Network Working Group Dimitri Papadimitriou, Editor
Internet Draft Alcatel-Lucent
Expires: November 30, 2010 Michael Welzl
University of Oslo
Michael Scharf
University of Stuttgart
Bob Briscoe
BT & UCL
May 31, 2010
Open Research Issues in Internet Congestion Control
draft-irtf-iccrg-welzl-congestion-control-open-research-06.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
challenges that are becoming important as the network grows, as well
as some issues that have been known for many years. These challenges
are generally considered to be open research topics that may require
more study or application of innovative techniques before Internet-
scale solutions can be confidently engineered and deployed. This
document is the result of the ICCRG Research Group work.
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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..............................................4
2.1 Heterogeneity..............................................4
2.2 Stability..................................................6
2.3 Fairness...................................................7
3. Detailed Challenges............................................9
3.1 Challenge 1: Network Support...............................9
3.1.1 Performance and Robustness.........................12
3.1.2 Granularity of network component functions.........13
3.1.3 Information Acquisition............................14
3.1.4 Feedback signaling.................................15
3.2 Challenge 2: Corruption Loss..............................15
3.3 Challenge 3: Packet Size..................................17
3.4 Challenge 4: Flow Startup.................................21
3.5 Challenge 5: Multi-domain Congestion Control..............23
3.5.1 Multi-domain Transport of Explicit Congestion
Notification.............................................23
3.5.2 Multi-domain Exchange of Topology or Explicit Rate
Information..............................................24
3.5.3 Multi-domain Pseudowires...........................25
3.6 Challenge 6: Precedence for Elastic Traffic...............26
3.7 Challenge 7: Misbehaving Senders and Receivers............28
3.8 Other Challenges..........................................29
3.8.1 RTT Estimation.....................................29
3.8.2 Malfunctioning Devices.............................31
3.8.3 Dependence on RTT..................................32
3.8.4 Congestion Control in Multi-layered Networks.......32
3.8.5 Multipath End-to-end Congestion Control and Traffic
Engineering..............................................33
3.8.6 ALGs and Middleboxes...............................33
4. Security Considerations.......................................34
5. References....................................................35
5.1 RFC References............................................35
5.2 Other References..........................................37
6. Acknowledgments...............................................44
7. Author's Addresses............................................44
8. Contributors..................................................44
Copyright Statement..............................................46
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1. Introduction
This document, result of the ICCRG Research Group, describes some
of the open research topics in the domain of Internet congestion
control that are known today. We begin by reviewing some proposed
definitions of congestion and congestion control based on current
understandings.
Congestion can be defined as a state or condition that occurs when
network resources are overloaded resulting in impairments for network
users as objectively measured by the probability of loss and/or of
delay. The overload results in the reduction of utility in networks
that support both spatial and temporal multiplexing, but no
reservation [Keshav07]. Congestion control is a (typically
distributed) algorithm to share network resources among competing
traffic sources.
Two components of distributed congestion control have been defined in
the context of primal-dual modeling [Kelly98]. Primal congestion
control refers to the algorithm executed by the traffic sources for
controlling their sending rates or window sizes. This is normally a
closed-loop control, where this operation depends on feedback. TCP
algorithms fall in this category. Dual congestion control is
implemented by the routers through gathering information about the
traffic traversing them. A dual congestion control algorithm updates,
implicitly or explicitly, a congestion measure or congestion rate and
sends it back, implicitly or explicitly, to the traffic sources that
use that link. Queue management algorithms such as Random Early
Detection (RED) [Floyd93] or Random Exponential Marking (REM) [Ath01]
fall into the "dual" category.
Congestion control provides for a fundamental set of mechanisms for
maintaining the stability and efficiency of the Internet. Congestion
control has been associated with TCP since Van Jacobson's work in
1988, but there is also congestion control outside of TCP (e.g. for
real-time multimedia applications, multicast, and router-based
mechanisms) [ICCRG-RFCs]. The Van Jacobson end-to-end congestion
control algorithms [Jacobson88] [RFC2581] [RFC5681] are used by the
Internet transport protocol TCP [RFC4614]. They have been proven to
be highly successful over many years but have begun to reach their
limits, as the heterogeneity of both the data link and physical
layer and applications are pulling TCP congestion control beyond its
natural operating regime, because it performs poorly as the bandwidth
or delay increases. A side effect of these deficiencies is that an
increasing share of hosts use non-standardized congestion control
enhancements (for instance, many Linux distributions have been
shipped with "CUBIC" [Ha08] as the default TCP congestion control
mechanism).
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While the original Van Jacobson algorithm requires no congestion-
related state in routers, more recent modifications have departed
from the strict application of the end-to-end principle [Saltzer84]
in order to avoid congestion collapse. Active Queue Management (AQM)
in routers, e.g., RED and some of its variants such as Adaptive RED
(ARED), improves performance by keeping queues small (implicit
feedback via dropped packets), while Explicit Congestion Notification
(ECN) [Floyd94] [RFC3168] passes one bit of congestion information
back to senders when an AQM would normally drop a packet. It is to be
noted that other variants of RED built on AQM such as Weighted RED
(WRED), and RED with In/Out (RIO) [Clark98] for quality enforcement
whereas Stabilized RED (SRED), and XCHOKe [Pan00] are flow policers.
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], [Kelly98],
[Kelly05]).
Congestion control includes many new challenges that are becoming
important as the network grows in addition to the issues that have
been known for many years. These are generally considered to be open
research topics that may require more study or application of
innovative techniques before Internet-scale solutions can be
confidently engineered and deployed. In what follows, an overview of
some of these challenges is given.
2. Global Challenges
This section describes the global challenges to be addressed in the
domain of Internet congestion control.
2.1 Heterogeneity
The Internet encompasses a large variety of heterogeneous IP networks
that are realized by a multitude of technologies, which result in a
tremendous variety of link and path characteristics: capacity can be
either scarce in very slow speed radio links (several kbps), or there
may be an abundant supply in high-speed optical links (several
gigabit per second). Concerning latency, scenarios range from local
interconnects (much less than a millisecond) to certain wireless and
satellite links with very large latencies up to or over a second).
Even higher latencies can occur in space communication. As a
consequence, both the available bandwidth and the end-to-end delay in
the Internet may vary over many orders of magnitude, and it is likely
that the range of parameters will further increase in future.
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Additionally, neither the available bandwidth nor the end-to-end
delay is constant. At the IP layer, competing cross-traffic, traffic
management in routers, and dynamic routing can result in sudden
changes of the characteristics of an end-to-end path. Additional
dynamics can be caused by link layer mechanisms, such as shared media
access (e.g., in wireless networks), changes of links due to mobility
(horizontal/vertical handovers), topology modifications (e. g., in
ad-hoc or meshed networks), link layer error correction and dynamic
bandwidth provisioning schemes. From this, it follows that path
characteristics can be subject to substantial changes within short
time frames.
Congestion control algorithms have to deal with this variety in an
efficient and stable way. The congestion control principles
introduced by Van Jacobson assume a rather static scenario and
implicitly target configurations where the bandwidth-delay product is
of the order of some dozens of packets at most. While these
principles have proved to work in the Internet for almost two
decades, much larger bandwidth-delay products and increased dynamics
challenge them more and more. There are many situations where today's
congestion control algorithms react in a suboptimal way, resulting
among other things in low resource utilization, and non-optimal
congestion avoidance.
This has resulted in a multitude of new proposals for congestion
control algorithms. For instance, since the Additive Increase
Multiplicative Decrease (AIMD) behavior of TCP is too conservative in
practical environments when the congestion window is large, several
high-speed congestion control extensions have been developed.
However, these new algorithms may be less robust or starve legacy
flows in certain situations for which they have not been designed. Up
to now, there is still no common agreement in the IETF on which
algorithm(s) and protocol(s) to choose.
It is always possible to tune congestion control parameters based on
some knowledge of the environment and the application scenario.
However, the interaction between multiple congestion control
techniques interacting with each other is not yet well understood.
The fundamental challenge is whether it is possible to define one
congestion control mechanism that operates reasonably well in a
whole range of scenarios that exist in the Internet. Hence, it is an
important research question how new Internet congestion control
mechanisms would have to be designed, which maximum degree of
dynamics they can efficiently handle, and whether they can keep the
generality of the existing end-to-end solutions.
Some improvements to congestion control could be realized by simple
changes of single functions in end-system or optimizations of network
components. However, new mechanism(s) might also require a
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fundamental redesign of the overall network architecture, and they
may even affect the design of Internet applications. This can imply
significant interoperability and backward compatibility challenges
and/or create network accessibility obstacles. In particular,
networks and/or applications that do not use or support a new
congestion control mechanism could be penalized by a significantly
worse performance compared to what they would get if everybody used
the existing mechanisms (cf. the discussion on fairness in section
2.3). [RFC5033] defines several criteria to evaluate the
appropriateness of a new congestion control mechanism. However, a key
issue is how much performance deterioration is acceptable for
"legacy" applications. This tradeoff between performance and cost has
to be very carefully examined for all new congestion control schemes.
2.2 Stability
Control theory is a mathematical tool for describing dynamic systems.
It lends itself to modeling congestion control - TCP is a perfect
example of a typical "closed loop" system that can be described in
control theoretic terms. However, control theory has had to be
extended to model the interactions between multiple control loops in
a network. In control theory, there is a mathematically defined
notion of system stability. In a stable system, for any bounded input
over any amount of time, the output will also be bounded. For
congestion control, what is actually meant by global stability is
typically asymptotic stability: a mechanism should converge to a
certain state irrespective of the initial state of the network. Local
stability means that if the system is perturbed from its stable state
it will quickly return towards the locally stable state.
