[Docs] [txt|pdf] [draft-ietf-aqm-re...] [Diff1] [Diff2]

BEST CURRENT PRACTICE

Internet Engineering Task Force (IETF)                     F. Baker, Ed.
Request for Comments: 7567                                 Cisco Systems
BCP: 197                                               G. Fairhurst, Ed.
Obsoletes: 2309                                   University of Aberdeen
Category: Best Current Practice                                July 2015
ISSN: 2070-1721


         IETF Recommendations Regarding Active Queue Management

Abstract

   This memo presents recommendations to the Internet community
   concerning measures to improve and preserve Internet performance.  It
   presents a strong recommendation for testing, standardization, and
   widespread deployment of active queue management (AQM) in network
   devices to improve the performance of today's Internet.  It also
   urges a concerted effort of research, measurement, and ultimate
   deployment of AQM mechanisms to protect the Internet from flows that
   are not sufficiently responsive to congestion notification.

   Based on 15 years of experience and new research, this document
   replaces the recommendations of RFC 2309.

Status of This Memo

   This memo documents an Internet Best Current Practice.

   This document is a product of the Internet Engineering Task Force
   (IETF).  It represents the consensus of the IETF community.  It has
   received public review and has been approved for publication by the
   Internet Engineering Steering Group (IESG).  Further information on
   BCPs is available in 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/rfc7567.














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Copyright Notice

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   than English.

























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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   4
     1.1.  Congestion Collapse . . . . . . . . . . . . . . . . . . .   4
     1.2.  Active Queue Management to Manage Latency . . . . . . . .   5
     1.3.  Document Overview . . . . . . . . . . . . . . . . . . . .   6
     1.4.  Changes to the Recommendations of RFC 2309  . . . . . . .   7
     1.5.  Requirements Language . . . . . . . . . . . . . . . . . .   7
   2.  The Need for Active Queue Management  . . . . . . . . . . . .   7
     2.1.  AQM and Multiple Queues . . . . . . . . . . . . . . . . .  11
     2.2.  AQM and Explicit Congestion Marking (ECN) . . . . . . . .  12
     2.3.  AQM and Buffer Size . . . . . . . . . . . . . . . . . . .  12
   3.  Managing Aggressive Flows . . . . . . . . . . . . . . . . . .  13
   4.  Conclusions and Recommendations . . . . . . . . . . . . . . .  16
     4.1.  Operational Deployments SHOULD Use AQM Procedures . . . .  17
     4.2.  Signaling to the Transport Endpoints  . . . . . . . . . .  17
       4.2.1.  AQM and ECN . . . . . . . . . . . . . . . . . . . . .  18
     4.3.  AQM Algorithm Deployment SHOULD NOT Require Operational
           Tuning  . . . . . . . . . . . . . . . . . . . . . . . . .  20
     4.4.  AQM Algorithms SHOULD Respond to Measured Congestion, Not
           Application Profiles  . . . . . . . . . . . . . . . . . .  21
     4.5.  AQM Algorithms SHOULD NOT Be Dependent on Specific
           Transport Protocol Behaviors  . . . . . . . . . . . . . .  22
     4.6.  Interactions with Congestion Control Algorithms . . . . .  22
     4.7.  The Need for Further Research . . . . . . . . . . . . . .  23
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .  25
   6.  Privacy Considerations  . . . . . . . . . . . . . . . . . . .  25
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  25
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .  25
     7.2.  Informative References  . . . . . . . . . . . . . . . . .  26
   Acknowledgements  . . . . . . . . . . . . . . . . . . . . . . . .  31
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  31



















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1.  Introduction

   The Internet protocol architecture is based on a connectionless end-
   to-end packet service using the Internet Protocol, whether IPv4
   [RFC791] or IPv6 [RFC2460].  The advantages of its connectionless
   design -- flexibility and robustness -- have been amply demonstrated.
   However, these advantages are not without cost: careful design is
   required to provide good service under heavy load.  In fact, lack of
   attention to the dynamics of packet forwarding can result in severe
   service degradation or "Internet meltdown".  This phenomenon was
   first observed during the early growth phase of the Internet in the
   mid 1980s [RFC896] [RFC970]; it is technically called "congestion
   collapse" and was a key focus of RFC 2309.

   Although wide-scale congestion collapse is not common in the
   Internet, the presence of localized congestion collapse is by no
   means rare.  It is therefore important to continue to avoid
   congestion collapse.

   Since 1998, when RFC 2309 was written, the Internet has become used
   for a variety of traffic.  In the current Internet, low latency is
   extremely important for many interactive and transaction-based
   applications.  The same type of technology that RFC 2309 advocated
   for combating congestion collapse is also effective at limiting
   delays to reduce the interaction delay (latency) experienced by
   applications [Bri15].  High or unpredictable latency can impact the
   performance of the control loops used by end-to-end protocols
   (including congestion control algorithms using TCP).  There is now
   also a focus on reducing network latency using the same technology.

