size is h [bit] (i.e. packet
headers with no payload), then a line rate of x [bit per second] will
never be able to overload packet processing as long as x =< r.h.
However, realistic equipment is often designed to only cope with a
near-worst-case workload with a few larger packets in the mix, rather
than the worst-cast of all minimum size packets. In this case, x =
r.(h + e) for some small value of e.
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Therefore, it is likely that most congestion seen on today's Internet
is due to an excess of bits rather than packets, although packet-
congestion is not impossible for runs of small packets (e.g. TCP ACKs
or DoS attacks with small UDP datagrams).
This observation raises additional open issues:
o) Will bit congestion remain prevalent?
Being able to assume that congestion is generally due to excess bits
not excess packets is a useful simplifying assumption in the design
of congestion control protocols. Can we rely on this assumption into
the future?
Over the last three decades, performance gains have mainly been
through increased packet rates, not bigger packets. But if bigger
maximum segment sizes become more prevalent, tiny segments (e.g.
ACKs) will still continue to be widely used---a widening /range/ of
packet sizes.
The open question is thus whether 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.
o) Confusable Causes of Drop
There is a considerable body of research on how to distinguish
whether packet drops are due to transmission corruption or to
congestion. But the full list of confusable causes of drop is longer
and includes transmission loss, congestion loss (bit congestion and
packet congestion), and policing loss
If congestion is due to excess bits, the bit rate should be reduced.
If congestion is due to excess packets, the packet rate can be
reduced without reducing the bit rate---by using larger packets.
However, if the transport cannot tell which of these causes led to a
specific drop, its only safe response is to reduce bit rate. This is
why the Internet would be more complicated if packet-congestion were
prevalent, as reducing the bit rate also reduces the packet rate
(except in perverse cases), while reducing the packet rate doesn't
necessarily reduce the bit rate.
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Given distinguishing between transmission loss and congestion is
already an open issue (Section 3.2), if that problem is ever solved,
a further open issue would be whether to standardize a solution that
distinguishes all the above causes of drop, not just two of them.
Nonetheless, even if we find a way for network equipment to
explicitly distinguish which sort of drop has occurred, we will never
be able to assume that such a smart AQM solution is deployed at every
congestible resource throughout the Internet---at every higher layer
device like firewalls, proxies, servers and at every lower layer
device like low-end home hubs, DSLAMs, WLAN cards, cellular base-
stations and so on. Thus, transport protocols will always have to
cope with drops due to unguessable causes, so we should always treat
AQM smarts as an optimization, not a given.
o) 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 [Bri08], along with other aspects of
packet size and congestion control.
[Bri08] makes the strong recommendation that network equipment should
drop or mark packets with a probability independent of each specific
packet's size, while congestion controls should respond to dropped or
marked packets in proportion to the packet's size. This issue is
deferred to the Transport Area Working Group.
o) Packet Size and Congestion Control Protocol Design
If the above recommendation is correct---that the packet size of a
congestion notification should be taken into account when the
transport reads, not when the network writes the notification---it
opens up a significant program of protocol engineering and re-
engineering. Indeed, TCP does not take packet size into account when
responding to losses or ECN. At present this is not a pressing
problem because use of 1500B data segments is very prevalent for TCP
and the range of alternative segment sizes is not large. However, we
should design the Internet's protocols so they will scale with packet
size, so an open issue is whether we should evolve TCP, or expect new
protocols to take over.
As we continue to standardize new congestion control protocols, we
must then face the issue of how they should take account of packet
size. If we determine that TCP was incorrect in not taking account of
packet size, even if we don't change TCP, we should not allow new
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protocols to follow TCP's example in this respect. For example, as
explained here above, the small-packet variant of TCP-friendly rate
control (TFRC-SP [RFC4828]) is an experimental protocol that aims to
take account of packet size. Whatever packet size it uses, it ensures
its rate approximately equals that of a TCP using 1500B segments.
This raises the further question of whether TCP with 1500B segments
will be a suitable long-term gold standard, or whether we need a more
thoroughgoing review of what it means for a congestion control to
scale with packet size.
3.4 Challenge 4: Pseudo-Wires
Pseudowires (PW) may carry non-TCP data flows (e.g. TDM traffic).
