| Rfc | 6077 |
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| Title | Open Research Issues in Internet Congestion Control |
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| Author | D.
Papadimitriou, Ed., M. Welzl, M. Scharf, B. Briscoe |
|---|
| Date | February 2011 |
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| Format: | TXT, HTML |
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| Status: | INFORMATIONAL |
|---|
|
Internet Research Task Force (IRTF) D. Papadimitriou, Ed.
Request for Comments: 6077 Alcatel-Lucent
Category: Informational M. Welzl
ISSN: 2070-1721 University of Oslo
M. Scharf
University of Stuttgart
B. Briscoe
BT & UCL
February 2011
Open Research Issues in Internet Congestion Control
Abstract
This document describes some of the open problems in Internet
congestion control that are known today. This includes several new
challenges that are becoming important as the network grows, as well
as some issues that have been known for many years. These challenges
are generally considered to be open research topics that may require
more study or application of innovative techniques before Internet-
scale solutions can be confidently engineered and deployed.
Status of This Memo
This document is not an Internet Standards Track specification; it is
published for informational purposes.
This document is a product of the Internet Research Task Force
(IRTF). The IRTF publishes the results of Internet-related research
and development activities. These results might not be suitable for
deployment. This RFC represents the consensus of the Internet
Congestion Control Research Group (ICCRG) of the Internet Research
Task Force (IRTF). Documents approved for publication by the IRSG
are not a candidate for any level of Internet Standard; see Section 2
of RFC 5741.
Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
http://www.rfc-editor.org/info/rfc6077.
Copyright Notice
Copyright (c) 2011 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(http://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect
to this document.
Table of Contents
1. Introduction ....................................................3
2. Global Challenges ...............................................5
2.1. Heterogeneity ..............................................5
2.2. Stability ..................................................7
2.3. Fairness ...................................................8
3. Detailed Challenges ............................................10
3.1. Challenge 1: Network Support ..............................10
3.1.1. Performance and Robustness .........................14
3.1.2. Granularity of Network Component Functions .........15
3.1.3. Information Acquisition ............................16
3.1.4. Feedback Signaling .................................17
3.2. Challenge 2: Corruption Loss ..............................17
3.3. Challenge 3: Packet Size ..................................19
3.4. Challenge 4: Flow Startup .................................24
3.5. Challenge 5: Multi-Domain Congestion Control ..............26
3.5.1. Multi-Domain Transport of Explicit
Congestion Notification ............................26
3.5.2. Multi-Domain Exchange of Topology or
Explicit Rate Information ..........................27
3.5.3. Multi-Domain Pseudowires ...........................28
3.6. Challenge 6: Precedence for Elastic Traffic ...............30
3.7. Challenge 7: Misbehaving Senders and Receivers ............31
3.8. Other Challenges ..........................................33
3.8.1. RTT Estimation .....................................33
3.8.2. Malfunctioning Devices .............................35
3.8.3. Dependence on RTT ..................................36
3.8.4. Congestion Control in Multi-Layered Networks .......36
3.8.5. Multipath End-to-End Congestion Control and
Traffic Engineering ................................37
3.8.6. ALGs and Middleboxes ...............................37
4. Security Considerations ........................................38
5. References .....................................................39
5.1. Informative References ....................................39
6. Acknowledgments ................................................50
7. Contributors ...................................................50
1. Introduction
This document, the result of the Internet Congestion Control Research
Group (ICCRG), describes some of the open research topics in the
domain of Internet congestion control that are known today. We begin
by reviewing some proposed definitions of congestion and congestion
control based on current understandings.
Congestion can be defined as a state or condition that occurs when
network resources are overloaded, resulting in impairments for
network users as objectively measured by the probability of loss
and/or delay. The overload results in the reduction of utility in
networks that support both spatial and temporal multiplexing, but no
reservation [Keshav07]. Congestion control is a (typically
distributed) algorithm to share network resources among competing
traffic sources.
Two components of distributed congestion control have been defined in
the context of primal-dual modeling [Kelly98]. Primal congestion
control refers to the algorithm executed by the traffic sources for
controlling their sending rates or window sizes. This is normally a
closed-loop control, where this operation depends on feedback. TCP
algorithms fall in this category. Dual congestion control is
implemented by the routers through gathering information about the
traffic traversing them. A dual congestion control algorithm
updates, implicitly or explicitly, a congestion measure or congestion
rate and sends it back, implicitly or explicitly, to the traffic
sources that use that link. Queue management algorithms such as
Random Early Detection (RED) [Floyd93] or Random Exponential Marking
(REM) [Ath01] fall into the "dual" category.
Congestion control provides for a fundamental set of mechanisms for
maintaining the stability and efficiency of the Internet. Congestion
control has been associated with TCP since Van Jacobson's work in
1988, but there is also congestion control outside of TCP (e.g., for
real-time multimedia applications, multicast, and router-based
mechanisms) [RFC5783]. The Van Jacobson end-to-end congestion
control algorithms [Jacobson88] [RFC2581] [RFC5681] are used by the
Internet transport protocol TCP [RFC4614]. They have been proven to
be highly successful over many years but have begun to reach their
limits, as the heterogeneity of the data link and physical layer on
the one hand, and of applications on the other, are pulling TCP
congestion control beyond its natural operating regime. This is
because it performs poorly as the bandwidth or delay increases. A
side effect of these deficiencies is that an increasing share of
hosts use non-standardized congestion control enhancements (for
instance, many Linux distributions have been shipped with "CUBIC"
[Ha08] as the default TCP congestion control mechanism).
While the original Van Jacobson algorithm requires no congestion-
related state in routers, more recent modifications have departed
from the strict application of the end-to-end principle [Saltzer84]
in order to avoid congestion collapse. Active Queue Management (AQM)
in routers, e.g., RED and some of its variants such as Adaptive RED
(ARED), improves performance by keeping queues small (implicit
feedback via dropped packets), while Explicit Congestion Notification
(ECN) [Floyd94] [RFC3168] passes one bit of congestion information
back to senders when an AQM would normally drop a packet. It is to
be noted that other variants of RED built on AQM, such as Weighted
RED (WRED) and RED with In/Out (RIO) [Clark98] are for quality
enforcement, whereas Stabilized RED (SRED), and CHOKe [Pan00] and its
extensions such as XCHOKe [Chhabra02], are flow policers. In
[Bonald00], authors analytically evaluated RED performance.
These measures do improve performance, but there is a limit to how
much can be accomplished without more information from routers. The
requirement of extreme scalability together with robustness has been
a difficult hurdle for acceleration of this information flow.
Primal-dual TCP/AQM distributed algorithm stability and equilibrium
properties have been extensively studied (cf. [Low02], [Low03.1],
[Low03.2], [Kelly98], and [Kelly05]).
Congestion control includes many new challenges that are becoming
important as the network grows, in addition to the issues that have
been known for many years. These are generally considered to be open
research topics that may require more study or application of
innovative techniques before Internet-scale solutions can be
confidently engineered and deployed. In what follows, an overview of
some of these challenges is given.
2. Global Challenges
This section describes the global challenges to be addressed in the
domain of Internet congestion control.
2.1. Heterogeneity
The Internet encompasses a large variety of heterogeneous IP networks
that are realized by a multitude of technologies, which result in a
tremendous variety of link and path characteristics: capacity can be
either scarce in very-slow-speed radio links (several kbps), or there
may be an abundant supply in high-speed optical links (several
gigabit per second). Concerning latency, scenarios range from local
interconnects (much less than a millisecond) to certain wireless and
satellite links with very large latencies up to or over a second).
Even higher latencies can occur in space communication. As a
consequence, both the available bandwidth and the end-to-end delay in
the Internet may vary over many orders of magnitude, and it is likely
that the range of parameters will further increase in the future.
Additionally, neither the available bandwidth nor the end-to-end
delay is constant. At the IP layer, competing cross-traffic, traffic
management in routers, and dynamic routing can result in sudden
changes in the characteristics of an end-to-end path. Additional
dynamics can be caused by link layer mechanisms, such as shared-media
access (e.g., in wireless networks), changes to new links due to
mobility (horizontal/vertical handovers), topology modifications
(e.g., in ad hoc or meshed networks), link layer error correction,
and dynamic bandwidth provisioning schemes. From this, it follows
that path characteristics can be subject to substantial changes
within short time frames.
Congestion control algorithms have to deal with this variety in an
efficient and stable way. The congestion control principles
introduced by Van Jacobson assume a rather static scenario and
implicitly target configurations where the bandwidth-delay product is
of the order of some dozens of packets at most. While these
principles have proved to work in the Internet for almost two
decades, much larger bandwidth-delay products and increased dynamics
challenge them more and more. There are many situations where
today's congestion control algorithms react in a suboptimal way,
resulting, among other things, in low resource utilization.
This has resulted in a multitude of new proposals for congestion
control algorithms. For instance, since the Additive Increase
Multiplicative Decrease (AIMD) behavior of TCP is too conservative in
practical environments when the congestion window is large, several
high-speed congestion control extensions have been developed.
However, these new algorithms may be less robust or starve legacy
flows in certain situations for which they have not been designed.
At the time of writing, there is no common agreement in the IETF on
which algorithm(s) and protocol(s) to choose.
It is always possible to tune congestion control parameters based on
some knowledge of the environment and the application scenario.
However, the interaction between multiple congestion control
techniques is not yet well understood. The fundamental challenge is
whether it is possible to define one congestion control mechanism
that operates reasonably well in a whole range of scenarios that
exist in the Internet. Hence, important research questions are how
new Internet congestion control mechanisms would have to be designed,
which maximum degree of dynamics they can efficiently handle, and
whether they can keep the generality of the existing end-to-end
solutions.
Some improvements to congestion control could be realized by simple
changes to single functions in end-systems or optimizations of
network components. However, new mechanism(s) might also require a
fundamental redesign of the overall network architecture, and they
may even affect the design of Internet applications. This can imply
significant interoperability and backward compatibility challenges
and/or create network accessibility obstacles. In particular,
networks and/or applications that do not use or support a new
congestion control mechanism could be penalized by a significantly
worse performance compared to what they would get if everybody used
the existing mechanisms (cf. the discussion on fairness in
Section 2.3). [RFC5033] defines several criteria to evaluate the
appropriateness of a new congestion control mechanism. However, a
key issue is how much performance deterioration is acceptable for
"legacy" applications. This tradeoff between performance and cost
has to be very carefully examined for all new congestion control
schemes.
2.2. Stability
Control theory is a mathematical tool for describing dynamic systems.
It lends itself to modeling congestion control -- TCP is a perfect
example of a typical "closed loop" system that can be described in
control theoretic terms. However, control theory has had to be
extended to model the interactions between multiple control loops in
a network [Vinnic02]. In control theory, there is a mathematically
defined notion of system stability. In a stable system, for any
bounded input over any amount of time, the output will also be
bounded. For congestion control, what is actually meant by global
stability is typically asymptotic stability: a mechanism should
converge to a certain state irrespective of the initial state of the
network. Local stability means that if the system is perturbed from
its stable state it will quickly return toward the locally stable
state.