Some fundamental facts known from control theory are useful as
guidelines when designing a congestion control mechanism. For
instance, a controller should only be fed a system state that
reflects its output. A (low-pass) filter function should be used in
order to pass only states to the controller that are expected to last
long enough for its action to be meaningful [Jain88]. Action should
be carried out whenever such feedback arrives, as it is a fundamental
principle of control that the control frequency should ideally be
equal to the feedback frequency. Reacting faster leads to
oscillations and instability while reacting slower makes the system
tardy [Jain90].
Control theoretic modeling of a realistic network can be quite
difficult, especially when taking distinct packet sizes and
heterogeneous RTTs into account. It has therefore become common
practice to model simpler cases and to leave the more complicated
(realistic) situations for simulations. Clearly, if a mechanism is
not stable in a simple scenario, it is generally useless; this method
therefore helps to eliminate faulty congestion control candidates at
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an early stage. However, a mechanism that is found to be stable in
simulations can still note be safely deployed in real networks, since
simulation scenarios make simplifying assumptions.
TCP stability can be attributed to two key aspects which were
introduced in [Jacobson88]: the AIMD control law during congestion
avoidance, which is based on a simple, vector based analysis of two
controllers sharing one resource with synchronous RTTs [Chiu89], and
the "conservation of packets principle", which, once the control has
reached "steady state", tries to maintain an equal amount of packets
in flight at any time by only sending a packet into the network when
a packet has left the network (as indicated by an ACK arriving at the
sender). The latter aspect has guided many decisions regarding
changes that were made to TCP over the years.
The reasoning in [Jacobson88] assumes all senders to be acting at the
same time. The stability of TCP under more realistic network
conditions has been investigated in a large number of ensuing works,
leading to no clear conclusion that TCP would also be asymptotically
stable under arbitrary network conditions. On the other hand,
research has concluded that stability can be assured with constraints
on dynamics that are less stringent than the "conservation of packets
principle". From control theory, only rate increase (not the target
rate) needs to be inversely proportional to RTT (whereas window-based
control converges on a target rate inversely proportional to RTT).
A congestion control mechanism can therefore converge on a rate that
is independent of RTT as long as its dynamics depend on RTT (e.g.
FAST TCP [Jin04]).
In the stability analysis of TCP and of these more modern controls,
the impact of Slow Start on stability (which can be significant as
short-lived HTTP flows often never leave this phase) is not entirely
clear.
2.3 Fairness
Recently, the way the Internet community reasons about fairness has
been called into deep questioning [Bri07]. Much of the community has
taken fairness to mean approximate equality between the rates of
flows (flow rate fairness) that experience equivalent path congestion
as with TCP [RFC2581] [RFC5681] and TFRC [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
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fairness is about mere details. These three all share the assumption
that equal flow rates are desirable; they merely differ in the second
order issue of how to share out excess capacity in a network of many
bottlenecks. In contrast, cost fairness should lead to extremely
unequal flow rates by design. Equivalently, equal flow rates would
typically be considered extremely unfair.
The two traditional approaches are not protocol options that can each
be followed in different parts of an inter-network. They lead to
research agendas that are different in their respective objectives,
resulting in a different set of open issues.
If we assume TCP-friendliness as a goal with flow rate as the metric,
open issues would be:
- Should flow fairness depend on the packet rate or the bit rate?
- Should the target flow rate depend on RTT (as in TCP) or should
only flow dynamics depend on RTT (e.g. as in Fast TCP [Jin04])?
- How should we estimate whether a particular flow start strategy is
fair, or whether a particular fast recovery strategy after a
reduction in rate due to congestion is fair?
- Should we judge what is reasonably fair if an application needs,
for example, even smoother flows than TFRC, or it needs to
burst occasionally, or with any other application behavior?
- During brief congestion bursts (e.g. due to new flow arrivals) how
should we judge at what point it becomes unfair for some flows to
continue at a smooth rate while others reduce their rate?
- Which mechanism(s) could be used to enforce approximate flow rate
fairness?
- Should we introduce some degree of fairness that takes account of
different users' flow activity over time?
- How should we judge the fairness of applications using a large
number of flows over separate paths (e.g. via an overlay)?
If we assume cost fairness as a goal with congestion volume as the
metric, open issues would be:
- Can one application's sensitivity to instantaneous congestion
really be protected by longer-term accountability of competing
applications?
- Which protocol mechanism(s) are needed to give accountability for
causing congestion?
- How might we design one or two weighted transport protocols (such
as TCP, UDP, etc.) with the addition of application policy control
over the weight?
- Which policy enforcement might be used by networks and what are
the interactions between application policy and network policy
enforcement?
- How to design a new policy enforcement framework that will
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appropriately compete with existing flows aiming for rate equality
(e.g. TCP)?
The question of how to reason about fairness is a pre-requisite to
agreeing on the research agenda. If the relevant metric is flow-rate
it places constraints at protocol design-time, whereas if the metric
is congestion volume the constraints move to run-time, while design-
time constraints can be relaxed [Bri08]. However, that question does
not require more research in itself, it is merely a debate that needs
to be resolved by studying existing research and by assessing how bad
fairness problems could become if they are not addressed rigorously,
and whether we can rely on trust to maintain approximate fairness
without requiring policing complexity [RFC5290]. The latter points
may themselves lead to additional research. However, it is also
accepted that more research will not necessarily lead to convince
either side to change their opinions. More debate would be needed. It
seems also that if the architecture is built to support cost-fairness
then equal instantaneous cost rates for flows sharing a bottleneck
result in flow-rate fairness; that is, flow-rate fairness can be seen
as a special case of cost-fairness. One can be used to build the
other, but not vice-versa.
3. Detailed Challenges
3.1 Challenge 1: Network Support
This challenge is perhaps the most critical to get right. Changes to
the balance of functions between the endpoints and network equipment
could require a change to the per-datagram data plane interface
between the transport and network layers. Network equipment vendors
need to be assured that any new interface is stable enough (on decade
timescales) to build into firmware and hardware, and OS vendors will
not use a new interface unless it is likely to be widely deployed.
Network components can be involved in congestion control in two ways:
first, they can implicitly optimize their functions, such as queue
management and scheduling strategies, in order to support the
operation of an end-to-end congestion control. Second, network
components can participate in congestion control via explicit
signaling mechanisms. Explicit signaling mechanisms, whether in-
band or out-of-band, require a communication between network
components and end-systems. Signals realized within or over the IP
layer are only meaningful to network components that process IP
packets. This always includes routers and potentially also
middleboxes, but not pure link layer devices. The following section
distinguishes clearly between the term "network component" and the
term "router"; the term "router" is used whenever the processing of
IP packets is explicitly required. One fundamental challenge of
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network supported congestion control is that typically not all
network components along a path are routers (cf. Section 3.1.3).
The first (optimizing) category of implicit mechanisms can be
implemented in any network component that processes and stores
packets. Various approaches have been proposed and also deployed,
such as different AQM techniques. Even though these implicit
techniques are known to improve network performance during congestion
phases, they are still only partly deployed in the Internet. This may
be due to the fact that finding optimal and robust parameterizations
for these mechanisms is a non-trivial problem. Indeed, the problem
with various AQM schemes is the difficulty to identify correct values
of the parameter set that affects the performance of the queuing
scheme (due to variation in the number of sources, the capacity and
the feedback delay) [Firoiu00] [Hollot01] [Zhang03]. Many AQM schemes
(RED, REM, BLUE, PI-Controller but also Adaptive Virtual Queue (AVQ))
do not define a systematic rule for setting their parameters.
The second class of approaches uses explicit signalling. By using
explicit feedback from the network, connection endpoints can obtain
more accurate information about the current network characteristics
on the path. This allows endpoints to make more precise decisions
that can better control congestion.
Explicit feedback techniques fall into three broad categories:
- Explicit congestion feedback: one bit Explicit Congestion
Notification (ECN) [RFC3168] or proposals for more than one bit
[Xia05];
- Explicit per-datagram rate feedback: the eXplicit Control Protocol
(XCP) [Katabi02] [Falk07], the Rate Control Protocol (RCP)
[Dukki05];
- Explicit rate feedback: by means of in-band signaling, such as by
Quick-Start [RFC4782] or by means of out-of-band signaling, e.g.
CADPC/PTP [Welzl03].
Explicit router feedback can address some of the inherent
shortcomings of TCP. For instance, XCP was developed to overcome the
inefficiency, and instability that TCP suffers from when the per-flow
bandwidth-delay product increases. By decoupling resource
utilization/congestion control from fairness control, XCP achieves
equal bandwidth allocation, high utilization, a small standing queue
size, and near-zero packet drops, with both steady and highly varying
traffic. Importantly, XCP does not maintain any per-flow state in
routers and requires few CPU cycles per packet, hence making it
potentially applicable in high-speed routers. However, XCP is still
subject to research: as [Andrew05] has pointed out, XCP is locally
stable but globally unstable when the maximum RTT of a flow is much
larger than the mean RTT. This instability can be removed by changing
the update strategy for the estimation interval, but this makes the
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system vulnerable to erroneous RTT advertisements. The authors of
[PAP02] have shown that, when flows with different RTTs are applied,
XCP sometimes discriminates among heterogeneous traffic flows, even
if XCP generally equalizes rate among different flows. [Low05]
provides for a complete characterization of the XCP equilibrium
properties.