Structure Agnostic TDM over Packet (SATOP) [RFC4553], Circuit
Emulation over Packet Switched Networks (CESoPSN), TDM over IP, are
not responsive to congestion control in a TCP-friendly manner as
prescribed by [RFC2914]. Moreover, it is not possible to simply
reduce the flow rate of a TDM PW when facing packet loss.
Carrying TDM PW over an IP network poses a real problem. Indeed,
providers can rate control corresponding incoming traffic but it may
not be able to detect that a PW carries TDM traffic. This can be
illustrated with the following example.
........... ............
. . .
S1 --- E1 --- . .
. | . .
. === E5 === E7 ---
. | . . |
S2 --- E2 --- . . |
. . . | |
........... . | v
. ----- R --->
........... . | ^
. . . | |
S3 --- E3 --- . . |
. | . . |
. === E6 === E8 ---
. | . .
S4 --- E4 --- . .
. . .
........... ............
\---- P1 ---/ \---------- P2 -----
Sources S1, S2, S3 and S4 are originating TDM over IP traffic. P1
provider edges E1, E2, E3, and E4 are rate limiting such traffic. The
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SLA of provider P1 with transit provider P2 is such that the latter
assumes a BE traffic pattern and that the distribution shows the
typical properties of common BE traffic (elastic, non-real time, non-
interactive).
The problem arises for transit provider P2 that is not able to detect
that IP packets are carrying constant-bit rate service traffic that
is by definition unresponsive to any congestion control mechanisms.
Assuming P1 providers are rate limiting BE traffic, a transit P2
provider router R may be subject to serious congestion as all TDM PWs
cross the same router. TCP-friendly traffic would follow TCP's AIMD
algorithm of reducing the sending rate in half in response to each
packet drop. Nevertheless, the TDM PWs will take all the available
capacity, leaving no room for any other type of traffic. Note that
the situation may simply occur because S4 suddenly turns up a TDM PW.
As it is not possible to assume that edge routers will soon have the
ability to detect the type of the carried traffic, it is important
for transit routers (P2 provider) to be able to apply a fair, robust,
responsive and efficient congestion control technique in order to
prevent impacting normally behaving Internet traffic. However, it is
still an open question how the corresponding mechanisms in the data
and control planes have to be designed.
3.5 Challenge 5: Multi-domain Congestion Control
Transport protocols such as TCP operate over the Internet that is
divided into autonomous systems. These systems are characterized by
their heterogeneity as IP networks are realized by a multitude of
technologies. Variety of conditions and their variations leads to
correlation effects between policers that regulate traffic against
certain conformance criteria.
With the advent of techniques allowing for early detection of
congestion, packet loss is no longer the sole metric of congestion.
ECN (Explicit Congestion Notification) marks packets - set by active
queue management techniques - to convey congestion information trying
to prevent packet losses (packet loss and the number of packets
marked gives an indication of the level of congestion). Using TCP
ACKs to feed back that information allows the hosts to realign their
transmission rate and thus encourage them to efficiently use the
network. In IP, ECN uses the two unused bits of the TOS field
[RFC2474]. Further, ECN in TCP uses two bits in the TCP header that
were previously defined as reserved [RFC793].
ECN [RFC3168] is an example of a congestion feedback mechanism from
the network toward hosts, while the policer must sit at every
potential point of congestion. The congestion-based feedback scheme
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however has limitations when applied on an inter-domain basis.
Indeed, the same congestion feedback mechanism is required along the
entire path for optimal control at end-systems.
Another solution in a multi-domain environment may be the TCP rate
controller (TRC), a traffic conditioner which regulates the TCP flow
at the ingress node in each domain by controlling packet drops and
RTT of the packets in a flow. The outgoing traffic from a TRC
controlled domain is shaped in such a way that no packets are dropped
at the policer. However, the TRC depends on the end-to-end TCP model,
and thus the diversity of TCP implementations is a general problem.
Security is another challenge for multi-domain operation. At some
domain boundaries, an increasing number of application layer gateways
(e. g., proxies) are deployed, which split up end-to-end connections
and prevent end-to-end congestion control.