Some fundamental facts known from control theory are useful as
guidelines when designing a congestion control mechanism. For
instance, a controller should only be fed a system state that
reflects its output. A (low-pass) filter function should be used in
order to pass to the controller only states that are expected to last
long enough for its action to be meaningful [Jain88]. Action should
be carried out whenever such feedback arrives, as it is a fundamental
principle of control that the control frequency should ideally be
equal to the feedback frequency. Reacting faster leads to
oscillations and instability, while reacting more slowly makes the
system tardy [Jain90].
Control theoretic modeling of a realistic network can be quite
difficult, especially when taking distinct packet sizes and
heterogeneous round-trip times (RTTs) into account. It has therefore
become common practice to model simpler cases and to leave the more
complicated (realistic) situations for simulations. Clearly, if a
mechanism is not stable in a simple scenario, it is generally
useless; this method therefore helps to eliminate faulty congestion
control candidates at an early stage. However, a mechanism that is
found to be stable in simulations can still not be safely deployed in
real networks, since simulation scenarios make simplifying
assumptions.
TCP stability can be attributed to two key aspects that were
introduced in [Jacobson88]: the AIMD control law during congestion
avoidance, which is based on a simple, vector-based analysis of two
controllers sharing one resource with synchronous RTTs [Chiu89]; and
the "conservation of packets principle", which, once the control has
reached "steady state", tries to maintain an equal amount of packets
in flight at any time by only sending a packet into the network when
a packet has left the network (as indicated by an ACK arriving at the
sender). The latter aspect has guided many decisions regarding
changes that were made to TCP over the years.
The reasoning in [Jacobson88] assumes all senders to be acting at the
same time. The stability of TCP under more realistic network
conditions has been investigated in a large number of ensuing works,
leading to no clear conclusion that TCP would also be asymptotically
stable under arbitrary network conditions. On the other hand,
research has concluded that stability can be assured with constraints
on dynamics that are less stringent than the "conservation of packets
principle". From control theory, only rate increase (not the target
rate) needs to be inversely proportional to RTT (whereas window-based
control converges on a target rate inversely proportional to RTT). A
congestion control mechanism can therefore converge on a rate that is
independent of RTT as long as its dynamics depend on RTT (e.g., FAST
TCP [Jin04]).
In the stability analysis of TCP and of these more modern controls,
the impact of slow-start on stability (which can be significant as
short-lived HTTP flows often never leave this phase) is not entirely
clear.
2.3. Fairness
Recently, the way the Internet community reasons about fairness has
been called deeply into question [Bri07]. Much of the community has
taken fairness to mean approximate equality between the rates of
flows (flow rate fairness) that experience equivalent path congestion
as with TCP [RFC2581] [RFC5681] and TCP-Friendly Rate Control (TFRC)
[RFC5348]. [RFC3714] depicts the resulting situation as "The
Amorphous Problem of Fairness".
A parallel tradition has been built on [Kelly98] where, as long as
each user is accountable for the cost their rate causes to others
[MacK95], the set of rates that everyone chooses is deemed fair (cost
fairness) -- because with any other set of choices people would lose
more value than they gained overall.
In comparison, the debate between max-min, proportional, and TCP
fairness is about mere details. These three all share the assumption
that equal flow rates are desirable; they merely differ in the
second-order issue of how to share out excess capacity in a network
of many bottlenecks. In contrast, cost fairness should lead to
extremely unequal flow rates by design. Equivalently, equal flow
rates would typically be considered extremely unfair.
The two traditional approaches are not protocol options that can each
be followed in different parts of an internetwork. They lead to
research agendas that are different in their respective objectives,
resulting in a different set of open issues.
If we assume TCP-friendliness as a goal with flow rate as the metric,
open issues would be:
- Should flow fairness depend on the packet rate or the bit rate?
- Should the target flow rate depend on RTT (as in TCP) or should
only flow dynamics depend on RTT (e.g., as in FAST TCP [Jin04])?
- How should we estimate whether a particular flow start strategy is
fair, or whether a particular fast recovery strategy after a
reduction in rate due to congestion is fair?
- Should we judge what is reasonably fair if an application needs,
for example, even smoother flows than TFRC, or it needs to burst
occasionally, or with any other application behavior?
- During brief congestion bursts (e.g., due to new flow arrivals),
how should we judge at what point it becomes unfair for some flows
to continue at a smooth rate while others reduce their rate?
- Which mechanism(s) could be used to enforce approximate flow rate
fairness?
- Should we introduce some degree of fairness that takes into
account different users' flow activity over time?
- How should we judge the fairness of applications using a large
number of flows over separate paths (e.g., via an overlay)?
If we assume cost fairness as a goal with congestion-volume as the
metric, open issues would be:
- Can one application's sensitivity to instantaneous congestion
really be protected by longer-term accountability of competing
applications?
- Which protocol mechanism(s) are needed to give accountability for
causing congestion?
- How might we design one or two weighted transport protocols (such
as TCP, UDP, etc.) with the addition of application policy control
over the weight?
- Which policy enforcement might be used by networks, and what are
the interactions between application policy and network policy
enforcement?
- How should we design a new policy enforcement framework that will
appropriately compete with existing flows aiming for rate equality
(e.g., TCP)?
The question of how to reason about fairness is a prerequisite to
agreeing on the research agenda. If the relevant metric is flow
rate, it places constraints at protocol design time, whereas if the
metric is congestion-volume, the constraints move to run-time while
design-time constraints can be relaxed [Bri08]. However, that
question does not require more research in itself; it is merely a
debate that needs to be resolved by studying existing research and by
assessing how bad fairness problems could become if they are not
addressed rigorously, and whether we can rely on trust to maintain
approximate fairness without requiring policing complexity [RFC5290].
The latter points may themselves lead to additional research.
However, it is also accepted that more research will not necessarily
lead to convincing either side to change their opinions. More debate
would be needed. It seems also that if the architecture is built to
support cost fairness, then equal instantaneous cost rates for flows
sharing a bottleneck result in flow-rate fairness; that is, flow-rate
fairness can be seen as a special case of cost fairness. One can be
used to build the other, but not vice-versa.
3. Detailed Challenges
3.1. Challenge 1: Network Support
This challenge is perhaps the most critical to get right. Changes to
the balance of functions between the endpoints and network equipment
could require a change to the per-datagram data plane interface
between the transport and network layers. Network equipment vendors
need to be assured that any new interface is stable enough (on decade
timescales) to build into firmware and hardware, and operating-system
vendors will not use a new interface unless it is likely to be widely
deployed.
Network components can be involved in congestion control in two ways:
first, they can implicitly optimize their functions, such as queue
management and scheduling strategies, in order to support the
operation of end-to-end congestion control. Second, network
components can participate in congestion control via explicit
signaling mechanisms. Explicit signaling mechanisms, whether in-band
or out-of-band, require a communication between network components
and end-systems. Signals realized within or over the IP layer are
only meaningful to network components that process IP packets. This
always includes routers and potentially also middleboxes, but not
pure link layer devices. The following section distinguishes clearly
between the term "network component" and the term "router"; the term
"router" is used whenever the processing of IP packets is explicitly
required. One fundamental challenge of network-supported congestion
control is that typically not all network components along a path are
routers (cf. Section 3.1.3).
The first (optimizing) category of implicit mechanisms can be
implemented in any network component that processes and stores
packets. Various approaches have been proposed and also deployed,
such as different AQM techniques. Even though these implicit
techniques are known to improve network performance during congestion
phases, they are still only partly deployed in the Internet. This
may be due to the fact that finding optimal and robust
parameterizations for these mechanisms is a non-trivial problem.
Indeed, the problem with various AQM schemes is the difficulty in
identifying correct values of the parameters that affect the
performance of the queuing scheme (due to variation in the number of
sources, the capacity, and the feedback delay) [Firoiu00] [Hollot01]
[Zhang03]. Many AQM schemes (RED, REM, BLUE, and PI-Controller, but
also Adaptive Virtual Queue (AVQ)) do not define a systematic rule
for setting their parameters.
The second class of approaches uses explicit signaling. By using
explicit feedback from the network, connection endpoints can obtain
more accurate information about the current network characteristics
on the path. This allows endpoints to make more precise decisions
that can better control congestion.
Explicit feedback techniques fall into three broad categories:
- Explicit congestion feedback: one-bit Explicit Congestion
Notification (ECN) [RFC3168] or proposals for more than one bit
[Xia05];
- Explicit per-datagram rate feedback: the eXplicit Control Protocol
(XCP) [Katabi02] [Falk07], or the Rate Control Protocol (RCP)
[Dukki05];
- Explicit rate feedback: by means of in-band signaling, such as by
Quick-Start [RFC4782], or by means of out-of-band signaling, e.g.,
Congestion Avoidance with Distributed Proportional
Control/Performance Transparency Protocol (CADPC/PTP) [Welzl03].
Explicit router feedback can address some of the inherent
shortcomings of TCP. For instance, XCP was developed to overcome the
inefficiency and instability that TCP suffers from when the per-flow
bandwidth-delay product increases. By decoupling resource
utilization/congestion control from fairness control, XCP achieves
equal bandwidth allocation, high utilization, a small standing queue
size, and near-zero packet drops, with both steady and highly varying
traffic. Importantly, XCP does not maintain any per-flow state in
routers and requires few CPU cycles per packet, hence making it
potentially applicable in high-speed routers. However, XCP is still
subject to research: as [Andrew05] has pointed out, XCP is locally
stable but globally unstable when the maximum RTT of a flow is much
larger than the mean RTT. This instability can be removed by
changing the update strategy for the estimation interval, but this
makes the system vulnerable to erroneous RTT advertisements. The
authors of [Pap02] have shown that when flows with different RTTs are
applied, XCP sometimes discriminates among heterogeneous traffic
flows, even if XCP generally equalizes rates among different flows.
[Low05] provides for a complete characterization of the XCP
equilibrium properties.
Several other explicit router feedback schemes have been developed
with different design objectives. For instance, RCP uses per-packet
feedback similar to XCP. But unlike XCP, RCP focuses on the
reduction of flow completion times [Dukki06], taking an optimistic
approach to flows likely to arrive in the next RTT and tolerating
larger instantaneous queue sizes [Dukki05]. XCP, on the other hand,
gives very poor flow completion times for short flows.