Several other explicit router feedback schemes have been developed
with different design objectives. For instance, RCP uses per-packet
feedback similar to XCP. But unlike XCP, RCP focuses on the reduction
of flow completion times [Dukki06], taking an optimistic approach to
flows likely to arrive in the next RTT and tolerating larger
instantaneous queue sizes [Dukki05]. XCP on the other hand gives very
poor flow completion times for short flows.
Both implicit and explicit router support should be considered in the
context of the end-to-end argument [Saltzer84], which is one of the
key design principles of the Internet. It suggests that functions
that can be realized both in the end-systems and in the network
should be implemented in the end-systems. This principle ensures that
the network provides a general service and that it remains as simple
as possible (any additional complexity is placed above the IP layer,
i.e., at the edges) so as to ensure evolvability, reliability and
robustness. Furthermore, the fate-sharing principle ([Clark88]
"Design Philosophy of the DARPA Internet Protocols") mandates that an
end-to-end Internet protocol design should not rely on the
maintenance of any per-flow state (i.e., information about the state
of the end-to-end communication) inside the network and that the
network state (e.g. routing state) maintained by the Internet shall
minimize its interaction with the states maintained at the end-
points/hosts [RFC1958].
However, as discussed for instance in [Moors02], congestion control
cannot be realized as a pure end-to-end function only. Congestion is
an inherent network phenomenon and can only be resolved efficiently
by some cooperation of end-systems and the network. Congestion
control in today's Internet protocols follows the end-to-end design
principle insofar as only minimal feedback from the network is used,
e.g., packet loss and delay. The end-systems only decide how to
react and how to avoid congestion. The crux is that, on the one hand,
there would be substantial benefit by further assistance from the
network, but, on the other hand, such network support could lead to
duplication of functions, which might even harmfully interact with
end-to-end protocol mechanisms. The different requirements of
applications (cf. the fairness discussion in Section 2.3) call for a
variety of different congestion control approaches, but putting such
per-flow behavior inside the network should be avoided, as such
design would clearly be at odds with the end-to-end and fate sharing
design principles.
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The end-to-end and fate sharing principles are generally regarded as
the key ingredients for ensuring a scalable and survivable network
design. In order to ensure that new congestion control mechanisms are
scalable, violating these principles must therefore be avoided.
For instance, protocols like XCP and RCP seem not to require flow
state in the network, but this is only the case if the network trusts
i) the receiver not to lie when feeding back the network's delta to
the requested rate; ii) the source not to lie when declaring its
rate; and iii) the source not to cheat when setting its rate in
response to the feedback [Katabi04].
Solving these problems for non-cooperative environments like the
public Internet requires flow state, at least on a sampled basis.
However, because flows can create new identifiers whenever they want,
sampling does not provide a deterrent---a flow can simply cheat until
it is discovered then switch to a whitewashed identifier [Feldmann04]
and continue cheating until it is discovered again [Bri09, S7.3].
However, holding flow state in the network only seems to solve these
policing problems in single autonomous system settings. A multi-
domain system would seem to require a completely different protocol
structure, as the information required for policing is only seen as
packets leave the internetwork, but the networks where packets enter
will also want to police compliance.
Even if a new protocol structure were found, it seems unlikely
network flow state could be avoided given the network's per-packet
flow rate instructions would need to be compared against variations
in the actual flow rate, which is inherently not a per-packet metric.
These issues have been outstanding ever since Intserv was identified
as unscalable in 1997 [RFC2208]. All subsequent attempts to involve
network elements in limiting flow-rates (XCP, RCP etc) will run up
against the same open issue if anyone attempts to standardise them
for use on the public Internet.
In general, network support of congestion control raises many issues
that have not been completely solved yet.
3.1.1 Performance and Robustness
Congestion control is subject to some tradeoffs: on the one hand, it
must allow high link utilizations and fair resource sharing but on
the other hand, the algorithms must also be robust in particular
during congestion phases.
Router support can help to improve performance but it can also result
in additional complexity and more control loops. This requires a
careful design of the algorithms in order to ensure stability and
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avoid e.g. oscillations. A further challenge is the fact that
information may be imprecise. For instance, severe congestion can
delay feedback signals. Also, in-network measurement of parameters
such as RTTs or data rates may contain estimation errors. Even though
there has been significant progress in providing fundamental
theoretical models for such effects, research has not completely
explored the whole problem space yet.
Open questions are:
- How much can network elements theoretically improve performance in
the complete range of communication scenarios that exists in the
Internet without damaging or impacting end-to-end mechanisms
already in place?
- Is it possible to design robust congestion control mechanisms that
offer significant benefits with minimum additional risks, even if
the Internet traffic patterns will change in future?
- What is the minimum support that is needed from the network in
order to achieve significantly better performance than with
end-to-end mechanisms and the current IP header limitations that
provide at most unary ECN signals?
3.1.2 Granularity of network component functions
There are several degrees of freedom concerning the involvement of
network entities, ranging from some few additional functions in
network management procedures on the one end to additional per
packet processing on the other end of the solution space.
Furthermore, different amounts of state can be kept in routers (no
per-flow state, partial per-flow state, soft state, hard state). The
additional router processing is a challenge for Internet scalability
and could also increase end-to-end latencies.
Although there are many research proposals that do not require per-
flow state and thus do not cause a large processing overhead, there
are no known full solutions (i.e. including anti-cheating) that do
not require per-flow processing. Also, scalability issues could also
be caused, for instance, by synchronization mechanisms for state
information among parallel processing entities, which are e.g. used
in high-speed router hardware designs.
Open questions are:
- What granularity of router processing can be realized without
affecting Internet scalability?
- How can additional processing efforts be kept at a minimum?
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3.1.3 Information Acquisition
In order to support congestion control, network components have to
obtain at least a subset of the following information. Obtaining that
information may result in complex tasks.
1. Capacity of (outgoing) links
Link characteristics depend on the realization of lower protocol
layers. Routers operating at IP layer do not necessarily know the
link layer network topology and link capacities, and these are not
always constant (e.g., on shared wireless links or bandwidth-on-
demand links). Depending on the network technology, there can be
queues or bottlenecks that are not directly visible at the IP
networking layer.
Difficulties also arise when using IP-in-IP tunnels [RFC 2003]
IPsec tunnels [RFC4301], IP encapsulated in L2TP [RFC2661], GRE
[RFC1701] [RFC2784], PPTP [RFC2637] or MPLS [RFC3031] [RFC3032]
[RFC5129]. In these cases, link information could be determined by
cross-layer information exchange, but this requires interfaces
capable of processing link layer technology specific information.
An alternative could be online measurements, but this can cause
significant additional network overhead. It is an open research
question as how much, if any, online traffic measurement would
be acceptable (at run-time). General guidelines for encapsulation
and decapsulation of explicit congestion information are currently
in preparation [ECN-tunnel].
2. Traffic carried over (outgoing) links
Accurate online measurement of data rates is challenging when
traffic is bursty. For instance, measuring a "current link load"
requires defining the right measurement interval / sampling
interval. This is a challenge for proposals that require knowledge
e.g. about the current link utilization.
3. Internal buffer statistics
Some proposals use buffer statistics such as a virtual queue
length to trigger feedback. However, network components can
include multiple distributed buffer stages that make it difficult
to obtain such metrics.
Open questions are:
- Can and should this information be made available, e.g., by
additional interfaces or protocols?
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- Which information is so important to higher layer controllers that
machine architecture research should focus on designing to provide
it?
3.1.4 Feedback signaling
Explicit notification mechanisms can be realized either by in-band
signaling (notifications piggybacked along with the data traffic) or
by out-of-band signaling [Sarola07]. The latter case requires
additional protocols and a secure binding between the signals and the
packets they refer to. Out-of-band signaling can be further
subdivided into path-coupled and path-decoupled approaches.
Open questions concerning feedback signaling include:
- At which protocol layer should the feedback signaling occur
(IP/network layer assisted, transport layer assisted, hybrid
solutions, shim layer, intermediate sub-layer, etc.)? Should the
feedback signaling be path-coupled or path-decoupled?
- What is the optimal frequency of feedback (only in case of
congestion events, per RTT, per packet, etc.)?
- What direction should feedback take (from network resource via
receiver to sender, or directly back to sender)?
3.2 Challenge 2: Corruption Loss
It is common for congestion control mechanisms to interpret packet
loss as a sign of congestion. This is appropriate when packets are
dropped in routers because of a queue that overflows, but there are
other possible reasons for packet drops. In particular, in wireless
networks, packets can be dropped because of corruption loss, making
the typical reaction of a congestion control mechanism inappropriate.
As a result, non-congestive loss may be more prevalent in these
networks due to corruption loss (when the wireless link cannot be
conditioned to properly control its error rate or due to transient
wireless links interruption in areas of poor coverage).
TCP over wireless and satellite is a topic that has been investigated
for a long time [Krishnan04]. There are some proposals where the
congestion control mechanism would react as if a packet had not been
dropped in the presence of corruption (cf. TCP HACK [Balan01]), but
discussions in the IETF have shown that there is no agreement that
this type of reaction is appropriate. For instance, it has been said
that congestion can manifest itself as corruption on shared wireless
links, and it is questionable whether a source that sends packets
that are continuously impaired by link noise should keep sending at a
high rate because it has lost the integrity of the feedback loop.
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Generally, two questions must be addressed when designing congestion
control mechanism that takes corruption loss into account:
1. How is corruption detected?
2. What should be the reaction?
In addition to question 1 above, it may be useful to consider
detecting the reason for corruption, but this has not yet been done
to the best of our knowledge.