Furthermore, authentication and authorization issues can arise at
domain boundaries whenever information is exchanged, and so far the
Internet does not have a single general security architecture that
could be used in all cases. Many autonomous systems also only
exchange some limited amount of information about their internal
state (topology hiding principle), even though having more precise
information could be highly beneficial for congestion control. The
future evolution of the Internet inter-domain operation has to show
whether more multi-domain information exchange can be realized.
3.6 Challenge 6: Precedence for Elastic Traffic
Traffic initiated by so-called elastic applications adapt to the
available bandwidth using feedback about the state of the network.
There are two types of flows: 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 email. 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
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here: what is the meaning of "relative"? What is the role of the
Transport Layer?
The preferential treatment of higher precedence traffic with
appropriate congestion control mechanisms is still an open issue that
may, depending on the proposed solution, impact both the host and the
network precedence awareness, and thereby congestion control.
TODO:
- Discuss existing work on low-priority flows - why isn't this stuff
used? That's an open issue, interesting things could be done with it!
- Discuss DiffServ [RFC2474] [RFC2475] related aspects with
congestion control.
3.7 Challenge 7: Misbehaving Senders and Receivers
In the current Internet architecture, congestion control depends on
parties acting against their own interests. It is not in a receiver's
interest to honestly return feedback about congestion on the path,
effectively requesting a slower transfer. It is not in the sender's
interest to reduce its rate in response to congestion if it can rely
on others to do so. Additionally, networks may have strategic reasons
to make other networks appear congested.
Numerous strategies to divert congestion control have already been
identified. The IETF has particularly focused on misbehaving TCP
receivers that could confuse a compliant sender into assigning
excessive network and/or server resources to that receiver (e.g.
[Sav99], [RFC3540]). But, although such strategies are worryingly
powerful, they do not yet seem common.
A growing proportion of Internet traffic comes from applications
designed not to use congestion control at all, or worse, applications
that add more forward error correction the more losses they
experience. Some believe the Internet was designed to allow such
freedom so it can hardly be called misbehavior. But others consider
that it is misbehavior to abuse this freedom [RFC3714], given one
person's freedom can constrain the freedom of others (congestion
represents this conflict of interests). Indeed, leaving freedom
unchecked might result in congestion collapse in parts of the
Internet. Proportionately, large volumes of unresponsive voice
traffic could represent such a threat, particularly for countries
with less generous provisioning [RFC3714]. More recently, Internet
video on demand services are becoming popular that transfer much
greater data rates without congestion control (e.g. the peer-to-peer
Joost service currently streams media over UDP at about 700kbps
downstream and 220kbps upstream).
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Note that the problem is not just misbehavior driven by a selfish
desire for more bandwidth (see Section 4).
Open research questions resulting from these considerations are:
- By design, new congestion control protocols need to enable one end
to check the other for protocol compliance.
- Provide congestion control primitives that satisfy more demanding
applications (smoother than TFRC, faster than high speed TCPs), so
that application developers and users do not turn off congestion
control to get the rate they expect and need.
Note also that self-restraint is disappearing from the Internet. So,
it may no longer be sufficient to rely on developers/users
voluntarily submitting themselves to congestion control. As main
consequence, mechanisms to enforce fairness (see Section 2.3) need to
have more emphasis within the research agenda.
3.8 Other challenges
This section provides additional challenges and open research issues
that are not (at this point in time) deemed sufficiently large or of
different nature compared to the main challenges depicted since so
far.
Note that this section may be complemented in future release of this
document by topics discussed during the last ICCRG meeting co-located
with PFLDNet 2008 International Workshop. Topics of interest include
but not limited to multipath congestion control and congestion
control for multimedia codecs that only support certain set of data
rates.
3.8.1 RTT estimation
Several congestion control schemes have to precisely know the round-
trip time (RTT) of a path. The RTT is a measure of the current delay
on a network. It is defined as the delay between the sending of a
packet and the reception of a corresponding response, which is echoed
back immediately by receiver upon receipt of the packet. This
corresponds to the sum of the one-way delay of the packet and the
(potentially different) one-way delay of the response. Furthermore,
any RTT measurement also includes some additional delay due to the
packet processing in both end-systems.
There are various techniques to measure the RTT: Active measurements
inject special probe packets to the network and then measure the
response time, using e.g. ICMP. In contrast, passive measurements
determine the RTT from ongoing communication processes, without
sending additional packets.