Both implicit and explicit router support should be considered in the
context of the end-to-end argument [Saltzer84], which is one of the
key design principles of the Internet. It suggests that functions
that can be realized both in the end-systems and in the network
should be implemented in the end-systems. This principle ensures
that the network provides a general service and that it remains as
simple as possible (any additional complexity is placed above the IP
layer, i.e., at the edges) so as to ensure evolvability, reliability,
and robustness. Furthermore, the fate-sharing principle ([Clark88],
"Design Philosophy of the DARPA Internet Protocols") mandates that an
end-to-end Internet protocol design should not rely on the
maintenance of any per-flow state (i.e., information about the state
of the end-to-end communication) inside the network and that the
network state (e.g., routing state) maintained by the Internet shall
minimize its interaction with the states maintained at the
endpoints/hosts [RFC1958].
However, as discussed in [Moors02] for instance, congestion control
cannot be realized as a pure end-to-end function only. Congestion is
an inherent network phenomenon and can only be resolved efficiently
by some cooperation of end-systems and the network. Congestion
control in today's Internet protocols follows the end-to-end design
principle insofar as only minimal feedback from the network is used,
e.g., packet loss and delay. The end-systems only decide how to
react and how to avoid congestion. The crux is that on the one hand,
there would be substantial benefit by further assistance from the
network, but, on the other hand, such network support could lead to
duplication of functions, which might even harmfully interact with
end-to-end protocol mechanisms. The different requirements of
applications (cf. the fairness discussion in Section 2.3) call for a
variety of different congestion control approaches, but putting such
per-flow behavior inside the network should be avoided, as such a
design would clearly be at odds with the end-to-end and fate-sharing
design principles.
The end-to-end and fate-sharing principles are generally regarded as
the key ingredients for ensuring a scalable and survivable network
design. In order to ensure that new congestion control mechanisms
are scalable, violating these principles must therefore be avoided.
For instance, protocols like XCP and RCP seem not to require flow
state in the network, but this is only the case if the network trusts
i) the receiver not to lie when feeding back the network's delta to
the requested rate; ii) the source not to lie when declaring its
rate; and iii) the source not to cheat when setting its rate in
response to the feedback [Katabi04].
Solving these problems for non-cooperative environments like the
public Internet requires flow state, at least on a sampled basis.
However, because flows can create new identifiers whenever they want,
sampling does not provide a deterrent -- a flow can simply cheat
until it is discovered and then switch to a whitewashed identifier
[Feldman04], and continue cheating until it is discovered again
([Bri09], S7.3).
However, holding flow state in the network only seems to solve these
policing problems in single autonomous system settings. A
multi-domain system would seem to require a completely different
protocol structure, as the information required for policing is only
seen as packets leave the internetwork, but the networks where
packets enter will also want to police compliance.
Even if a new protocol structure were found, it seems unlikely that
network flow state could be avoided given the network's per-packet
flow rate instructions would need to be compared against variations
in the actual flow rate, which is inherently not a per-packet metric.
These issues have been outstanding ever since integrated services
(IntServ) was identified as unscalable in 1997 [RFC2208]. All
subsequent attempts to involve network elements in limiting flow
rates (XCP, RCP, etc.) will run up against the same open issue if
anyone attempts to standardize them for use on the public Internet.
In general, network support of congestion control raises many issues
that have not been completely solved yet.
3.1.1. Performance and Robustness
Congestion control is subject to some tradeoffs: on the one hand, it
must allow high link utilizations and fair resource sharing, but on
the other hand, the algorithms must also be robust.
Router support can help to improve performance, but it can also
result in additional complexity and more control loops. This
requires a careful design of the algorithms in order to ensure
stability and avoid, e.g., oscillations. A further challenge is the
fact that feedback information may be imprecise. For instance,
severe congestion can delay feedback signals. Also, in-network
measurement of parameters such as RTTs or data rates may contain
estimation errors. Even though there has been significant progress
in providing fundamental theoretical models for such effects,
research has not completely explored the whole problem space yet.
Open questions are:
- How much can network elements theoretically improve performance in
the complete range of communication scenarios that exist in the
Internet without damaging or impacting end-to-end mechanisms
already in place?
- Is it possible to design robust congestion control mechanisms that
offer significant benefits with minimum additional risks, even if
Internet traffic patterns will change in the future?
- What is the minimum support that is needed from the network in
order to achieve significantly better performance than with end-
to-end mechanisms and the current IP header limitations that
provide at most unary ECN signals?
3.1.2. Granularity of Network Component Functions
There are several degrees of freedom concerning the involvement of
network entities, ranging from some few additional functions in
network management procedures on the one end to additional per-packet
processing on the other end of the solution space. Furthermore,
different amounts of state can be kept in routers (no per-flow state,
partial per-flow state, soft state, or hard state). The additional
router processing is a challenge for Internet scalability and could
also increase end-to-end latencies.
Although there are many research proposals that do not require
per-flow state and thus do not cause a large processing overhead,
there are no known full solutions (i.e., including anti-cheating)
that do not require per-flow processing. Also, scalability issues
could be caused, for instance, by synchronization mechanisms for
state information among parallel processing entities, which are,
e.g., used in high-speed router hardware designs.
Open questions are:
- What granularity of router processing can be realized without
affecting Internet scalability?
- How can additional processing efforts be kept to a minimum?
3.1.3. Information Acquisition
In order to support congestion control, network components have to
obtain at least a subset of the following information. Obtaining
that information may result in complex tasks.
1. Capacity of (outgoing) links
Link characteristics depend on the realization of lower protocol
layers. Routers operating at the IP layer do not necessarily know
the link layer network topology and link capacities, and these are
not always constant (e.g., on shared wireless links or bandwidth-
on-demand links). Depending on the network technology, there can
be queues or bottlenecks that are not directly visible at the IP
networking layer.
Difficulties also arise when using IP-in-IP tunnels [RFC2003],
IPsec tunnels [RFC4301], IP encapsulated in the Layer Two
Tunneling Protocol (L2TP) [RFC2661], Generic Routing Encapsulation
(GRE) [RFC1701] [RFC2784], the Point-to-Point Tunneling Protocol
(PPTP) [RFC2637], or Multiprotocol Label Switching (MPLS)
[RFC3031] [RFC3032]. In these cases, link information could be
determined by cross-layer information exchange, but this requires
interfaces capable of processing link layer technology specific
information. An alternative could be online measurements, but
this can cause significant additional network overhead. It is an
open research question as to how much, if any, online traffic
measurement would be acceptable (at run-time). Encapsulation and
decapsulation of explicit congestion information have been
specified for IP-in-IP tunnelling [RFC6040] and for MPLS-in-MPLS
or MPLS-in-IP [RFC5129].
2. Traffic carried over (outgoing) links
Accurate online measurement of data rates is challenging when
traffic is bursty. For instance, measuring a "current link load"
requires defining the right measurement interval / sampling
interval. This is a challenge for proposals that require
knowledge, e.g., about the current link utilization.
3. Internal buffer statistics
Some proposals use buffer statistics such as a virtual queue
length to trigger feedback. However, network components can
include multiple distributed buffer stages that make it difficult
to obtain such metrics.
Open questions are:
- Can and should this information be made available, e.g., by
additional interfaces or protocols?
- Which information is so important to higher-layer controllers that
machine architecture research should focus on designing to
provide it?
3.1.4. Feedback Signaling
Explicit notification mechanisms can be realized either by in-band
signaling (notifications piggybacked along with the data traffic) or
by out-of-band signaling [Sarola07]. The latter case requires
additional protocols and a secure binding between the signals and the
packets they refer to. Out-of-band signaling can be further
subdivided into path-coupled and path-decoupled approaches.
Open questions concerning feedback signaling include:
- At which protocol layer should the feedback signaling occur
(IP/network layer assisted, transport layer assisted, hybrid
solutions, shim layer, intermediate sub-layer, etc.)? Should the
feedback signaling be path-coupled or path-decoupled?
- What is the optimal frequency of feedback (only in case of
congestion events, per RTT, per packet, etc.)?
- What direction should feedback take (from network resource via
receiver to sender, or directly back to sender)?
3.2. Challenge 2: Corruption Loss
It is common for congestion control mechanisms to interpret packet
loss as a sign of congestion. This is appropriate when packets are
dropped in routers because of a queue that overflows, but there are
other possible reasons for packet drops. In particular, in wireless
networks, packets can be dropped because of corruption loss,
rendering the typical reaction of a congestion control mechanism
inappropriate. As a result, non-congestive loss may be more
prevalent in these networks due to corruption loss (when the wireless
link cannot be conditioned to properly control its error rate or due
to transient wireless link interruption in areas of poor coverage).
TCP over wireless and satellite is a topic that has been investigated
for a long time [Krishnan04]. There are some proposals where the
congestion control mechanism would react as if a packet had not been
dropped in the presence of corruption (cf. TCP HACK [Balan01]), but
discussions in the IETF have shown (see, for instance, the discussion
that occurred in April 2003 on the Datagram Congestion Control
Protocol (DCCP) working group list
http://www.ietf.org/mail-archive/web/dccp/current/mail6.html) that
there is no agreement that this type of reaction is appropriate. For
instance, it has been said that congestion can manifest itself as
corruption on shared wireless links, and it is questionable whether a
source that sends packets that are continuously impaired by link
noise should keep sending at a high rate because it has lost the
integrity of the feedback loop.
Generally, two questions must be addressed when designing a
congestion control mechanism that takes corruption loss into account:
1. How is corruption detected?
2. What should be the reaction?
In addition to question 1 above, it may be useful to consider
detecting the reason for corruption, but this has not yet been done
to the best of our knowledge.
Corruption detection can be done using an in-band or out-of-band
signaling mechanism, much in the same way as described for
Challenge 1. Additionally, implicit detection can be considered:
link layers sometimes retransmit erroneous frames, which can cause
the end-to-end delay to increase -- but, from the perspective of a
sender at the transport layer, there are many other possible reasons
for such an effect.
Header checksums provide another implicit detection possibility: if a
checksum only covers all the necessary header fields and this
checksum does not show an error, it is possible for errors to be
found in the payload using a second checksum. Such error detection
is possible with UDP-Lite and DCCP; it was found to work well over a
General Packet Radio Service (GPRS) network in a study [Chester04]
and poorly over a WiFi network in another study [Rossi06] [Welzl08].
Note that while UDP-Lite and DCCP enable the detection of corruption,
the specifications of these protocols do not foresee any specific
reaction to it for the time being.
The idea of having a transport endpoint detecting and accordingly
reacting (or not) to corruption poses a number of interesting
questions regarding cross-layer interactions. As IP is designed to
operate over arbitrary link layers, it is therefore difficult to
design a congestion control mechanism on top of it that appropriately
reacts to corruption -- especially as the specific data link layers
that are in use along an end-to-end path are typically unknown to
entities at the transport layer.
While the IETF has not yet specified how a congestion control
mechanism should react to corruption, proposals exist in the
literature, e.g., [Tickoo05]. For instance, TCP Westwood [Mascolo01]
sets the congestion window equal to the measured bandwidth at the
time of congestion in response to three DupACKs or a timeout. This
measurement is obtained by counting and filtering the ACK rate. This
setting provides a significant goodput improvement in noisy channels
because the "blind" by half window reduction of standard TCP is
avoided, i.e., the window is not reduced by too much.