Corruption detection can be done using an in-band or out-of-band
signaling mechanism, much in the same way as described for
Challenge 1. Additionally, implicit detection can be considered: link
layers sometimes retransmit erroneous frames, which can cause the
end-to-end delay to increase - but, from the perspective of a sender
at the transport layer, there are many other possible reasons for
such an effect.
Header checksums provide another implicit detection possibility: if a
checksum only covers all the necessary header fields and this
checksum does not show an error, it is possible for errors to be
found in the payload using a second checksum. Such error detection is
possible with UDP-Lite and DCCP; it was found to work well over a
GPRS network in a study [Chester04] and poorly over a WiFi network in
another study [Rossi06] [Welzl08]. Note that, while UDP-Lite and DCCP
enable the detection of corruption, the specifications of these
protocols do not foresee any specific reaction to it for the time
being.
The idea of having a transport end-point detecting and accordingly
reacting (or not) to corruption poses a number of interesting
questions regarding cross-layer interactions. As IP is designed to
operate over arbitrary link layers, it is therefore difficult to
design a congestion control mechanism on top of it that appropriately
reacts to corruption - especially as the specific data link layers
that are in use along an end-to-end path are typically unknown to
entities at the transport layer.
While the IETF has not yet specified how a congestion control
mechanism should react to corruption, proposals exist in the
literature. For instance, TCP Westwood sets the congestion window
equal to the measured bandwidth at the time of congestion in response
to three DupACKs or a timeout. This measurement is obtained by
counting and filtering the ACK rate. This setting provides a
significant goodput improvement in noisy channels because the "blind"
by half window reduction of standard TCP is avoided, i.e. the window
is not reduced by too much [Mascolo01].
Open questions concerning corruption loss include:
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- How should corruption loss be detected?
- How should a source react when it is known that corruption has
occurred?
- Can an ECN-capable flow infer that loss must be due to corruption
just from lack of explicit congestion notifications around a loss
episode [LT-TCP]? Or could this inference be dangerous given the
transport does not know whether all queues on the path are ECN-
capable or not?
3.3 Challenge 3: Packet Size
TCP does not take packet size into account when responding to losses
or ECN. Over past years, the performance of TCP congestion avoidance
algorithms has been extensively studied. The well known "square root
formula" provides the performance of the TCP congestion avoidance
algorithm for TCP Reno [RFC2581]. [Padhye98] enhances the model to
account for timeouts, receiver window, and delayed ACKs.
For the sake of the present discussion, we will assume that the TCP
throughput is expressed using the simplified formula. Using this
formula, the TCP throughput B is proportional to the segment size
and inversely proportional to the RTT and the square root of the
drop probability:
S 1
B ~ C --- -------
RTT sqrt(p)
where, S is the TCP segment size (in bytes)
RTT is the end-to-end round trip time of the TCP
connection (in seconds)
p is the packet drop probability
Neglecting the fact that the TCP rate linearly depends on it,
choosing the ideal packet size is a trade-off between high throughput
(the larger a packet, the smaller the relative header overhead) and
low packet latency (the smaller a packet, the shorter the time that
is needed until it is filled with data). Observing that TCP is not
optimal for applications with streaming media (since reliable in-
order delivery and congestion control can cause arbitrarily long
delays), this trade-off has not usually been considered for TCP
applications. Therefore, the influence of the packet size on the
sending rate has not typically been seen as a significant issue,
given there are still few paths through the Internet that support
packets larger than the 1500Bytes common with Ethernet.
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The situation is already different for the Datagram Congestion
Control Protocol (DCCP) [RFC4340], which has been designed to enable
unreliable but congestion-controlled datagram transmission, avoiding
the arbitrary delays associated with TCP. DCCP is intended for
applications such as streaming media that can benefit from control
over the tradeoffs between delay and reliable in-order delivery.
DCCP provides for a choice of modular congestion control mechanisms.
DCCP uses Congestion Control Identifiers (CCIDs) to specify the
congestion control mechanism. Three profiles are currently specified:
- DCCP Congestion Control ID 2 (CCID 2) [RFC4341]:
TCP-like Congestion Control. CCID 2 sends data using a close
approximation of TCP's congestion control, incorporating a
variant of SACK [RFC2018, RFC3517]. CCID 2 is suitable for senders
which can adapt to the abrupt changes in congestion window typical
of TCP's AIMD congestion control, and particularly useful for
senders which would like to take advantage of the available
bandwidth in an environment with rapidly changing conditions.
- DCCP Congestion Control ID 3 (CCID 3) [RFC4342]:
TCP-Friendly Rate Control (TFRC) [RFC3448bis] is a congestion
control mechanism designed for unicast flows operating in a best-
effort Internet environment. When competing for bandwidth its
window is similar to TCP flows, but has a much lower variation of
throughput over time than TCP, making it more suitable for
applications such as streaming media where a relatively smooth
sending rate is of importance. CCID 3 is appropriate for flows that
would prefer to minimize abrupt changes in the sending rate,
including streaming media applications with small or moderate
receiver buffering before playback.
- DCCP Congestion Control ID 4 [draft-ietf-ccid4-04.txt]:
TFRC Small Packets (TFRC-SP) [RFC4828], a variant of the TFRC
mechanism has been designed for applications that exchange small
packets. The objective of TFRC-SP is to achieve the same bandwidth
in 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. CCID 4 has been designed to be used either
by applications that use a small fixed segment size, or by
applications that change their sending rate by varying the segment
size. Because CCID 4 is intended for applications that use a fixed
small segment size, or that vary their segment size in response to
congestion, the transmit rate derived from the TCP throughput
equation is reduced by a factor that accounts for the packet header
size, as specified in [RFC4828].
The resulting open questions are:
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- How does TFRC-SP operate under various network conditions?
- How to design congestion control so as to scale with packet
size (dependency of congestion algorithm on packet size)?
Today, many network resources are designed so that packet processing
cannot be overloaded even for incoming loads at the maximum bit-rate
of the line. If packet processing can handle sustained load r [packet
per second] and the minimum packet size is h [bit] (i.e. frame,
packet and transport headers with no payload), then a line rate of x
[bit per second] will never be able to overload packet processing as
long as x =< r.h. However, realistic equipment is often designed to
only cope with a near-worst-case workload with a few larger packets
in the mix, rather than the worst-cast of all minimum size packets.
In this case, x = r.(h + e) for some small value of e. Therefore,
further research should be conducted to determine if the hypothesis
that most congestion seen on today's Internet is due to an excess of
bits rather than packets is verified, although packet-congestion is
not impossible for runs of small packets (e.g. TCP ACKs or DoS
attacks with TCP SYNs or small UDP datagrams).
This observation raises additional open issues:
- 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 for the future? An alternative view is that in-network
processing will become commonplace, so that per-packet processing
will be as likely to be the bottleneck as per-bit transmission
[Shin08].
Over the last three decades, performance gains have mainly been
achieved through increased packet rates, not bigger packets. But if
bigger maximum segment sizes do become more prevalent, tiny
segments (e.g. ACKs) will not stop being widely used - leading to a
widening range of packet sizes.
The open question is thus whether or not packet processing rates
(r) will keep up with growth in transmission rates (x). A
superficial look at Moore's Law type trends would suggest that
processing (r) will continue to outstrip growth in transmission
(x). But predictions based on actual knowledge of technology
futures would be useful. Another open question is whether there are
likely to be more small packets in the average packet mix. If the
answers to either of these questions predict that packet congestion
could become prevalent, congestion control protocols will have to
be more complicated.
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- Confusable Causes of Loss
There is a considerable body of research on how to distinguish
whether packet drops are due to transmission corruption or to
congestion. But the full list of confusable causes of loss is
longer and includes transmission corruption loss, congestion loss
(bit congestion and packet congestion), and policing loss.
If congestion is due to excess bits, the bit rate should be
reduced. If congestion is due to excess packets, the packet rate
can be reduced without reducing the bit rate - by using larger
packets. However, if the transport cannot tell which of these
causes led to a specific packet drop, its only safe response is
to reduce the bit rate. This is why the Internet would be more
complicated if packet congestion were prevalent, as reducing the
bit rate normally also reduces the packet rate, while reducing
the packet rate does not necessarily reduce the bit rate.
Given distinguishing between corruption loss and congestion is
already an open issue (Section 3.2), if that problem is ever
solved, a further open issue would be whether to standardize a
solution that distinguishes all the above causes of loss, not just
two of them.
Nonetheless, even if we find a way for network equipment to
explicitly distinguish which sort of loss has occurred, we will
never be able to assume that such a smart AQM solution is deployed
at every congestible resource throughout the Internet - at every
higher layer device like firewalls, proxies, servers and at every
lower layer device like low-end Hubs, DSLAMs, WLAN cards, cellular
base-stations and so on. Thus, transport protocols will always
have to cope with packet drops due to unpredictable causes, so we
should always treat, e.g., AQM as an optimization because as long
as it is not ubiquitous throughout the public Internet.
- What does a congestion notification on a packet of a certain size
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 under discussion in the Transport Area Working Group.
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- Packet Size and Congestion Control Protocol Design
If the above recommendation is correct - that the packet size of a
congestion notification should be taken into account when the
transport reads, not when the network writes the notification - it
opens up a significant program of protocol engineering and re-
engineering. Indeed, TCP does not take packet size into account
when responding to losses or ECN. At present this is not a pressing
problem because use of 1500B data segments is very prevalent for
TCP and the incidence of alternative maximum segment sizes is not
large. However, we should design the Internet's protocols so they
will scale with packet size. So, an open issue is whether we should
evolve TCP to be sensitive to packet size, or expect new protocols
to take over.