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The connection endpoints of reliable transport protocols such as TCP,
SCTP, and DCCP, as well as several application protocols, keep track
of the RTT in order to dynamically adjust protocol parameters such as
the retransmission timeout (RTO). They can implicitly measure the RTT
on the sender side by observing the time difference between the
sending of data and the arrival of the corresponding
acknowledgements. For TCP, this is the default RTT measurement
procedure, in combination with Karn's algorithm that prohibits RTT
measurements from retransmitted segments [RFC2988]. Traditionally,
TCP implementations take one RTT measurement at a time (i. e., about
once per RTT). As alternative, the TCP timestamp option [RFC1323]
allows more frequent explicit measurements, since a sender can safely
obtain an RTT sample from every received acknowledgment. In
principle, similar measurement mechanisms are used by protocols other
than TCP.
Sometimes it would be beneficial to know the RTT not only at the
sender, but also at the receiver. A passive receiver can deduce some
information about the RTT by analyzing the sequence numbers of
received segments. But this method is error-prone and only works if
the sender permanently sends data. Other network entities on the path
can apply similar heuristics in order to approximate the RTT of a
connection, but this mechanism is protocol-specific and requires per-
connection state. In the current Internet, there is no simple and
safe solution to determine the RTT of a connection in network
entities other than the sender.
As outlined earlier in this document, the round-trip time is
typically not a constant value. For a given path, there is
theoretical minimum value, which is given by the minimum
transmission, processing and propagation delay on that path. However,
additional variable delays might be caused by congestion, cross-
traffic, shared mediums access control schemes, recovery procedures,
or other sub-IP layer mechanisms. Furthermore, a change of the path
(e. g., route flipping, handover in mobile networks) can result in
completely different delay characteristics.
Due to this variability, one single measured RTT value is hardly
sufficient to characterize a path. This is why many protocols use RTT
estimators that derive an averaged value and keep track of a certain
history of previous samples. For instance, TCP endpoints derive a
smoothed round-trip time (SRTT) from an exponential weighted moving
average [RFC2988]. Such a low-pass filter ensures that measurement
noise and single outliers do not significantly affect the estimated
RTT. Still, a fundamental drawback of low-pass filters is that the
averaged value reacts slower to sudden changes of the measured RTT.
There are various solutions to overcome this effect: For instance,
the standard TCP retransmission timeout calculation considers not
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only the SRTT, but also a measure for the variability of the RTT
measurements [RFC2988]. Since this algorithm is not well-suited for
frequent RTT measurements with timestamps, certain implementations
modify the weight factors (e.g., [SK02]). There are also proposals
for more sophisticated estimators, such as Kalman filters or
estimators that utilize mainly peak values.
However, open questions concerning RTT estimation in the Internet
remain:
- Optimal measurement frequency: Currently, there is no common
understanding of the right time scale of RTT measurement. In
particular, the implications of rather frequent measurements (e. g.,
per packet) are not well understood. There is some empirical evidence
that 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
towards higher precision timers, the limited granularity may still
prevent highly accurate RTT estimations.
3.8.2 Malfunctioning devices
There is a long history of malfunctioning devices harming the
deployment of new and potentially beneficial functionality in the
Internet. Sometimes, such devices drop packets when a certain
mechanism is used, causing users to opt for reliability instead of
performance and disable the mechanism, or operating system vendors to
disable it by default. One well-known example is ECN, whose
deployment was long hindered by malfunctioning firewalls, but there
are many other examples (e.g. the Window Scaling option of TCP).
As new congestion control mechanisms are developed with the intention
of eventually seeing them deployed in the Internet, it would be
useful to collect information about failures caused by devices of
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this sort, analyze the reasons for these failures, and determine
whether there are ways for such devices to do what they intend to do
without causing unintended failures. Recommendation for vendors of
these devices could be derived from such an analysis. It would also
be useful to see whether there are ways for failures caused by such
devices to become more visible to endpoints, or for those failures to
become more visible to the maintainers of such devices.