Open questions concerning corruption loss include:
- How should corruption loss be detected?
- How should a source react when it is known that corruption has
occurred?
- Can an ECN-capable flow infer that loss must be due to corruption
just from lack of explicit congestion notifications around a loss
episode [Tickoo05]? Or could this inference be dangerous, given
the transport does not know whether all queues on the path are
ECN-capable or not?
3.3. Challenge 3: Packet Size
TCP does not take packet size into account when responding to losses
or ECN. Over past years, the performance of TCP congestion avoidance
algorithms has been extensively studied. The well-known "square root
formula" provides an estimation of the performance of the TCP
congestion avoidance algorithm for TCP Reno [RFC2581]. [Padhye98]
enhances the model to account for timeouts, receiver window, and
delayed ACKs.
For the sake of the present discussion, we will assume that the TCP
throughput is expressed using the simplified formula. Using this
formula, the TCP throughput B is proportional to the segment size and
inversely proportional to the RTT and the square root of the drop
probability:
S 1
B ~ C --- -------
RTT sqrt(p)
where
C is a constant
S is the TCP segment size (in bytes)
RTT is the end-to-end round-trip time of the TCP
connection (in seconds)
p is the packet drop probability
Neglecting the fact that the TCP rate linearly depends on it,
choosing the ideal packet size is a tradeoff between high throughput
(the larger a packet, the smaller the relative header overhead) and
low packet latency (the smaller a packet, the shorter the time that
is needed until it is filled with data). Observing that TCP is not
optimal for applications with streaming media (since reliable
in-order delivery and congestion control can cause arbitrarily long
delays), this tradeoff has not usually been considered for TCP
applications. Therefore, the influence of the packet size on the
sending rate has not typically been seen as a significant issue,
given there are still few paths through the Internet that support
packets larger than the 1500 bytes common with Ethernet.
The situation is already different for the Datagram Congestion
Control Protocol (DCCP) [RFC4340], which has been designed to enable
unreliable but congestion-controlled datagram transmission, avoiding
the arbitrary delays associated with TCP. DCCP is intended for
applications such as streaming media that can benefit from control
over the tradeoffs between delay and reliable in-order delivery.
DCCP provides for a choice of modular congestion control mechanisms.
DCCP uses Congestion Control Identifiers (CCIDs) to specify the
congestion control mechanism. Three profiles are currently
specified:
- DCCP Congestion Control ID 2 (CCID 2) [RFC4341]: TCP-like
Congestion Control. CCID 2 sends data using a close approximation
of TCP's congestion control as well as incorporating a variant of
Selective Acknowledgment (SACK) [RFC2018] [RFC3517]. CCID 2 is
suitable for senders that can adapt to the abrupt changes in the
congestion window typical of TCP's AIMD congestion control, and
particularly useful for senders that would like to take advantage
of the available bandwidth in an environment with rapidly changing
conditions.
- DCCP Congestion Control ID 3 (CCID 3) [RFC4342]: TCP-Friendly Rate
Control (TFRC) [RFC5348] is a congestion control mechanism
designed for unicast flows operating in a best-effort Internet
environment. When competing for bandwidth, its window is similar
to TCP flows but has a much lower variation of throughput over
time than TCP, making it more suitable for applications such as
streaming media where a relatively smooth sending rate is of
importance. CCID 3 is appropriate for flows that would prefer to
minimize abrupt changes in the sending rate, including streaming
media applications with small or moderate receiver buffering
before playback.
- DCCP Congestion Control ID 4 (CCID 4) [RFC5622]: TFRC Small
Packets (TFRC-SP) [RFC4828], a variant of the TFRC mechanism, has
been designed for applications that exchange small packets. The
objective of TFRC-SP is to achieve the same bandwidth in bits per
second as a TCP flow using packets of up to 1500 bytes. TFRC-SP
enforces a minimum interval of 10 ms between data packets to
prevent a single flow from sending small packets arbitrarily
frequently. CCID 4 has been designed to be used either by
applications that use a small fixed segment size, or by
applications that change their sending rate by varying the segment
size. Because CCID 4 is intended for applications that use a
fixed small segment size, or that vary their segment size in
response to congestion, the transmit rate derived from the TCP
throughput equation is reduced by a factor that accounts for the
packet header size, as specified in [RFC4828].
The resulting open questions are:
- How does TFRC-SP operate under various network conditions?
- How can congestion control be designed so as to scale with packet
size (dependency of congestion algorithm on packet size)?
Today, many network resources are designed so that packet processing
cannot be overloaded even for incoming loads at the maximum bit rate
of the line. If packet processing can handle sustained load r
[packet per second] and the minimum packet size is h [bit] (i.e.,
frame, packet, and transport headers with no payload), then a line
rate of x [bit per second] will never be able to overload packet
processing as long as x =< r*h.
However, realistic equipment is often designed to only cope with a
near-worst-case workload with a few larger packets in the mix, rather
than the worst-case scenario of all minimum-size packets. In this
case, x = r*(h + e) for some small value of e. Therefore, packet
congestion is not impossible for runs of small packets (e.g., TCP
ACKs or denial-of-service (DoS) attacks with TCP SYNs or small UDP
datagrams). But absent such anomalous workloads, equipment vendors
at the 2008 ICCRG meeting believed that equipment could still be
designed so that any congestion should be due to bit overload and not
packet overload.
This observation raises additional open issues:
- Can bit congestion remain prevalent?
Being able to assume that congestion is generally due to excess
bits and not excess packets is a useful simplifying assumption in
the design of congestion control protocols. Can we rely on this
assumption for the future? An alternative view is that in-network
processing will become commonplace, so that per-packet processing
will as likely be the bottleneck as per-bit transmission [Shin08].
Over the last three decades, performance gains have mainly been
achieved through increased packet rates and not bigger packets.
But if bigger maximum segment sizes do become more prevalent, tiny
segments (e.g., ACKs) will not stop being widely used -- leading
to a widening range of packet sizes.
The open question is thus whether or not packet processing rates
(r) will keep up with growth in transmission rates (x). A
superficial look at Moore's Law-type trends would suggest that
processing (r) will continue to outstrip growth in transmission
(x). But predictions based on actual knowledge of technology
futures would be useful. Another open question is whether there
are likely to be more small packets in the average packet mix. If
the answers to either of these questions predict that packet
congestion could become prevalent, congestion control protocols
will have to be more complicated.
- Confusable causes of loss
There is a considerable body of research on how to distinguish
whether packet drops are due to transmission corruption or to
congestion. But the full list of confusable causes of loss is
longer and includes transmission corruption loss, congestion loss
(bit congestion and packet congestion), and policing loss.
If congestion is due to excess bits, the bit rate should be
reduced. If congestion is due to excess packets, the packet rate
can be reduced without reducing the bit rate -- by using larger
packets. However, if the transport cannot tell which of these
causes led to a specific packet drop, its only safe response is to
reduce the bit rate. This is why the Internet would be more
complicated if packet congestion were prevalent, as reducing the
bit rate normally also reduces the packet rate, while reducing the
packet rate does not necessarily reduce the bit rate.
Given distinguishing between corruption loss and congestion is
already an open issue (Section 3.2), if that problem is ever
solved, a further open issue would be whether to standardize a
solution that distinguishes all the above causes of loss, and not
just two of them.
Nonetheless, even if we find a way for network equipment to
explicitly distinguish which sort of loss has occurred, we will
never be able to assume that such a smart AQM solution is deployed
at every congestible resource throughout the Internet -- at every
higher-layer device like firewalls, proxies, and servers; and at
every lower-layer device like low-end hubs, DSLAMs, Wireless LAN
(WLAN) cards, cellular base-stations, and so on. Thus, transport
protocols will always have to cope with packet drops due to
unpredictable causes, so we should always treat AQM as an
optimization, given it will never be ubiquitous throughout the
public Internet.
- What does a congestion notification on a packet of a certain size
mean?
The open issue here is whether a loss or explicit congestion mark
should be interpreted as a single congestion event irrespective of
the size of the packet lost or marked, or whether the strength of
the congestion notification is weighted by the size of the packet.
This issue is discussed at length in [Bri10], along with other
aspects of packet size and congestion control.
[Bri10] makes the strong recommendation that network equipment
should drop or mark packets with a probability independent of each
specific packet's size, while congestion controls should respond
to dropped or marked packets in proportion to the packet's size.
- Packet size and congestion control protocol design
If the above recommendation is correct -- that the packet size of
a congestion notification should be taken into account when the
transport reads, and not when the network writes, the notification
-- it opens up a significant problem of protocol engineering and
re-engineering. Indeed, TCP does not take packet size into
account when responding to losses or ECN. At present, this is not
a pressing problem because use of 1500 byte data segments is very
prevalent for TCP, and the incidence of alternative maximum
segment sizes is not large. However, we should design the
Internet's protocols so they will scale with packet size. So, an
open issue is whether we should evolve TCP to be sensitive to
packet size, or expect new protocols to take over.
As we continue to standardize new congestion control protocols, we
must then face the issue of how they should account for packet
size. It is still an open research issue to establish whether TCP
was correct in not taking packet size into account. If it is
determined that TCP was wrong in this respect, we should
discourage future protocol designs from following TCP's example.
For example, as explained above, the small-packet variant of TCP-
friendly rate control (TFRC-SP [RFC4828]) is an experimental
protocol that aims to take packet size into account. Whatever
packet size it uses, it ensures that its rate approximately equals
that of a TCP using 1500 byte segments. This raises the further
question of whether TCP with 1500 byte segments will be a suitable
long-term gold standard, or whether we need a more thorough review
of what it means for a congestion control mechanism to scale with
packet size.
3.4. Challenge 4: Flow Startup
The beginning of data transmissions imposes some further, unique
challenges: when a connection to a new destination is established,
the end-systems have hardly any information about the characteristics
of the path in between and the available bandwidth. In this flow
startup situation, there is no obvious choice as to how to start to
send. A similar problem also occurs after relatively long idle
times, since the congestion control state then no longer reflects
current information about the state of the network (flow restart
problem).
Van Jacobson [Jacobson88] suggested using the slow-start mechanism
both for the flow startup and the flow restart, and this is today's
standard solution [RFC2581] [RFC5681]. Per [RFC5681], the slow-start
algorithm is used when the congestion window (cwnd) < slow-start
threshold (ssthresh), whose initial value is set arbitrarily high
(e.g., to the size of the largest possible advertised window) and
reduced in response to congestion. During slow-start, TCP increments
the cwnd by at most Sender Maximum Segment Size (MSS) bytes for each
ACK received that cumulatively acknowledges new data. Slow-start
ends when cwnd exceeds ssthresh or when congestion is observed.