As we continue to standardize new congestion control protocols, we
must then face the issue of how they should take account of packet
size. It is still an open research issue to establish whether TCP
was correct in not taking packet size into account. If it is
determined that TCP was wrong in this respect, we should discourage
future protocol designs from following TCP's example. For example,
as explained here above, the small-packet variant of TCP-friendly
rate control (TFRC-SP [RFC4828]) is an experimental protocol that
aims to take account of packet size. Whatever packet size it uses,
it ensures its rate approximately equals that of a TCP using 1500B
segments. This raises the further question of whether TCP with
1500B segments will be a suitable long-term gold standard, or
whether we need a more thorough review of what it means for a
congestion control to scale with packet size.
3.4 Challenge 4: Flow Startup
The beginning of data transmissions imposes some further, unique
challenges: when a connection to a new destination is established,
the end-systems have hardly any information about the characteristics
of the path in between and the available bandwidth. In this flow
startup situation there is no obvious choice how to start to send. A
similar problem also occurs after relatively long idle times, since
the congestion control state then no longer reflects current
information about the state of the network (flow restart problem).
Van Jacobson [Jacobson88] suggested using the slow-start mechanism
both for the flow startup and the flow restart, and this is today's
standard solution [RFC2581] [RFC5681]. Per [RFC5681], the slow-start
algorithm is used when the congestion window (cwnd) < slow start
threshold (ssthresh), whose initial value is set arbitrarily high
(e.g., to the size of the largest possible advertised window) and
reduced in response to congestion. During slow start, TCP increments
the cwnd by at most Sender MSS bytes for each ACK received that
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cumulatively acknowledges new data. Slow start ends when cwnd
exceeds ssthresh or when congestion is observed. However, the slow-
start is not optimal in many situations. First, it can take quite a
long time until a sender can fully utilize the available bandwidth
on a path. Second, the exponential increase may be too aggressive
and cause multiple packet loss if large congestion windows are
reached (slow-start overshooting). Finally, the slow-start does not
ensure that new flows converge quickly to a reasonable share of
resources, in particular, when the new flows compete with long-
lived flows and comes out of slow-start early (slow-start vs
overshoot trade-off). This convergence problem may even worsen if
more aggressive congestion control variants get widely used.
The slow-start and its interaction with the congestion avoidance
phase was largely designed by intuition [Jacobson88]. So far, little
theory has been developed to understand the flow startup problem and
its implication on congestion control stability and fairness. There
is also no established methodology to evaluate whether new flow
startup mechanisms are appropriate or not.
As a consequence, it is a non-trivial task to address the
shortcomings of the slow-start algorithm. Several experimental
enhancements have been proposed, such as congestion window validation
[RFC2861] and limited slow-start [RFC3742]. There are also ongoing
research activities, focusing e.g. on bandwidth estimation
techniques, delay-based congestion control, or rate pacing
mechanisms. However, any alternative end-to-end flow startup approach
has to cope with the inherent problem that there is no or only little
information about the path at the beginning of a data transfer. This
uncertainty could be reduced by more expressive feedback signaling
(cf. Section 3.1). For instance, a source could learn the path
characteristics faster with the Quick-Start mechanism [RFC4782]. But,
even if the source knew exactly what rate it should aim for, it would
still not necessarily be safe to jump straight to that rate. The end-
system still does not know how a change in its own rate will affect
the path, which also might become congested in less than one RTT.
Further research would be useful to understand the effect of
decreasing the uncertainty by explicit feedback separately from
control theoretic stability questions. Furthermore, flow startup
also raises fairness questions. For instance, it is unclear whether
it could be reasonable to use a faster startup when an end-system
detects that a path is currently not congested.
In summary, there are several topics for further research concerning
flow startup:
- Better theoretical understanding of the design and evaluation of
flow startup mechanisms, concerning their impact on congestion
risk, stability, and fairness.
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- Evaluate whether it may be appropriate to allow alternative
starting schemes, e.g., to allow higher initial rates under certain
constraints; this also requires refining the definition of fairness
for startup situations.
- Better theoretical models for the effects of decreasing
uncertainty by additional network feedback, in particular if the
path characteristics are very dynamic.
3.5 Challenge 5: Multi-domain Congestion Control
Transport protocols such as TCP operate over the Internet, which is
divided into autonomous systems. These systems are characterized by
their heterogeneity as IP networks are realized by a multitude of
technologies.
3.5.1 Multi-domain Transport of Explicit Congestion Notification
The variety of conditions and their variations leads to correlation
effects between policers that regulate traffic against certain
conformance criteria.
With the advent of techniques allowing for early detection of
congestion, packet loss is no longer the sole metric of congestion.
ECN (Explicit Congestion Notification) marks packets - set by active
queue management techniques - to convey congestion information
trying to prevent packet losses (packet loss and the number of
packets marked gives an indication of the level of congestion). Using
TCP ACKs to feed back that information allows the hosts to realign
their transmission rate and thus, encourages them to efficiently use
the network. In IP, ECN uses the two unused bits of the Type Of
Service (TOS) field [RFC2474]. Further, ECN in TCP uses two bits in
the TCP header that were previously defined as reserved [RFC793].
ECN [RFC3168] is an example of a congestion feedback mechanism from
the network toward hosts. The congestion-based feedback scheme
however has limitations when applied on an inter-domain basis.
Indeed, Sections 8 and 19 of [RFC3168] details the implications
of i) a network erasing CE introduced earlier on the path and ii) a
network changing Not-ECN Capable Transport (ECT) to ECT. Both of
which could allow an attacking network to cause excess congestion in
an upstream network, even if the transports were behaving correctly.
There are to date two possible solutions to problem:
i) The ECN-nonce [RFC3540] and the re-ECN incentive system.
Nevertheless, the absence of an IPv6 header checksum implies that
corruption could be more impacting than in the IPv4 case.
Fragmentation is another: the ECN-nonce cannot protect against
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misbehaving receivers that conceal marked fragments; thus, some
protection is lost in situations where Path MTU discovery is
disabled. Note also that ECN-nonce wouldn't protect against
attack.
ii) (Changing Not-ECT to ECT) because by definition a Not-ECT packet
comes from a source without ECN enabled, and therefore, without
the ECN-nonce enabled. So, there is still room for improvement
on the ECN mechanism when operating in multi-domain networks.
Operational/deployment experience is nevertheless required to
determine the extent of these problems. The second problem is mainly
related to deployment and usage practices and does not seem to result
in any specific research challenge.
Another controversial solution in a multi-domain environment may be
the TCP rate controller (TRC), a traffic conditioner which regulates
the TCP flow at the ingress node in each domain by controlling packet
drops and delays of the packets in a flow. The outgoing traffic from
a TRC controlled domain is shaped in such a way that no packets are
dropped at the policer. However, the TRC interferes with the end-to-
end TCP model, and thus it would interfere with past and future
diversity of TCP implementations (violating the end-to-end
principle). In particular, the TRC embeds the flow rate equality view
of fairness in the network, and would prevent evolution to forms of
fairness based on congestion-volume (Section 2.3).
3.5.2 Multi-domain Exchange of Topology or Explicit Rate Information
Security is a challenge for multi-domain exchange of explicit rate
signals, whether in-band or out-of-band. At domain boundaries,
authentication and authorization issues can arise whenever congestion
control information is exchanged. From this perspective, the Internet
does not so far have any security architecture for this problem.
The future evolution of the Internet inter-domain operation has to
show whether more multi-domain information exchange can be
effectively realized. This is of particular importance for congestion
control schemes that make use of explicit per-datagram rate feedback
(e.g. RCP or XCP) or explicit rate feedback that use in-band
congestion signaling (e.g. QuickStart) or out-of-band signaling (e.g.
CADPC/PTP). Explicit signaling exchanges at the inter-domain level
that result in local domain triggers are currently absent from the
Internet. From this perspective, security means resulting from
limited trust between different administrative units result in policy
enforcement that exacerbates the difficulty encountered when explicit
feedback congestion control information is exchanged between domains.
Note that even though authentication mechanisms could be extended for
this purpose (by recognizing that explicit rate schemes such as RCP
or XCP have the same inter-domain security requirements and structure
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as IntServ), they suffer from the same scalability problems as
identified in [RFC2208]. Indeed, in-band rate signaling or out-of-
band per-flow traffic specification signaling (like in RSVP) results
in similar scalability issues.
Also, many autonomous systems also only exchange some limited amount
of information about their internal state (topology hiding
principle), even though having more precise information could be
highly beneficial for congestion control. Indeed, revealing the
internal network structure is highly sensitive in multi-domain
network operations and thus, also a concern when it comes to the
deployability of congestion control schemes. For instance, a network-
assisted congestion control scheme with explicit signaling could
reveal more information about the internal network dimensioning than
TCP does today.
3.5.3 Multi-domain Pseudowires
Extending pseudo-wires across multiple domains poses specific issues.
Pseudowires (PW) may carry non-TCP data flows (e.g. TDM traffic) over
a multi-domain IP network. Structure Agnostic TDM over Packet
(SATOP) [RFC4553], Circuit Emulation over Packet Switched Networks
(CESoPSN), TDM over IP, are not responsive to congestion control as
discussed by [RFC2914] (see also [RFC5033]).