4. Security Considerations
Misbehavior may be driven by pure malice, or malice may in turn be
driven by wider selfish interests, e.g. using distributed denial of
service (DDoS) attacks to gain rewards by extortion [RFC4948]. DDoS
attacks are possible both because of vulnerabilities in operating
systems and because the Internet delivers packets without requiring
congestion control.
Currently the focus of the research agenda against denial of service
is about identifying attack packets, attacking machines and networks
hosting them, with a particular focus on mitigating source address
spoofing. But if mechanisms to enforce congestion control fairness
were robust to both selfishness and malice [Bri06] they would also
naturally mitigate denial of service, which can be considered (from
the perspective of well-behaving Internet user) as a congestion
control enforcement problem.
5. Contributors
This document is the result of a collective effort to which the
following people have contributed:
Dimitri Papadimitriou <Dimitri.Papadimitriou@alcatel-lucent.be>
Michael Welzl <michael.welzl@uibk.ac.at>
Wesley Eddy <weddy@grc.nasa.gov>
Bela Berde <bela.berde@gmx.de>
Paulo Loureiro <loureiro.pjg@gmail.com>
Chris Christou <christou_chris@bah.com>
Michael Scharf <michael.scharf@ikr.uni-stuttgart.de>
6. References
6.1 Normative References
[RFC791] Postel, J., "Internet Protocol", STD 5, RFC 791,
September 1981.
[RFC793] Postel, J., "Transmission Control Protocol", STD 7,
RFC793, September 1981.
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[RFC896] Nagle, J., "Congestion Control in IP/TCP", RFC 896,
January 1984.
[RFC1323] Jacobson, V., Braden, R., Borman, D., "TCP Extensions for
High Performance", RFC 1323, May 1992.
[RFC2309] Braden, B., et al., "Recommendations on queue management
and congestion avoidance in the Internet", RFC 2309,
April 1998.
[RFC2003] Perkins, C., "IP Encapsulation within IP", RFC 1633,
October 1996.
[RFC2474] Nichols, K., Blake, S. Baker, F. and D. Black,
"Definition of the Differentiated Services Field (DS
Field) in the IPv4 and IPv6 Headers", RFC 2474, December
1998.
[RFC2475] Blake, S., Black, D., Carlson, M., Davies, E., Wang, Z.
and W. Weiss, "An Architecture for Differentiated
Services", RFC 2475, December 1998.
[RFC2581] Allman, M., Paxson, V., and W. Stevens, "TCP Congestion
Control", RFC 2581, April 1999.
[RFC2914] Floyd, S., "Congestion Control Principles", BCP 41,
RFC 2914, September 2000.
[RFC2988] Paxson, V. and Allman, M., "Computing TCP's
Retransmission Timer", RFC 2988, Nov. 2000
[RFC3168] Ramakrishnan, K., Floyd, S., and D. Black, "The Addition
of Explicit Congestion Notification (ECN) to IP",
RFC 3168, September 2001.
[RFC3448] Handley, M., Floyd, S., Padhye, J., and J. Widmer, "TCP
Friendly Rate Control (TFRC): Protocol Specification",
RFC 3448, January 2003.
[RFC3540] N. Spring, D. Wetherall, "Robust Explicit Congestion
Notification (ECN) Signaling with Nonces", RFC 3540, June
2003.
[RFC3714] S. Floyd, Ed., J. Kempf, Ed. "IAB Concerns Regarding
Congestion Control for Voice Traffic in the Internet",
RFC 3714, March 2004.
[RFC3985] Bryant, S. and P. Pate, "Pseudo Wire Emulation Edge-to-
Edge (PWE3) Architecture", RFC 3985, March 2005.
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[RFC4340] Kohler, E., Handley, M., and S. Floyd, "Datagram
Congestion Control Protocol (DCCP)", RFC 4340, March
2006.
[RFC4341] Floyd, S. and E. Kohler, "Profile for Datagram Congestion
Control Protocol (DCCP) Congestion Control ID 2: TCP-like
Congestion Control", RFC 4341, March 2006.
[RFC4342] Floyd, S., Kohler, E., and J. Padhye, "Profile for
Datagram Congestion Control Protocol (DCCP) Congestion
Control ID 3: TCP-Friendly Rate Control (TFRC)", RFC
4342, March 2006.