However, the slow-start is not optimal in many situations. First, it
can take quite a long time until a sender can fully utilize the
available bandwidth on a path. Second, the exponential increase may
be too aggressive and cause multiple packet loss if large congestion
windows are reached (slow-start overshooting). Finally, the slow-
start does not ensure that new flows converge quickly to a reasonable
share of resources, particularly when the new flows compete with
long-lived flows and come out of slow-start early (slow-start vs
overshoot tradeoff). This convergence problem may even worsen if
more aggressive congestion control variants are widely used.
The slow-start and its interaction with the congestion avoidance
phase was largely designed by intuition [Jacobson88]. So far, little
theory has been developed to understand the flow startup problem and
its implication on congestion control stability and fairness. There
is also no established methodology to evaluate whether new flow
startup mechanisms are appropriate or not.
As a consequence, it is a non-trivial task to address the
shortcomings of the slow-start algorithm. Several experimental
enhancements have been proposed, such as congestion window validation
[RFC2861] and limited slow-start [RFC3742]. There are also ongoing
research activities, focusing, e.g., on bandwidth estimation
techniques, delay-based congestion control, or rate-pacing
mechanisms. However, any alternative end-to-end flow startup
approach has to cope with the inherent problem that there is no or
only little information about the path at the beginning of a data
transfer. This uncertainty could be reduced by more expressive
feedback signaling (cf. Section 3.1). For instance, a source could
learn the path characteristics faster with the Quick-Start mechanism
[RFC4782]. But even if the source knew exactly what rate it should
aim for, it would still not necessarily be safe to jump straight to
that rate. The end-system still does not know how a change in its
own rate will affect the path, which also might become congested in
less than one RTT. Further research would be useful to understand
the effect of decreasing the uncertainty by explicit feedback
separately from control theoretic stability questions. Furthermore,
flow startup also raises fairness questions. For instance, it is
unclear whether it could be reasonable to use a faster startup when
an end-system detects that a path is currently not congested.
In summary, there are several topics for further research concerning
flow startup:
- Better theoretical understanding of the design and evaluation of
flow startup mechanisms, concerning their impact on congestion
risk, stability, and fairness.
- Evaluating whether it may be appropriate to allow alternative
starting schemes, e.g., to allow higher initial rates under
certain constraints [Chu10]; this also requires refining the
definition of fairness for startup situations.
- Better theoretical models for the effects of decreasing
uncertainty by additional network feedback, particularly if the
path characteristics are very dynamic.
3.5. Challenge 5: Multi-Domain Congestion Control
Transport protocols such as TCP operate over the Internet, which is
divided into autonomous systems. These systems are characterized by
their heterogeneity as IP networks are realized by a multitude of
technologies.
3.5.1. Multi-Domain Transport of Explicit Congestion Notification
Different conditions and their variations lead to correlation effects
between policers that regulate traffic against certain conformance
criteria.
With the advent of techniques allowing for early detection of
congestion, packet loss is no longer the sole metric of congestion.
ECN (Explicit Congestion Notification) marks packets -- set by active
queue management techniques -- to convey congestion information,
trying to prevent packet losses (packet loss and the number of
packets marked gives an indication of the level of congestion).
Using TCP ACKs to feed back that information allows the hosts to
realign their transmission rate and thus encourages them to
efficiently use the network. In IP, ECN uses the two least
significant bits of the (former) IPv4 Type of Service (TOS) octet or
the (former) IPv6 Traffic Class octet [RFC2474] [RFC3260]. Further,
ECN in TCP uses two bits in the TCP header that were previously
defined as reserved [RFC793].
ECN [RFC3168] is an example of a congestion feedback mechanism from
the network toward hosts. The congestion-based feedback scheme,
however, has limitations when applied on an inter-domain basis.
Indeed, Sections 8 and 19 of [RFC3168] detail the implications of two
possible attacks:
1. non-compliance: a network erasing a Congestion Experienced (CE)
codepoint introduced earlier on the path, and
2. subversion: a network changing Not ECN-Capable Transport (Not-ECT)
to ECT.
Both of these problems could allow an attacking network to cause
excess congestion in an upstream network, even if the transports were
behaving correctly. There are to date two possible solutions to the
non-compliance problem (number 1 above): the ECN-nonce [RFC3540] and
the [CONEX] work item inspired by the re-ECN incentive system
[Bri09]. Nevertheless, accidental rather than malicious erasure of
ECN is an issue for IPv6 where the absence of an IPv6 header checksum
implies that corruption of ECN could be more impacting than in the
IPv4 case.
Fragmentation is another issue: the ECN-nonce cannot protect against
misbehaving receivers that conceal marked fragments; thus, some
protection is lost in situations where path MTU discovery is
disabled. Note also that ECN-nonce wouldn't protect against the
subversion issue (number 2 above) because, by definition, a Not-ECT
packet comes from a source without ECN enabled, and therefore without
the ECN-nonce enabled. So, there is still room for improvement on
the ECN mechanism when operating in multi-domain networks.
Operational/deployment experience is nevertheless required to
determine the extent of these problems. The second problem is mainly
related to deployment and usage practices and does not seem to result
in any specific research challenge.
Another controversial solution in a multi-domain environment may be
the TCP rate controller (TRC), a traffic conditioner that regulates
the TCP flow at the ingress node in each domain by controlling packet
drops and delays of the packets in a flow. The outgoing traffic from
a TRC-controlled domain is shaped in such a way that no packets are
dropped at the policer. However, the TRC interferes with the end-to-
end TCP model, and thus it would interfere with past and future
diversity of TCP implementations (violating the end-to-end
principle). In particular, the TRC embeds the flow rate equality
view of fairness in the network, and would prevent evolution to forms
of fairness based on congestion-volume (Section 2.3).
3.5.2. Multi-Domain Exchange of Topology or Explicit Rate Information
Security is a challenge for multi-domain exchange of explicit rate
signals, whether in-band or out-of-band. At domain boundaries,
authentication and authorization issues can arise whenever congestion
control information is exchanged. From this perspective, the
Internet does not so far have any security architecture for this
problem.
The future evolution of Internet inter-domain operation has to show
whether more multi-domain information exchange can be effectively
realized. This is of particular importance for congestion control
schemes that make use of explicit per-datagram rate feedback (e.g.,
RCP or XCP) or explicit rate feedback that uses in-band congestion
signaling (e.g., Quick-Start) or out-of-band signaling (e.g.,
CADPC/PTP). Explicit signaling exchanges at the inter-domain level
that result in local domain triggers are currently absent from the
Internet. From this perspective, security issues resulting from
limited trust between different administrative units result in policy
enforcement that exacerbates the difficulty encountered when explicit
feedback congestion control information is exchanged between domains.
Note that even though authentication mechanisms could be extended for
this purpose (by recognizing that explicit rate schemes such as RCP
or XCP have the same inter-domain security requirements and structure
as IntServ), they suffer from the same scalability problems as
identified in [RFC2208]. Indeed, in-band rate signaling or out-of-
band per-flow traffic specification signaling (like in the Resource
Reservation Protocol (RSVP)) results in similar scalability issues
(see Section 3.1).
Also, many autonomous systems only exchange some limited amount of
information about their internal state (topology hiding principle),
even though having more precise information could be highly
beneficial for congestion control. Indeed, revealing the internal
network structure is highly sensitive in multi-domain network
operations and thus also a concern when it comes to the deployability
of congestion control schemes. For instance, a network-assisted
congestion control scheme with explicit signaling could reveal more
information about the internal network dimensioning than TCP does
today.
3.5.3. Multi-Domain Pseudowires
Extending pseudowires across multiple domains poses specific issues.
Pseudowires (PWs) [RFC3985] may carry non-TCP data flows (e.g., Time-
Division Multiplexing (TDM) traffic or Constant Bit Rate (CBR) ATM
traffic) over a multi-domain IP network. Structure-Agnostic TDM over
Packet (SAToP) [RFC4553], Circuit Emulation Service over Packet
Switched Network (CESoPSN) [RFC5086], and TDM over IP (TDMoIP)
[RFC5087] are not responsive to congestion control as discussed in
[RFC2914] (see also [RFC5033]). The same observation applies to ATM
circuit emulating services (CESs) interconnecting CBR equipment
(e.g., Private Branch Exchanges (PBX)) across a Packet Switched
Network (PSN).
Moreover, it is not possible to simply reduce the flow rate of a TDM
PW or an ATM PW when facing packet loss. Providers can rate-control
corresponding incoming traffic, but they may not be able to detect
that PWs carry TDM or CBR ATM traffic (mechanisms for characterizing
the traffic's temporal properties may not necessarily be supported).
This can be illustrated with the following example.
........... ............
. . .
S1 --- E1 --- . .
. | . .
. === E5 === E7 ---
. | . . |
S2 --- E2 --- . . |
. . . | |
........... . | v
. ----- R --->
........... . | ^
. . . | |
S3 --- E3 --- . . |
. | . . |
. === E6 === E8 ---
. | . .
S4 --- E4 --- . .
. . .
........... ............
\---- P1 ---/ \---------- P2 -----
Sources S1, S2, S3, and S4 are originating TDM over IP traffic. P1
provider edges E1, E2, E3, and E4 are rate-limiting such traffic.
The Service Level Agreement (SLA) of provider P1 with transit
provider P2 is such that the latter assumes a BE traffic pattern and
that the distribution shows the typical properties of common BE
traffic (elastic, non-real time, non-interactive).
The problem arises for transit provider P2 because it is not able to
detect that IP packets are carrying constant-bit-rate service traffic
for which the only useful congestion control mechanism would rely on
implicit or explicit admission control, meaning self-blocking or
enforced blocking, respectively.
Assuming P1 providers are rate-limiting BE traffic, a transit P2
provider router R may be subject to serious congestion as all TDM PWs
cross the same router. TCP-friendly traffic (e.g., each flow within
another PW) would follow TCP's AIMD algorithm of reducing the sending
rate by half, in response to each packet drop. Nevertheless, the PWs
carrying TDM traffic could take all the available capacity while
other more TCP-friendly or generally congestion-responsive traffic
reduced itself to nothing. Note here that the situation may simply
occur because S4 suddenly turns on additional TDM channels.
It is neither possible nor desirable to assume that edge routers will
soon have the ability to detect the responsiveness of the carried
traffic, but it is still important for transit providers to be able
to police a fair, robust, responsive, and efficient congestion
control technique in order to avoid impacting congestion-responsive
Internet traffic. However, we must not require only certain specific
responses to congestion to be embedded within the network, which
would harm evolvability. So designing the corresponding mechanisms
in the data and control planes still requires further investigation.
3.6. Challenge 6: Precedence for Elastic Traffic
Traffic initiated by so-called elastic applications adapts to the
available bandwidth using feedback about the state of the network.