Moreover, it is not possible to simply reduce the flow rate of a TDM
PW when facing packet loss. Providers can rate control corresponding
incoming traffic but they may not be able to detect that PWs carry
TDM traffic (mechanisms for characterizing the traffic temporal
properties may not necessarily be supported). This can be illustrated
with the following example.
........... ............
. . .
S1 --- E1 --- . .
. | . .
. === E5 === E7 ---
. | . . |
S2 --- E2 --- . . |
. . . | |
........... . | v
. ----- R --->
........... . | ^
. . . | |
S3 --- E3 --- . . |
. | . . |
. === E6 === E8 ---
. | . .
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S4 --- E4 --- . .
. . .
........... ............
\---- P1 ---/ \---------- P2 -----
Sources S1, S2, S3 and S4 are originating TDM over IP traffic. P1
provider edges E1, E2, E3, and E4 are rate limiting such traffic. The
SLA of provider P1 with transit provider P2 is such that the latter
assumes a BE traffic pattern and that the distribution shows the
typical properties of common BE traffic (elastic, non-real time, non-
interactive).
The problem arises for transit provider P2 that is not able to detect
that IP packets are carrying constant-bit rate service traffic for
which the only useful congestion control mechanism would rely on
implicit or explicit admission control, meaning self-blocking or
enforced blocking respectively.
Assuming P1 providers are rate limiting BE traffic, a transit P2
provider router R may be subject to serious congestion as all TDM PWs
cross the same router. TCP-friendly traffic (e.g. each flow within
the PW) would follow TCP's AIMD algorithm of reducing the sending
rate in half in response to each packet drop. Nevertheless, the PWs
carrying TDM traffic could take all the available capacity while
other more TCP-friendly or generally congestion-responsive traffic
reduced itself to nothing. Note here that the situation may simply
occur because S4 suddenly turns on additional TDM channels.
It is neither possible nor desirable to assume that edge routers will
soon have the ability to detect the responsiveness of the carried
traffic, but it is still important for transit providers to be able
to police a fair, robust, responsive and efficient congestion control
technique in order to avoid impacting congestion responsive Internet
traffic.
However, we must not require only certain specific responses to
congestion to be embedded within the network, which would harm
evolvability. So designing the corresponding mechanisms in the data
and control planes still requires further investigation.
3.6 Challenge 6: Precedence for Elastic Traffic
Traffic initiated by so-called elastic applications adapt to the
available bandwidth using feedback about the state of the network.
For all these flows the application dynamically adjusts the data
generation rate. Examples encompass short-lived elastic traffic
including HTTP and instant messaging traffic as well as long file
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transfers with FTP. In brief, elastic data applications can show
extremely different requirements and traffic characteristics.
The idea to distinguish several classes of best-effort traffic types
is rather old, since it would be beneficial to address the relative
delay sensitivities of different elastic applications. The notion of
traffic precedence was already introduced in [RFC791], and it was
broadly defined as "An independent measure of the importance of this
datagram." For instance, low precedence traffic should experience
lower average throughput than higher precedence traffic. Several
questions arise here: what is the meaning of "relative"? What is the
role of the Transport Layer?
The preferential treatment of higher precedence traffic combined with
appropriate congestion control mechanisms is still an open issue that
may, depending on the proposed solution, impact both the host and the
network precedence awareness, and thereby congestion control.
[RFC2990] points out that the interactions between congestion control
and DiffServ [RFC2475] remained unaddressed up to recently.
Recently, a study and a potential solution have been proposed that
introduce Guaranteed TFRC (gTFRC) [Lochin06]. gTFRC is an adaptation
of TCP-Friendly Rate Control providing throughput guarantee for
unicast flows over the DiffServ/AF class. The purpose of gTFRC is to
distinguish the guaranteed part from the best-effort part of the
traffic resulting from AF conditioning. The proposed congestion
control has been specified and tested inside DCCP/CCID3 for
DiffServ/AF networks [Lochin07]. A complete reliable transport
protocol based-on gTFRC and SACK appears to be the first reliable
DiffServ/AF compliant transport protocol [Jourjon08].
Nevertheless, there is still work to be performed regarding lower
precedence traffic - data transfers which are useful, yet not
important enough to warrant significantly impairing other traffic.
Examples of applications that could make use of such traffic are web
caches and web browsers (e.g. for pre-fetching) as well as peer-to-
peer applications. There are proposals for achieving low precedence
on a pure end-to-end basis (e.g. TCP-LP [Kuzmanovic03]), and there is
a specification for achieving it via router mechanisms [RFC3662]. It
seems, however, that network-based lower precedence mechanisms are
not yet a common service on the Internet. There is an expectation
that end-to-end mechanisms for lower precedence e.g. [LEDBAT] could
become common --at least when competing with other traffic as part of
its own queues (e.g. in a home router). But it is less clear whether
user will be willing to make their background traffic yield to other
people's foreground traffic unless the appropriate incentives are
created.
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There is an issue over how to reconcile two divergent views of the
relation between traffic class precedence and congestion control. One
view considers that congestion signals (losses or explicit
notifications) in one traffic class are independent of those in
another. The other relates marking of the classes together within the
active queue management (AQM) mechanism [Gibbens02]. In the
independent case, using a higher precedence class of traffic gives a
higher scheduling precedence and generally lower congestion level. In
the linked case, higher precedence still gives higher scheduling
precedence, but results in a higher level of congestion. This higher
congestion level reflects the extra congestion higher precedence
traffic causes to both classes combined. The linked case separates
scheduling precedence from rate control. The end-to-end congestion
control algorithm can separately choose to take a higher rate by
responding less to the higher level of congestion. This second
approach could become prevalent if weighted congestion controls were
common. However, it is an open issue how the two approaches might co-
exist or how one might evolve into the other.
3.7 Challenge 7: Misbehaving Senders and Receivers
In the current Internet architecture, congestion control depends on
parties acting against their own interests. It is not in a receiver's
interest to honestly return feedback about congestion on the path,
effectively requesting a slower transfer. It is not in the sender's
interest to reduce its rate in response to congestion if it can rely
on others to do so. Additionally, networks may have strategic reasons
to make other networks appear congested.
Numerous strategies to improve congestion control have already been
identified. The IETF has particularly focused on misbehaving TCP
receivers that could confuse a compliant sender into assigning
excessive network and/or server resources to that receiver (e.g.
[Savage99], [RFC3540]). But, although such strategies are worryingly
powerful, they do not yet seem common (however, evidence of attack
prevalence is itself a research requirement).
A growing proportion of Internet traffic comes from applications
designed not to use congestion control at all, or worse, applications
that add more forward error correction the more losses they
experience. Some believe the Internet was designed to allow such
freedom so it can hardly be called misbehavior. But others consider
that it is misbehavior to abuse this freedom [RFC3714], given one
person's freedom can constrain the freedom of others (congestion
represents this conflict of interests). Indeed, leaving freedom
unchecked might result in congestion collapse in parts of the
Internet. Proportionately, large volumes of unresponsive voice
traffic could represent such a threat, particularly for countries
with less generous provisioning [RFC3714]. Also, Internet video on
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demand services are becoming popular that transfer much greater data
rates without congestion control. In general, it is recommended that
such UDP applications use some form of congestion control [RFC5405].
Note that the problem is not just misbehavior driven by a self-
interested desire for more bandwidth. Indeed, congestion control may
be attacked by someone who makes no gain for themselves, other than
the satisfaction of harming others (see Security Considerations in
Section 4).
Open research questions resulting from these considerations are:
- By design, new congestion control protocols need to enable one end
to check the other for protocol compliance. Still, it is unclear
how such mechanisms would have to be designed.
- Which congestion control primitives could safely satisfy more
demanding applications (smoother than TFRC, faster than high speed
TCPs), so that application developers and users do not turn off
congestion control to get the rate they expect and need.
Note also that self-restraint could disappear from the Internet.
So, it may no longer be sufficient to rely on developers/users
voluntarily submitting themselves to congestion control. As a
consequence, mechanisms to enforce fairness (see Sections 2.3, 3.4,
and 3.5) need to have more emphasis within the research agenda.
3.8 Other Challenges
This section provides additional challenges and open research issues
that are not (at this point in time) deemed very large or of
different nature compared to the main challenges depicted so far.
3.8.1 RTT Estimation
Several congestion control schemes have to precisely know the round-
trip time (RTT) of a path. The RTT is a measure of the current delay
on a network. It is defined as the delay between the sending of a
packet and the reception of a corresponding response, if echoed back
immediately by the receiver upon receipt of the packet. This
corresponds to the sum of the one-way delay of the packet and the
(potentially different) one-way delay of the response. Furthermore,
any RTT measurement also includes some additional delay due to the
packet processing in both end-systems.
There are various techniques to measure the RTT: active measurements
inject special probe packets to the network and then measure the
response time, using e.g. ICMP. In contrast, passive measurements
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determine the RTT from ongoing communication processes, without
sending additional packets.
The connection endpoints of transport protocols such as TCP, SCTP,
and DCCP, as well as several application protocols, keep track of the
RTT in order to dynamically adjust protocol parameters such as the
retransmission timeout (RTO) or the rate control equation. They can
implicitly measure the RTT on the sender side by observing the time
difference between the sending of data and the arrival of the
corresponding acknowledgements. For TCP, this is the default RTT
measurement procedure, in combination with Karn's algorithm that
prohibits RTT measurements from retransmitted segments [RFC2988].