[RFC4553] Vainshtein, A. and Y. Stein, "Structure-Agnostic Time
Division Multiplexing (TDM) over Packet (SAToP)",
RFC 4553, June 2006.
[RFC4614] Duke, M., R. Braden, R., Eddy, W., and Blanton, E., "A
Roadmap for Transmission Control Protocol (TCP)
Specification Documents", RFC 4614, September 2006.
[RFC4782] Floyd, S., Allman, M., Jain, A., and P. Sarolahti,
"Quick-Start for TCP and IP", RFC 4782, Jan. 2007.
[RFC4948] L. Andersson, E. Davies, L. Zhang, "Report from the IAB
workshop on Unwanted Traffic March 9-10, 2006", RFC 4948,
August 2007.
6.2 Informative References
[Allman99] Allman, M. and V. Paxson, "On Estimating End-to-End
Network Path Properties", Proc. SIGCOMM, Sept. 99.
[Andrew00] L. Andrew, B. Wydrowski and S. Low, "An Example of
Instability in XCP", Manuscript available at
<http://netlab.caltech.edu/maxnet/XCP_instability.pdf>
[Ath01] S. Athuraliya, S. Low, V. Li, and Q. Yin, "REM: Active
queue management", IEEE Network Magazine, vol.15, no.3,
pp. 48-53, May 2001.
[BALAN01] Balan, R. K., Lee, B.P., Kumar, K.R.R., Jacob, L., Seah,
W.K.G., Ananda, A.L., "TCP HACK: TCP Header Checksum
Option to Improve Performance over Lossy Links",
Proceedings of IEEE Infocom, Anchorage, Alaska, April
2001.
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[Bonald00] T. Bonald, M. May, and J.-C. Bolot, "Analytic Evaluation
of RED Performance," In Proceedings of IEEE INFOCOM, Tel
Aviv, Israel, March 2000.
[Bri07] Bob Briscoe, "Flow Rate Fairness: Dismantling a Religion"
ACM SIGCOMM Computer Communication Review 37(2) 63--74
(April 2007).
[Bri06] Bob Briscoe, "Using Self-interest to Prevent Malice;
Fixing the Denial of Service Flaw of the Internet,"
Workshop on the Economics of Securing the Information
Infrastructure (Oct 2006)
<http://wesii.econinfosec.org/draft.php?paper_id=19>
[Chester04] Chesterfield, J., Chakravorty, R., Banerjee, S.,
Rodriguez, P., Pratt, I. and Crowcroft, J., "Transport
level optimisations for streaming media over wide-area
wireless networks", WIOPT'04, March 2004.
[Chiu89] D. M. Chiu and R. Jain, "Analysis of the increase and
decrease algorithms for congestion avoidance in computer
networks", Computer Networks and ISDN Systems, vol. 17,
pp. 1-14, 1989.
[Clark98] D. Clark and W. Fang, "Explicit Allocation of Best-Effort
Packet Delivery Service," IEEE/ACM Transactions on
Networking, vol.6, no.4, pp.362-373, August 1998
[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
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Acknowledgments
The authors would like to thank the following people whose feedback
and comments contributed to this document: Keith Moore, Jan
Vandenabeele.
Larry Dunn (his comments at the Manchester ICCRG and discussions with
him helped with the section on packet-congestibility). Bob Briscoe's
contribution was partly funded by [TRILOGY], a research project
supported by the European Commission.
Author's Addresses
Michael Welzl
University of Innsbruck
Technikerstr 21a
A-6020 Innsbruck, Austria
Phone: +43 (512) 507-6110
Email: michael.welzl@uibk.ac.at
Dimitri Papadimitriou
Alcatel-Lucent
Copernicuslaan, 50
B-2018 Antwerpen, Belgium
Phone : +32 3 240 8491
Email: dimitri.papadimitriou@alcatel-lucent.be
Michael Scharf
University of Stuttgart
Pfaffenwaldring 47
D-70569 Stuttgart
Germany
Phone: +49 711 685 69006
Email: michael.scharf@ikr.uni-stuttgart.de
Bob Briscoe
BT & UCL
B54/77, Adastral Park
Martlesham Heath
Ipswich IP5 3RE
UK
Email: bob.briscoe@bt.com
Welzl & Papadimitriou Expires - October 2008 [Page 30]
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