For elastic applications, the transport dynamically adjusts the data
traffic sending rate to different network conditions. Examples
encompass short-lived elastic traffic including HTTP and instant-
messaging traffic, as well as long file transfers with FTP and
applications targeted by [LEDBAT]. In brief, elastic data
applications can show extremely different requirements and traffic
characteristics.
The idea to distinguish several classes of best-effort traffic types
is rather old, since it would be beneficial to address the relative
delay sensitivities of different elastic applications. The notion of
traffic precedence was already introduced in [RFC791], and it was
broadly defined as "An independent measure of the importance of this
datagram". For instance, low-precedence traffic should experience
lower average throughput than higher-precedence traffic. Several
questions arise here: What is the meaning of "relative"? What is the
role of the transport layer?
The preferential treatment of higher-precedence traffic combined with
appropriate congestion control mechanisms is still an open issue that
may, depending on the proposed solution, impact both the host and the
network precedence awareness, and thereby congestion control.
[RFC2990] points out that the interactions between congestion control
and DiffServ [RFC2475] remained unaddressed until recently.
Recently, a study and a potential solution have been proposed that
introduce Guaranteed TFRC (gTFRC) [Lochin06]. gTFRC is an adaptation
of TCP-Friendly Rate Control providing throughput guarantees for
unicast flows over the DiffServ/Assured Forwarding (AF) class. The
purpose of gTFRC is to distinguish the guaranteed part from the best-
effort part of the traffic resulting from AF conditioning. The
proposed congestion control has been specified and tested inside
DCCP/CCID 3 for DiffServ/AF networks [Lochin07] [Jourjon08].
Nevertheless, there is still work to be performed regarding lower-
precedence traffic -- data transfers that are useful, yet not
important enough to warrant significantly impairing other traffic.
Examples of applications that could make use of such traffic are web
caches and web browsers (e.g., for pre-fetching) as well as peer-to-
peer applications. There are proposals for achieving low precedence
on a pure end-to-end basis (e.g., TCP Low Priority (TCP-LP)
[Kuzmanovic03]), and there is a specification for achieving it via
router mechanisms [RFC3662]. It seems, however, that network-based
lower-precedence mechanisms are not yet a common service on the
Internet. Since early 2010, end-to-end mechanisms for lower
precedence, e.g., [Shal10], have become common -- at least when
competing with other traffic as part of its own queues (e.g., in a
home router). But it is less clear whether users will be willing to
make their background traffic yield to other people's foreground
traffic, unless the appropriate incentives are created.
There is an issue over how to reconcile two divergent views of the
relation between traffic class precedence and congestion control.
One view considers that congestion signals (losses or explicit
notifications) in one traffic class are independent of those in
another. The other relates marking of the classes together within
the active queue management (AQM) mechanism [Gibbens02]. In the
independent case, using a higher-precedence class of traffic gives a
higher scheduling precedence and generally lower congestion level.
In the linked case, using a higher-precedence class of traffic still
gives higher scheduling precedence, but results in a higher level of
congestion. This higher congestion level reflects the extra
congestion higher-precedence traffic causes to both classes combined.
The linked case separates scheduling precedence from rate control.
The end-to-end congestion control algorithm can separately choose to
take a higher rate by responding less to the higher level of
congestion. This second approach could become prevalent if weighted
congestion controls were common. However, it is an open issue how
the two approaches might co-exist or how one might evolve into the
other.
3.7. Challenge 7: Misbehaving Senders and Receivers
In the current Internet architecture, congestion control depends on
parties acting against their own interests. It is not in a
receiver's interest to honestly return feedback about congestion on
the path, effectively requesting a slower transfer. It is not in the
sender's interest to reduce its rate in response to congestion if it
can rely on others to do so. Additionally, networks may have
strategic reasons to make other networks appear congested.
Numerous strategies to improve congestion control have already been
identified. The IETF has particularly focused on misbehaving TCP
receivers that could confuse a compliant sender into assigning
excessive network and/or server resources to that receiver (e.g.,
[Savage99], [RFC3540]). But, although such strategies are worryingly
powerful, they do not yet seem common (however, evidence of attack
prevalence is itself a research requirement).
A growing proportion of Internet traffic comes from applications
designed not to use congestion control at all, or worse, applications
that add more forward error correction as they experience more
losses. Some believe the Internet was designed to allow such
freedom, so it can hardly be called misbehavior. But others consider
it misbehavior to abuse this freedom [RFC3714], given one person's
freedom can constrain the freedom of others (congestion represents
this conflict of interests). Indeed, leaving freedom unchecked might
result in congestion collapse in parts of the Internet.
Proportionately, large volumes of unresponsive voice traffic could
represent such a threat, particularly for countries with less
generous provisioning [RFC3714]. Also, Internet video on demand
services that transfer much greater data rates without congestion
control are becoming popular. In general, it is recommended that
such UDP applications use some form of congestion control [RFC5405].
Note that the problem is not just misbehavior driven by a self-
interested desire for more bandwidth. Indeed, congestion control may
be attacked by someone who makes no gain for themselves, other than
the satisfaction of harming others (see Security Considerations in
Section 4).
Open research questions resulting from these considerations are:
- By design, new congestion control protocols need to enable one end
to check the other for protocol compliance. How would such
mechanisms be designed?
- Which congestion control primitives could safely satisfy more
demanding applications (smoother than TFRC, faster than high-speed
TCPs), so that application developers and users do not turn off
congestion control to get the rate they expect and need?
Note also that self-restraint could disappear from the Internet. So,
it may no longer be sufficient to rely on developers/users
voluntarily submitting themselves to congestion control. As a
consequence, mechanisms to enforce fairness (see Sections 2.3, 3.4,
and 3.5) need to have more emphasis within the research agenda.
3.8. Other Challenges
This section provides additional challenges and open research issues
that are not (at this point in time) deemed so significant, or they
are of a different nature compared to the main challenges depicted
so far.
3.8.1. RTT Estimation
Several congestion control schemes have to precisely know the round-
trip time (RTT) of a path. The RTT is a measure of the current delay
on a network. It is defined as the delay between the sending of a
packet and the reception of a corresponding response, if echoed back
immediately by the receiver upon receipt of the packet. This
corresponds to the sum of the one-way delay of the packet and the
(potentially different) one-way delay of the response. Furthermore,
any RTT measurement also includes some additional delay due to the
packet processing in both end-systems.
There are various techniques to measure the RTT: active measurements
inject special probe packets into the network and then measure the
response time, using, e.g., ICMP. In contrast, passive measurements
determine the RTT from ongoing communication processes, without
sending additional packets.
The connection endpoints of transport protocols such as TCP, the
Stream Control Transmission Protocol (SCTP), and DCCP, as well as
several application protocols, keep track of the RTT in order to
dynamically adjust protocol parameters such as the retransmission
timeout (RTO) or the rate-control equation. They can implicitly
measure the RTT on the sender side by observing the time difference
between the sending of data and the arrival of the corresponding
acknowledgments. For TCP, this is the default RTT measurement
procedure; it is used in combination with Karn's algorithm, which
prohibits RTT measurements from retransmitted segments [RFC2988].
Traditionally, TCP implementations take one RTT measurement at a time
(i.e., about once per RTT). As an alternative, the TCP timestamp
option [RFC1323] allows more frequent explicit measurements, since a
sender can safely obtain an RTT sample from every received
acknowledgment. In principle, similar measurement mechanisms are
used by protocols other than TCP.
Sometimes it would be beneficial to know the RTT not only at the
sender, but also at the receiver, e.g., to find the one-way variation
in delay due to one-way congestion. A passive receiver can deduce
some information about the RTT by analyzing the sequence numbers of
received segments. But this method is error-prone and only works if
the sender permanently sends data. Other network entities on the
path can apply similar heuristics in order to approximate the RTT of
a connection, but this mechanism is protocol-specific and requires
per-connection state. In the current Internet, there is no simple
and safe solution to determine the RTT of a connection in network
entities other than the sender. The more fundamental question is to
determine whether it is necessary or not for network elements to
measure or know the RTT.
As outlined earlier in this document, the round-trip time is
typically not a constant value. For a given path, there is a
theoretical minimum value, which is given by the minimum
transmission, processing, and propagation delay on that path.
However, additional variable delays might be caused by congestion,
cross-traffic, shared-media access control schemes, recovery
procedures, or other sub-IP layer mechanisms. Furthermore, a change
of the path (e.g., route flapping, hand-over in mobile networks) can
result in completely different delay characteristics.
Due to this variability, one single measured RTT value is hardly
sufficient to characterize a path. This is why many protocols use
RTT estimators that derive an averaged value and keep track of a
certain history of previous samples. For instance, TCP endpoints
derive a smoothed round-trip time (SRTT) from an exponential weighted
moving average [RFC2988]. Such a low-pass filter ensures that
measurement noise and single outliers do not significantly affect the
estimated RTT. Still, a fundamental drawback of low-pass filters is
that the averaged value reacts more slowly to sudden changes in the
measured RTT. There are various solutions to overcome this effect:
For instance, the standard TCP retransmission timeout calculation
considers not only the SRTT, but also a measure for the variability
of the RTT measurements [RFC2988]. Since this algorithm is not well
suited for frequent RTT measurements with timestamps, certain
implementations modify the weight factors (e.g., [Sarola02]). There
are also proposals for more sophisticated estimators, such as Kalman
filters or estimators that utilize mainly peak values.
However, open questions related to RTT estimation in the Internet
remain:
- Optimal measurement frequency: Currently, there is no theory or
common understanding of the right time scale of RTT measurement.
In particular, the necessity for rather frequent measurements
(e.g., per packet) is not well understood. There is some
empirical evidence that such frequent sampling may not have a
significant benefit [Allman99].
- Filter design: A closely related question is how to design good
filters for the measured samples. The existing algorithms are
known to be robust, but they are far from being perfect. The
fundamental problem is that there is no single set of RTT values
that could characterize the Internet as a whole, i.e., it is hard
to define a design target.
- Default values: RTT estimators can fail in certain scenarios,
e.g., when any feedback is missing. In this case, default values
have to be used. Today, most default values are set to
conservative values that may not be optimal for most Internet
communication. Still, the impact of more aggressive settings is
not well understood.
- Clock granularities: RTT estimation depends on the clock
granularities of the protocol stacks. Even though there is a
trend toward higher-precision timers, limited granularity
(particularly on low-cost devices) may still prevent highly
accurate RTT estimations.
3.8.2. Malfunctioning Devices
There is a long history of malfunctioning devices harming the
deployment of new and potentially beneficial functionality in the
Internet. Sometimes, such devices drop packets or even crash
completely when a certain mechanism is used, causing users to opt for
reliability instead of performance and disable the mechanism, or
operating-system vendors to disable it by default. One well-known
example is ECN, whose deployment was long hindered by malfunctioning
firewalls and is still hindered by malfunctioning home-hubs, but
there are many other examples (e.g., the Window Scaling option of
TCP) [Thaler07].