Traditionally, TCP implementations take one RTT measurement at a time
(i.e., about once per RTT). As alternative, the TCP timestamp option
[RFC1323] allows more frequent explicit measurements, since a sender
can safely obtain an RTT sample from every received acknowledgment.
In principle, similar measurement mechanisms are used by protocols
other than TCP.
Sometimes it would be beneficial to know the RTT not only at the
sender, but also at the receiver, e.g., to find the one-way variation
in delay due to one-way congestion. A passive receiver can deduce
some information about the RTT by analyzing the sequence numbers of
received segments. But this method is error-prone and only works if
the sender permanently sends data. Other network entities on the path
can apply similar heuristics in order to approximate the RTT of a
connection, but this mechanism is protocol-specific and requires per-
connection state. In the current Internet, there is no simple and
safe solution to determine the RTT of a connection in network
entities other than the sender. The more fundamental question being
to determine whether it is necessary or not for network elements to
measure or know the RTT.
As outlined earlier in this document, the round-trip time is
typically not a constant value. For a given path, there is
theoretical minimum value, which is given by the minimum
transmission, processing and propagation delay on that path. However,
additional variable delays might be caused by congestion, cross-
traffic, shared mediums access control schemes, recovery procedures,
or other sub-IP layer mechanisms. Furthermore, a change of the path
(e.g., route flapping, hand-over in mobile networks) can result in
completely different delay characteristics.
Due to this variability, one single measured RTT value is hardly
sufficient to characterize a path. This is why many protocols use RTT
estimators that derive an averaged value and keep track of a certain
history of previous samples. For instance, TCP endpoints derive a
smoothed round-trip time (SRTT) from an exponential weighted moving
average [RFC2988]. Such a low-pass filter ensures that measurement
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noise and single outliers do not significantly affect the estimated
RTT. Still, a fundamental drawback of low-pass filters is that the
averaged value reacts slower to sudden changes of the measured RTT.
There are various solutions to overcome this effect: For instance,
the standard TCP retransmission timeout calculation considers not
only the SRTT, but also a measure for the variability of the RTT
measurements [RFC2988]. Since this algorithm is not well-suited for
frequent RTT measurements with timestamps, certain implementations
modify the weight factors (e.g., [SK02]). There are also proposals
for more sophisticated estimators, such as Kalman filters or
estimators that utilize mainly peak values.
However, open questions concerning RTT estimation in the Internet
remain:
- Optimal measurement frequency: Currently, there is no theory of
common understanding of the right time scale of RTT measurement. In
particular, the necessity of rather frequent measurements
(e.g., per packet) is not well understood. There is some empirical
evidence that such frequent sampling may not have a significant
benefit [Allman99].
- Filter design: A closely related question is how to design good
filters for the measured samples. The existing algorithms are known
to be robust, but they are far from being perfect. The fundamental
problem is that there is no single set of RTT values that could
characterize the Internet as a whole, i.e., it is hard to define a
design target.
- Default values: RTT estimators can fail in certain scenarios, e.g.,
when any feedback is missing. In this case, default values have
to be used. Today, most default values are set to conservative
values that may not be optimal for most Internet communication.
Still, the impact of more aggressive settings is not well
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
(particularly on low cost devices) may still prevent highly
accurate RTT estimations.
3.8.2 Malfunctioning Devices
There is a long history of malfunctioning devices harming the
deployment of new and potentially beneficial functionality in the
Internet. Sometimes, such devices drop packets or even crash
completely when a certain mechanism is used, causing users to opt for
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reliability instead of performance and disable the mechanism, or
operating system vendors to disable it by default. One well-known
example is ECN, whose deployment was long hindered by malfunctioning
firewalls and is still hindered by malfunctioning home-hubs, but
there are many other examples (e.g. the Window Scaling option of TCP)
[Thaler07].
As new congestion control mechanisms are developed with the intention
of eventually seeing them deployed in the Internet, it would be
useful to collect information about failures caused by devices of
this sort, analyze the reasons for these failures, and determine
whether there are ways for such devices to do what they intend to do
without causing unintended failures. Recommendation for vendors of
these devices could be derived from such an analysis. It would also
be useful to see whether there are ways for failures caused by such
devices to become more visible to endpoints, or for those failures to
become more visible to the maintainers of such devices.
A possible way to reduce such problems in the future would be
guidelines for standards authors to ensure `forward compatibility' is
considered in all IETF work. That is, the default behavior of a
device should be precisely defined for all possible values and
combinations of protocol fields, not just the minimum necessary for
the protocol being defined. Then when previously unused or reserved
fields start to be used by newer devices to comply with a new
standard, older devices encountering unusual fields should at least
behave predictably.
3.8.3 Dependence on RTT
AIMD window algorithms that have the goal of packet conservation end
up converging on a rate that is inversely proportional to RTT.
However, control theoretic approaches to stability have shown that
only the increase in rate (acceleration) not the target rate needs to
be inversely proportional to RTT.
It is possible to have more aggressive behaviors for some demanding
applications as long as they are part of a mix with less aggressive
transports [Key04]. This beneficial effect of transport type mixing
is probably how the Internet currently manages to remain stable even
in the presence of TCP slow start, which is more aggressive than the
theory allows for stability. Research giving deeper insight into
these aspects would be very useful.
3.8.4 Congestion Control in Multi-layered Networks
A network of IP nodes is just as vulnerable to congestion in the
lower layers between IP-capable nodes as it is to congestion on the
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IP-capable nodes themselves. If network elements take a greater part
in congestion control (ECN, XCP, RCP, etc. - see Section 3.1), these
techniques will either need to be deployed at lower layers as well,
or they will need to interwork with lower layer mechanisms.
[ECN-tunnel] gives guidelines on propagating ECN from lower layers
upwards, but to the authors' knowledge the layering problem has not
been addressed for explicit rate protocol proposals such as XCP and
RCP. Some issues are straightforward matters of interoperability
(e.g. how exactly to copy fields up the layers) while others are
less obvious (e.g. re-framing issues: if RCP were deployed in a lower
layer, how might multiple small RCP frames all with different rates
in their headers be assembled into a larger IP-layer datagram?).
Multi-layer considerations also confound many mechanisms that aim to
discover whether every node on the path supports the new congestion
control protocol. For instance, some proposals maintain a secondary
TTL field parallel to that in the IP header. Any nodes that support
the new behavior update both TTL fields, whereas legacy IP nodes will
only update the IP TTL field. This allows the endpoints to check
whether all IP nodes on the path support the new behavior, in which
case both TTLs will be equal at the receiver. But mechanisms like
these overlook nodes at lower layers that might not support the new
behavior.
A further related issue is congestion control across overlay networks
of relays [Hilt08], [Noel07], [Shen08].
3.8.5 Multipath End-to-end Congestion Control and Traffic Engineering
Recent work has shown that multipath endpoint congestion control
[Kelly05] offers considerable benefits in terms of resilience and
resource usage efficiency. By pooling the resources on all paths,
even nodes not using multiple paths benefit from those that are.
There is considerable further research to do in this area,
particularly to understand interactions with network operator
controlled route provision and traffic engineering, and indeed
whether multipath congestion control can perform better traffic
engineering than the network itself, given the right incentives.
3.8.6 ALGs and Middleboxes
An increasing number of application layer gateways (ALG),
middleboxes, and proxies (see Section 3.6 of [RFC2775]) is deployed
at domain boundaries to verify conformance but also filter traffic
and control flows. One motivation is to prevent information beyond
routing data leaking between autonomous systems. These systems split
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up end-to-end TCP connections and disrupt end-to-end congestion
control. Furthermore, transport over encrypted tunnels may not allow
other network entities to participate in congestion control.
Basically, such systems disrupt the primal and dual congestion
control components. In particular, end-to-end congestion control may
be replaced by flow-control backpressure mechanisms on the split
connections. A large variety of ALGs and middleboxes use such
mechanisms to improve the performance of applications (Performance
Enhancing Proxies, Application Accelerators, etc.). However, the
implications of such mechanisms, which are often proprietary and not
documented, have not been studied systematically so far.
There are two levels of interference:
- The "transparent" case, i.e. the end-point address from the sender
perspective is still visible to the receiver (the destination IP
address). An example are relay systems that intercept payload but
do not relay congestion control information. Such middleboxes can
prevent the operation of end-to-end congestion control.
- The "non-transparent" case, which causes less problems. Although
these devices interfere with end-to-end network transparency, they
correctly terminate network, transport and application layer
protocols on both sides, which individually can be congestion
controlled.
4. Security Considerations
Misbehavior may be driven by pure malice, or malice may in turn be
driven by wider selfish interests, e.g. using distributed denial of
service (DDoS) attacks to gain rewards by extortion [RFC4948]. DDoS
attacks are possible both because of vulnerabilities in operating
systems and because the Internet delivers packets without requiring
congestion control.
To date, compliance with congestion control rules and being fair
requires end points to cooperate. The possibility of uncooperative
behavior can be regarded as a security issue; its implications are
discussed throughout these documents in a scattered fashion.
Currently the focus of the research agenda against denial of service
is about identifying attack packets, attacking machines and networks
hosting them, with a particular focus on mitigating source address
spoofing. But if mechanisms to enforce congestion control fairness
were robust to both selfishness and malice [Bri06] they would also
naturally mitigate denial of service against the network, which can
be considered (from the perspective of well-behaving Internet user)
as a congestion control enforcement problem. Even some denial of
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service attacks on hosts (rather than the network) could be
considered as a congestion control enforcement issue at the higher
layer. But clearly there are also denial of service attacks that
would not be solved by enforcing congestion control.