As new congestion control mechanisms are developed with the intention
of eventually seeing them deployed in the Internet, it would be
useful to collect information about failures caused by devices of
this sort, analyze the reasons for these failures, and determine
whether there are ways for such devices to do what they intend to do
without causing unintended failures. Recommendations for vendors of
these devices could be derived from such an analysis. It would also
be useful to see whether there are ways for failures caused by such
devices to become more visible to endpoints, or to the maintainers of
such devices.
A possible way to reduce such problems in the future would be
guidelines for standards authors to ensure that "forward
compatibility" is considered in all IETF work. That is, the default
behavior of a device should be precisely defined for all possible
values and combinations of protocol fields, and not just the minimum
necessary for the protocol being defined. Then, when previously
unused or reserved fields start to be used by newer devices to comply
with a new standard, older devices encountering unusual fields should
at least behave predictably.
3.8.3. Dependence on RTT
AIMD window algorithms that have the goal of packet conservation end
up converging on a rate that is inversely proportional to RTT.
However, control theoretic approaches to stability have shown that
only the increase in rate (acceleration), and not the target rate,
needs to be inversely proportional to RTT [Jin04].
It is possible to have more aggressive behaviors for some demanding
applications as long as they are part of a mix with less aggressive
transports [Key04]. This beneficial effect of transport type mixing
is probably how the Internet currently manages to remain stable even
in the presence of TCP slow-start, which is more aggressive than the
theory allows for stability. Research giving deeper insight into
these aspects would be very useful.
3.8.4. Congestion Control in Multi-Layered Networks
A network of IP nodes is just as vulnerable to congestion in the
lower layers between IP-capable nodes as it is to congestion on the
IP-capable nodes themselves. If network elements take a greater part
in congestion control (ECN, XCP, RCP, etc. -- see Section 3.1), these
techniques will either need to be deployed at lower layers as well,
or they will need to interwork with lower-layer mechanisms.
[RFC5129] shows how to propagate ECN from lower layers upwards for
the specific case of MPLS, but to the authors' knowledge the layering
problem has not been addressed for explicit rate protocol proposals
such as XCP and RCP. Some issues are straightforward matters of
interoperability (e.g., how exactly to copy fields up the layers)
while others are less obvious (e.g., re-framing issues: if RCP were
deployed in a lower layer, how might multiple small RCP frames, all
with different rates in their headers, be assembled into a larger IP
layer datagram?).
Multi-layer considerations also confound many mechanisms that aim to
discover whether every node on the path supports a new congestion
control protocol. For instance, some proposals maintain a secondary
Time to Live (TTL) field parallel to that in the IP header. Any
nodes that support the new behavior update both TTL fields, whereas
legacy IP nodes will only update the IP TTL field. This allows the
endpoints to check whether all IP nodes on the path support the new
behavior, in which case both TTLs will be equal at the receiver. But
mechanisms like these overlook nodes at lower layers that might not
support the new behavior.
A further related issue is congestion control across overlay networks
of relays [Hilt08] [Noel07] [Shen08].
Section 3.5.3 deals with inelastic multi-domain pseudowires (PWs),
where the identity of the pseudowire itself implies the
characteristics of the traffic crossing the multi-domain PSN
(independently of the actual characteristics of the traffic carried
in the PW). A more complex situation arises when inelastic traffic
is carried as part of a pseudowire (e.g., inelastic traffic over
Ethernet PW over PSN) whose edges do not have the means to
characterize the properties of the traffic encapsulated in the
Ethernet frames. In this case, the problem explained in
Section 3.5.3 is not limited to multi-domain pseudowires but more
generally arises from a "pseudowire carrying inelastic traffic"
(whether over a single- or multi-domain PSN).
The problem becomes even more intricate when the Ethernet PW carries
both inelastic and elastic traffic. Addressing this issue further
supports our observation that a general framework to efficiently deal
with congestion control problems in multi-layer networks without
harming evolvability is absolutely necessary.
3.8.5. Multipath End-to-End Congestion Control and Traffic Engineering
Recent work has shown that multipath endpoint congestion control
[Kelly05] offers considerable benefits in terms of resilience and
resource usage efficiency. The IETF has since initiated a work item
on multipath TCP [MPTCP]. By pooling the resources on all paths,
even nodes not using multiple paths benefit from those that are.
There is considerable further research to do in this area,
particularly to understand interactions with network-operator-
controlled route provisioning and traffic engineering, and indeed
whether multipath congestion control can perform better traffic
engineering than the network itself, given the right incentives
[Arkko09].
3.8.6. ALGs and Middleboxes
An increasing number of application layer gateways (ALGs),
middleboxes, and proxies (see Section 3.6 of [RFC2775]) are deployed
at domain boundaries to verify conformance but also filter traffic
and control flows. One motivation is to prevent information beyond
routing data leaking between autonomous systems. These systems split
up end-to-end TCP connections and disrupt end-to-end congestion
control. Furthermore, transport over encrypted tunnels may not allow
other network entities to participate in congestion control.
Basically, such systems disrupt the primal and dual congestion
control components. In particular, end-to-end congestion control may
be replaced by flow-control backpressure mechanisms on the split
connections. A large variety of ALGs and middleboxes use such
mechanisms to improve the performance of applications (Performance
Enhancing Proxies, Application Accelerators, etc.). However, the
implications of such mechanisms, which are often proprietary and not
documented, have not been studied systematically so far.
There are two levels of interference:
- The "transparent" case, i.e., the endpoint address from the sender
perspective is still visible to the receiver (the destination IP
address). Relay systems that intercept payloads but do not relay
congestion control information provide an example. Such
middleboxes can prevent the operation of end-to-end congestion
control.
- The "non-transparent" case, which causes fewer problems for
congestion control. Although these devices interfere with end-to-
end network transparency, they correctly terminate network,
transport, and application layer protocols on both sides, which
individually can be congestion controlled.
4. Security Considerations
Misbehavior may be driven by pure malice, or malice may in turn be
driven by wider selfish interests, e.g., using distributed denial-of-
service (DDoS) attacks to gain rewards by extortion [RFC4948]. DDoS
attacks are possible both because of vulnerabilities in operating
systems and because the Internet delivers packets without requiring
congestion control.
To date, compliance with congestion control rules and being fair
require endpoints to cooperate. The possibility of uncooperative
behavior can be regarded as a security issue; its implications are
discussed throughout these documents in a scattered fashion.
Currently the focus of the research agenda against denial of service
is about identifying attack-packets that attack machines and the
networks hosting them, with a particular focus on mitigating source
address spoofing. But if mechanisms to enforce congestion control
fairness were robust to both selfishness and malice [Bri06], they
would also naturally mitigate denial of service against the network,
which can be considered (from the perspective of a well-behaved
Internet user) as a congestion control enforcement problem. Even
some denial-of-service attacks on hosts (rather than the network)
could be considered as a congestion control enforcement issue at the
higher layer. But clearly there are also denial-of-service attacks
that would not be solved by enforcing congestion control.
Sections 3.5 and 3.7 on multi-domain issues and misbehaving senders
and receivers also discuss some information security issues suffered
by various congestion control approaches.
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[Low03.2] Low, S., Paganini, F., Wang, J., and J. Doyle, "Linear
stability of TCP/RED and a scalable control", Computer
Networks Journal, Vol. 43, No. 5, pp. 633-647, December
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[Low05] Low, S., Andrew, L., and B. Wydrowski, "Understanding
XCP: equilibrium and fairness", Proceedings of IEEE
INFOCOM'05, Miami (Florida), USA, March 2005.
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Network Resources", IEEE Journal on Selected Areas in
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[Mascolo01] Mascolo, S., Casetti, Cl., Gerla M., Sanadidi, M.Y., and
R. Wang, "TCP Westwood: Bandwidth estimation for enhanced
transport over wireless links", Proceedings of MOBICOM
2001, Rome, Italy, July 2001.
[Moors02] Moors, T., "A critical review of "End-to-end arguments in
system design"", Proceedings of IEEE International
Conference on Communications (ICC) 2002, New York City
(New Jersey), USA, April/May 2002.
[MPTCP] IETF WG Action: Multipath TCP (mptcp).
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Analyze SIP Based VoIP Networks Under Overload",
International Teletraffic Congress (ITC'07), Ottawa,
Canada, June 2007.
[Padhye98] Padhye, J., Firoiu, V., Towsley, D., and J. Kurose,
"Modeling TCP Throughput: A Simple Model and Its
Empirical Validation", University of Massachusetts
(UMass), CMPSCI Tech. Report TR98-008, February 1998.
[Pan00] Pan, R., Prabhakar, B., and K. Psounis, "CHOKe: a
stateless AQM scheme for approximating fair bandwidth
allocation", Proceedings of IEEE INFOCOM'00, Tel Aviv,
Israel, March 2000.
[Pap02] Papadimitriou, I. and G. Mavromatis, "Stability of
Congestion Control Algorithms using Control Theory with
an application to XCP", Technical Report, 2002.
<http://www.stanford.edu/class/ee384y/projects/
reports/ionnis.pdf>.
[RFC791] Postel, J., "Internet Protocol", STD 5, RFC 791,
September 1981.
[RFC793] Postel, J., "Transmission Control Protocol", STD 7,
RFC 793, September 1981.
[RFC1323] Jacobson, V., Braden, R., and D. Borman, "TCP Extensions
for High Performance", RFC 1323, May 1992.
[RFC1701] Hanks, S., Li, T., Farinacci, D., and P. Traina, "Generic
Routing Encapsulation (GRE)", RFC 1701, October 1994.
[RFC1958] Carpenter, B., Ed., "Architectural Principles of the
Internet", RFC 1958, June 1996.
[RFC2003] Perkins, C., "IP Encapsulation within IP", RFC 2003,
October 1996.
[RFC2018] Mathis, M., Mahdavi, J., Floyd, S., and A. Romanow, "TCP
Selective Acknowledgment Options", RFC 2018, October
1996.
[RFC2208] Mankin, A., Ed., Baker, F., Braden, B., Bradner, S.,
O'Dell, M., Romanow, A., Weinrib, A., and L. Zhang,
"Resource ReSerVation Protocol (RSVP) -- Version 1
Applicability Statement Some Guidelines on Deployment",
RFC 2208, September 1997.
[RFC2474] Nichols, K., Blake, S., Baker, F., and D. Black,
"Definition of the Differentiated Services Field (DS
Field) in the IPv4 and IPv6 Headers", RFC 2474, December
1998.
[RFC2475] Blake, S., Black, D., Carlson, M., Davies, E., Wang, Z.,
and W. Weiss, "An Architecture for Differentiated
Service", RFC 2475, December 1998.