Sections 3.5 and 3.7 on multi-domain issues and misbehaving senders
and receivers also discuss some information security issues suffered
by various congestion control approaches.
5. References
5.1 RFC References
[RFC791] Postel, J., "Internet Protocol", STD 5, RFC 791,
September 1981.
[RFC793] Postel, J., "Transmission Control Protocol", STD 7,
RFC 793, September 1981.
[RFC896] Nagle, J., "Congestion Control in IP/TCP", RFC 896,
January 1984.
[RFC1323] Jacobson, V., Braden, R., and Borman, D., "TCP Extensions
for High Performance", RFC 1323, May 1992.
[RFC1701] Hanks, S., Li, T, Farinacci, D., and P. Traina, "Generic
Routing Encapsulation", RFC 1701, October 1994.
[RFC1958] Carpenter, B., Ed., "Architectural Principles of the
Internet", RFC 1958, June 1996.
[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 Weiss, W., "An Architecture for Differentiated
Services", RFC 2475, December 1998.
[RFC2581] Allman, M., Paxson, V., and W. Stevens, "TCP Congestion
Control", RFC 2581, April 1999.
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[RFC2861] Handley, M., J. Padhye, J., and S., Floyd, "TCP
Congestion Window Validation", RFC 2861, June 2000.
[RFC2784] Farinacci, D., Li, T., Hanks, S., Meyer, D. and P.
Traina, "Generic Routing Encapsulation (GRE)", RFC 2784,
March 2000.
[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, November 2000.
[RFC2990] Huston, G., "Next Steps for the IP QoS Architecture",
RFC 2990, November 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] Spring, N., and D. Wetherall, "Robust Explicit Congestion
Notification (ECN) Signaling with Nonces", RFC 3540, June
2003.
[RFC3662] Bless, R., Nichols, K., and K. Wehrle, "A Lower Effort
Per-Domain Behavior for Differentiated Services", RFC
3662, December 2003.
[RFC3714] Floyd, S., and J. Kempf, Eds. "IAB Concerns Regarding
Congestion Control for Voice Traffic in the Internet",
RFC 3714, March 2004.
[RFC3742] Floyd, S., "Limited Slow-Start for TCP with Large
Congestion Windows", RFC 3742, March 2004.
[RFC3985] Bryant, S., and P. Pate, "Pseudo Wire Emulation Edge-to-
Edge (PWE3) Architecture", RFC 3985, March 2005.
[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.
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[RFC4342] Floyd, S., Kohler, E., and J. Padhye, "Profile for
Datagram Congestion Control Protocol (DCCP) Congestion
Control ID 3: TCP-Friendly Rate Control (TFRC)", RFC
4342, March 2006.
[RFC4553] Vainshtein, A., and Y. Stein, "Structure-Agnostic Time
Division Multiplexing (TDM) over Packet (SAToP)",
RFC 4553, June 2006.
[RFC4614] Duke, M., R. Braden, R., Eddy, W., and E. Blanton, "A
Roadmap for Transmission Control Protocol (TCP)
Specification Documents", RFC 4614, September 2006.
[RFC4782] Floyd, S., Allman, M., Jain, A., and P. Sarolahti,
"Quick-Start for TCP and IP", RFC 4782, January 2007.
[RFC4948] Andersson, L., Davies, E., and L. Zhang, "Report from the
IAB workshop on Unwanted Traffic March 9-10, 2006", RFC
4948, August 2007.
[RFC5033] Floyd, S., and M. Allman, "Specifying New Congestion
Control Algorithms", RFC 5033, August 2007.
[RFC5290] Floyd, S., and M. Allman, "Comments on the Usefulness of
Simple Best-Effort Traffic", RFC 5290, July 2008.
[RFC5405] Eggert, L., and G. Fairhurst, "Unicast UDP Usage
Guidelines for Application Designers, RFC 5405, November
2008.
[RFC5681] Allman, M., Paxson, V., and Blanton, E., "TCP Congestion
Control", RFC 5681 (Obsoletes RFC 2581), September 2009.
[ICCRG-RFCs] Welzl, M., and W. Eddy, "Congestion Control in the RFC
Series", Internet Draft, work in Progress, October 2008.
5.2 Other References
[Allman99] Allman, M., and V. Paxson, "On Estimating End-to-End
Network Path Properties", Proceedings of ACM SIGCOMM'99,
September 1999.
[Andrew05] Andrew, L., Wydrowski, B., and S. Low, "An Example of
Instability in XCP", Manuscript available at
<http://netlab.caltech.edu/maxnet/XCP_instability.pdf>
[Ath01] Athuraliya, S., Low, S., Li, V., and Q. Yin, "REM: Active
Queue Management", IEEE Network Magazine, Vol.15, No.3,
pp.48-53, May 2001.
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[Balan01] Balan, R. K., Lee, B.P., Kumar, K.R.R., Jacob, L., Seah,
W.K.G., and Ananda, A.L., "TCP HACK: TCP Header Checksum
Option to Improve Performance over Lossy Links",
Proceedings of IEEE INFOCOM'01, Anchorage (Alaska), USA,
April 2001.
[Bonald00] Bonald, T., May, M., and J.-C. Bolot, "Analytic
Evaluation of RED Performance," Proceedings of IEEE
INFOCOM'00, Tel Aviv, Israel, March 2000.
[Bri08] Briscoe, B., Moncaster, T. and L. Burness, "Problem
Statement: Transport Protocols Don't Have To Do
Fairness", Work in progress, draft-briscoe-tsvwg-relax-
fairness-01, July 2008.
[Bri07] Briscoe, B., "Flow Rate Fairness: Dismantling a
Religion", ACM SIGCOMM Computer Communication Review,
Vol.37, No.2, pp.63-74, April 2007.
[Bri06] Briscoe, B., "Using Self-interest to Prevent Malice;
Fixing the Denial of Service Flaw of the Internet,"
Workshop on the Economics of Securing the Information
Infrastructure, October 2006.
<http://wesii.econinfosec.org/draft.php?paper_id=19>
[Bri09] Briscoe, B., " Re-feedback: Freedom with Accountability
for Causing Congestion in a Connectionless Internetwork,"
UCL PhD Thesis (2009).
[Bryant08] Bryant, S., Davie, B., Martini, L., and E. Rosen,
"Pseudowire Congestion Control Framework", Work in
Progress, draft-ietf-pwe3-congestion-frmwk-01.txt, May
2008.
[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] Chiu, D.M., 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.
[Clark88] Clark, D., "The design philosophy of the DARPA internet
protocols", ACM SIGCOMM Computer Communication Review,
Vol.18, No.4, pp.106-114, August 1988.
[Clark98] Clark, D., and W. Fang, "Explicit Allocation of Best-
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Effort Packet Delivery Service," IEEE/ACM Transactions
on Networking, Vol.6, No.4, pp.362-373, August 1998.
[Dukki05] Dukkipati, N., Kobayashi, M., Zhang-Shen, R. and N.,
McKeown, "Processor Sharing Flows in the Internet",
Proceedings of International Workshop on QoS (IWQoS'05),
Passau, Germany, June 2005.
[Dukki06] Dukkipati, N. and N. McKeown, "Why Flow-Completion Time
is the Right Metric for Congestion Control", ACM SIGCOMM
Computer Communication Review, Vol.36, No.1, January
2006.
[ECN-tunnel] Briscoe, B., "Layered Encapsulation of Congestion
Notification", draft-ietf-tsvwg-ecn-tunnel, Internet
Draft, Work in progress.
[ECODE] "ECODE Project", European Commission Seventh Framework
Program, Grant No. 223936, <http://www.ecode-project.eu>
[Falk07] Falk, A., et al., "Specification for the Explicit Control
Protocol (XCP)", Work in Progress, draft-falk-xcp-spec-
03.txt, July 2007.
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6. Acknowledgments
The authors would like to thank the following people whose feedback
and comments contributed to this document: Keith Moore, Jan
Vandenabeele.
Dimitri Papadimitriou's contribution was partly funded by [ECODE], a
research project supported by the European Commission.
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.
Michael Scharf is now with Alcatel-Lucent.
7. Author's Addresses
Dimitri Papadimitriou (Editor)
Alcatel-Lucent
Copernicuslaan, 50
2018 Antwerpen, Belgium
Phone: +32 3 240 8491
Email: dimitri.papadimitriou@alcatel-lucent.be
Michael Welzl
University of Oslo, Department of Informatics
PO Box 1080 Blindern
N-0316 Oslo, Norway
Phone: +47 22 85 24 20
Email: michawe@ifi.uio.no
Michael Scharf
University of Stuttgart
Pfaffenwaldring 47
70569 Stuttgart, Germany
Email: michael.scharf@googlemail.com
Bob Briscoe
BT & UCL
B54/77, Adastral Park
Martlesham Heath
Ipswich IP5 3RE, UK
Email: bob.briscoe@bt.com
8. Contributors
D.Papadimitriou Expires - November 2010 [Page 44]
Open Research Issues in Internet Congestion Control May 2010
The following additional people have contributed to this document:
- Wesley Eddy <weddy@grc.nasa.gov>
- Bela Berde <bela.berde@gmx.de>
- Paulo Loureiro <loureiro.pjg@gmail.com>
- Chris Christou <christou_chris@bah.com>
D.Papadimitriou Expires - November 2010 [Page 45]
Open Research Issues in Internet Congestion Control May 2010
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D.Papadimitriou Expires - November 2010 [Page 46]