[RFC2581] Allman, M., Paxson, V., and W. Stevens, "TCP Congestion
Control", RFC 2581, April 1999.
[RFC2637] Hamzeh, K., Pall, G., Verthein, W., Taarud, J., Little,
W., and G. Zorn, "Point-to-Point Tunneling Protocol
(PPTP)", RFC 2637, July 1999.
[RFC2661] Townsley, W., Valencia, A., Rubens, A., Pall, G., Zorn,
G., and B. Palter, "Layer Two Tunneling Protocol "L2TP"",
RFC 2661, August 1999.
[RFC2775] Carpenter, B., "Internet Transparency", RFC 2775,
February 2000.
[RFC2784] Farinacci, D., Li, T., Hanks, S., Meyer, D., and P.
Traina, "Generic Routing Encapsulation (GRE)", RFC 2784,
March 2000.
[RFC2861] Handley, M., Padhye, J., and S. Floyd, "TCP Congestion
Window Validation", RFC 2861, June 2000.
[RFC2914] Floyd, S., "Congestion Control Principles", BCP 41,
RFC 2914, September 2000.
[RFC2988] Paxson, V. and M. Allman, "Computing TCP's Retransmission
Timer", RFC 2988, November 2000.
[RFC2990] Huston, G., "Next Steps for the IP QoS Architecture",
RFC 2990, November 2000.
[RFC3031] Rosen, E., Viswanathan, A., and R. Callon, "Multiprotocol
Label Switching Architecture", RFC 3031, January 2001.
[RFC3032] Rosen, E., Tappan, D., Fedorkow, G., Rekhter, Y.,
Farinacci, D., Li, T., and A. Conta, "MPLS Label Stack
Encoding", RFC 3032, January 2001.
[RFC3168] Ramakrishnan, K., Floyd, S., and D. Black, "The Addition
of Explicit Congestion Notification (ECN) to IP",
RFC 3168, September 2001.
[RFC3260] Grossman, D., "New Terminology and Clarifications for
Diffserv", RFC 3260, April 2002.
[RFC3517] Blanton, E., Allman, M., Fall, K., and L. Wang, "A
Conservative Selective Acknowledgment (SACK)-based Loss
Recovery Algorithm for TCP", RFC 3517, April 2003.
[RFC3540] Spring, N., Wetherall, D., and D. Ely, "Robust Explicit
Congestion Notification (ECN) Signaling with Nonces",
RFC 3540, June 2003.
[RFC3662] Bless, R., Nichols, K., and K. Wehrle, "A Lower Effort
Per-Domain Behavior (PDB) for Differentiated Services",
RFC 3662, December 2003.
[RFC3714] Floyd, S., Ed., and J. Kempf, Ed., "IAB Concerns
Regarding Congestion Control for Voice Traffic in the
Internet", RFC 3714, March 2004.
[RFC3742] Floyd, S., "Limited Slow-Start for TCP with Large
Congestion Windows", RFC 3742, March 2004.
[RFC3985] Bryant, S., Ed., and P. Pate, Ed., "Pseudo Wire Emulation
Edge-to-Edge (PWE3) Architecture", RFC 3985, March 2005.
[RFC4301] Kent, S. and K. Seo, "Security Architecture for the
Internet Protocol", RFC 4301, December 2005.
[RFC4340] Kohler, E., Handley, M., and S. Floyd, "Datagram
Congestion Control Protocol (DCCP)", RFC 4340, March
2006.
[RFC4341] Floyd, S. and E. Kohler, "Profile for Datagram Congestion
Control Protocol (DCCP) Congestion Control ID 2: TCP-like
Congestion Control", RFC 4341, March 2006.
[RFC4342] Floyd, S., Kohler, E., and J. Padhye, "Profile for
Datagram Congestion Control Protocol (DCCP) Congestion
Control ID 3: TCP-Friendly Rate Control (TFRC)",
RFC 4342, March 2006.
[RFC4553] Vainshtein, A., Ed., and YJ. Stein, Ed., "Structure-
Agnostic Time Division Multiplexing (TDM) over Packet
(SAToP)", RFC 4553, June 2006.
[RFC4614] Duke, M., Braden, R., Eddy, W., and E. Blanton, "A
Roadmap for Transmission Control Protocol (TCP)
Specification Documents", RFC 4614, September 2006.
[RFC4782] Floyd, S., Allman, M., Jain, A., and P. Sarolahti,
"Quick-Start for TCP and IP", RFC 4782, January 2007.
[RFC4828] Floyd, S. and E. Kohler, "TCP Friendly Rate Control
(TFRC): The Small-Packet (SP) Variant", RFC 4828, April
2007.
[RFC4948] Andersson, L., Davies, E., and L. Zhang, "Report from the
IAB workshop on Unwanted Traffic March 9-10, 2006",
RFC 4948, August 2007.
[RFC5033] Floyd, S. and M. Allman, "Specifying New Congestion
Control Algorithms", BCP 133, RFC 5033, August 2007.
[RFC5086] Vainshtein, A., Ed., Sasson, I., Metz, E., Frost, T., and
P. Pate, "Structure-Aware Time Division Multiplexed (TDM)
Circuit Emulation Service over Packet Switched Network
(CESoPSN)", RFC 5086, December 2007.
[RFC5087] Stein, Y(J)., Shashoua, R., Insler, R., and M. Anavi,
"Time Division Multiplexing over IP (TDMoIP)", RFC 5087,
December 2007.
[RFC5129] Davie, B., Briscoe, B., and J. Tay, "Explicit Congestion
Marking in MPLS", RFC 5129, January 2008.
[RFC5290] Floyd, S. and M. Allman, "Comments on the Usefulness of
Simple Best-Effort Traffic", RFC 5290, July 2008.
[RFC5348] Floyd, S., Handley, M., Padhye, J., and J. Widmer, "TCP
Friendly Rate Control (TFRC): Protocol Specification",
RFC 5348, September 2008.
[RFC5405] Eggert, L. and G. Fairhurst, "Unicast UDP Usage
Guidelines for Application Designers", BCP 145, RFC 5405,
November 2008.
[RFC5622] Floyd, S. and E. Kohler, "Profile for Datagram Congestion
Control Protocol (DCCP) Congestion ID 4: TCP-Friendly
Rate Control for Small Packets (TFRC-SP)", RFC 5622,
August 2009.
[RFC5681] Allman, M., Paxson, V., and E. Blanton, "TCP Congestion
Control", RFC 5681 (Obsoletes RFC 2581), September 2009.
[RFC5783] Welzl, M. and W. Eddy, "Congestion Control in the RFC
Series", RFC 5783, February 2010.
[RFC6040] Briscoe, B., "Tunnelling of Explicit Congestion
Notification", RFC 6040, November 2010.
[Rossi06] Rossi, M., "Evaluating TCP with Corruption Notification
in an IEEE 802.11 Wireless LAN", Master Thesis,
University of Innsbruck, November 2006. Available from
http://heim.ifi.uio.no/michawe/research/projects/
corruption/.
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arguments in system design", ACM Transactions on Computer
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Linux TCP", Proceedings of the USENIX Annual Technical
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Considerations for Explicit Cross-layer Indications",
Work in Progress, March 2007.
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Anderson, "TCP Congestion Control with a Misbehaving
Receiver", ACM SIGCOMM Computer Communication Review,
1999.
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Background Transport (LEDBAT)", Work in Progress, October
2010.
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Initiation Protocol (SIP) Server Overload Control: Design
and Evaluation, Principles", Systems and Applications of
IP Telecommunications (IPTComm'08), Heidelberg, Germany,
July 2008.
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for Processing-Constrained Networks", IEEE/ACM
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April 2008.
[Thaler07] Thaler, D., Sridharan, M., and D. Bansal, "Implementation
Report on Experiences with Various TCP RFCs",
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<http://www.ietf.org/proceedings/07mar/
slides/tsvarea-3/>.
[Tickoo05] Tickoo, O., Subramanian, V., Kalyanaraman, S., and K.K.
Ramakrishnan, "LT-TCP: End-to-End Framework to Improve
TCP Performance over Networks with Lossy Channels",
Proceedings of International Workshop on QoS (IWQoS),
Passau, Germany, June 2005.
[TRILOGY] "Trilogy Project", European Commission Seventh Framework
Program (FP7), Grant No: 216372, <http://www.trilogy-
project.org>.
[Vinnic02] Vinnicombe, G., "On the stability of networks operating
TCP-like congestion control," Proceedings of IFAC World
Congress, Barcelona, Spain, 2002.
[Welzl03] Welzl, M., "Scalable Performance Signalling and
Congestion Avoidance", Springer (ISBN 1-4020-7570-7),
September 2003.
[Welzl08] Welzl, M., Rossi, M., Fumagalli, A., and M. Tacca,
"TCP/IP over IEEE 802.11b WLAN: the Challenge of
Harnessing Known-Corrupt Data", Proceedings of IEEE
International Conference on Communications (ICC) 2008,
Beijing, China, May 2008.
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Kalyanaraman, "One more bit is enough", ACM SIGCOMM
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2005.
[Zhang03] Zhang, H., Towsley, D., Hollot, C., and V. Misra, "A
Self-Tuning Structure for Adaptation in TCP/AQM
Networks", Proceedings of ACM SIGMETRICS'03 Conference,
San Diego (California), USA, June 2003.
6. Acknowledgments
The authors would like to thank the following people whose feedback
and comments contributed to this document: Keith Moore, Jan
Vandenabeele, and Larry Dunn (his comments at the Manchester ICCRG
and discussions with him helped with the section on packet-
congestibility).
Dimitri Papadimitriou's contribution was partly funded by [ECODE], a
Seventh Framework Program (FP7) research project sponsored by the
European Commission.
Bob Briscoe's contribution was partly funded by [TRILOGY], a research
project supported by the European Commission.
Michael Scharf is now with Alcatel-Lucent.
7. Contributors
The following additional people have contributed to this document:
- Wesley Eddy <weddy@grc.nasa.gov>
- Bela Berde <bela.berde@gmx.de>
- Paulo Loureiro <loureiro.pjg@gmail.com>
- Chris Christou <christou_chris@bah.com>
Authors' Addresses
Dimitri Papadimitriou (editor)
Alcatel-Lucent
Copernicuslaan, 50
2018 Antwerpen, Belgium
Phone: +32 3 240 8491
EMail: dimitri.papadimitriou@alcatel-lucent.com
Michael Welzl
University of Oslo, Department of Informatics
PO Box 1080 Blindern
N-0316 Oslo, Norway
EMail: michawe@ifi.uio.no
Michael Scharf
University of Stuttgart
Pfaffenwaldring 47
70569 Stuttgart, Germany
EMail: michael.scharf@googlemail.com
Bob Briscoe
BT & UCL
B54/77, Adastral Park
Martlesham Heath
Ipswich IP5 3RE, UK
EMail: bob.briscoe@bt.com