   The mechanisms described in this document may be implemented in
   network devices on the path between endpoints that include routers,
   switches, and other network middleboxes.  The methods may also be
   implemented in the networking stacks within endpoint devices that
   connect to the network.

1.1.  Congestion Collapse

   The original fix for Internet meltdown was provided by Van Jacobsen.
   Beginning in 1986, Jacobsen developed the congestion avoidance
   mechanisms [Jacobson88] that are now required for implementations of
   the Transport Control Protocol (TCP) [RFC793] [RFC1122].  ([RFC7414]
   provides a roadmap to help identify TCP-related documents.)  These
   mechanisms operate in Internet hosts to cause TCP connections to
   "back off" during congestion.  We say that TCP flows are "responsive"
   to congestion signals (i.e., packets that are dropped or marked with
   explicit congestion notification [RFC3168]).  It is primarily these




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   TCP congestion avoidance algorithms that prevent the congestion
   collapse of today's Internet.  Similar algorithms are specified for
   other non-TCP transports.

   However, that is not the end of the story.  Considerable research has
   been done on Internet dynamics since 1988, and the Internet has
   grown.  It has become clear that the congestion avoidance mechanisms
   [RFC5681], while necessary and powerful, are not sufficient to
   provide good service in all circumstances.  Basically, there is a
   limit to how much control can be accomplished from the edges of the
   network.  Some mechanisms are needed in network devices to complement
   the endpoint congestion avoidance mechanisms.  These mechanisms may
   be implemented in network devices.

1.2.  Active Queue Management to Manage Latency

   Internet latency has become a focus of attention to increase the
   responsiveness of Internet applications and protocols.  One major
   source of delay is the buildup of queues in network devices.
   Queueing occurs whenever the arrival rate of data at the ingress to a
   device exceeds the current egress rate.  Such queueing is normal in a
   packet-switched network and is often necessary to absorb bursts in
   transmission and perform statistical multiplexing of traffic, but
   excessive queueing can lead to unwanted delay, reducing the
   performance of some Internet applications.

   RFC 2309 introduced the concept of "Active Queue Management" (AQM), a
   class of technologies that, by signaling to common congestion-
   controlled transports such as TCP, manages the size of queues that
   build in network buffers.  RFC 2309 also describes a specific AQM
   algorithm, Random Early Detection (RED), and recommends that this be
   widely implemented and used by default in routers.

   With an appropriate set of parameters, RED is an effective algorithm.
   However, dynamically predicting this set of parameters was found to
   be difficult.  As a result, RED has not been enabled by default, and
   its present use in the Internet is limited.  Other AQM algorithms
   have been developed since RFC 2309 was published, some of which are
   self-tuning within a range of applicability.  Hence, while this memo
   continues to recommend the deployment of AQM, it no longer recommends
   that RED or any other specific algorithm is used by default.  It
   instead provides recommendations on IETF processes for the selection
   of appropriate algorithms, and especially that a recommended
   algorithm is able to automate any required tuning for common
   deployment scenarios.






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   Deploying AQM in the network can significantly reduce the latency
   across an Internet path, and, since the writing of RFC 2309, this has
   become a key motivation for using AQM in the Internet.  In the
   context of AQM, it is useful to distinguish between two related
   classes of algorithms: "queue management" versus "scheduling"
   algorithms.  To a rough approximation, queue management algorithms
   manage the length of packet queues by marking or dropping packets
   when necessary or appropriate, while scheduling algorithms determine
   which packet to send next and are used primarily to manage the
   allocation of bandwidth among flows.  While these two mechanisms are
   closely related, they address different performance issues and
   operate on different timescales.  Both may be used in combination.

1.3.  Document Overview

   The discussion in this memo applies to "best-effort" traffic, which
   is to say, traffic generated by applications that accept the
   occasional loss, duplication, or reordering of traffic in flight.  It
   also applies to other traffic, such as real-time traffic that can
   adapt its sending rate to reduce loss and/or delay.  It is most
   effective when the adaption occurs on timescales of a single Round-
   Trip Time (RTT) or a small number of RTTs, for elastic traffic
   [RFC1633].

   Two performance issues are highlighted:

   The first issue is the need for an advanced form of queue management
   that we call "Active Queue Management", AQM.  Section 2 summarizes
   the benefits that active queue management can bring.  A number of AQM
   procedures are described in the literature, with different
   characteristics.  This document does not recommend any of them in
   particular, but it does make recommendations that ideally would
   affect the choice of procedure used in a given implementation.

   The second issue, discussed in Section 4 of this memo, is the
   potential for future congestion collapse of the Internet due to flows
   that are unresponsive, or not sufficiently responsive, to congestion
   indications.  Unfortunately, while scheduling can mitigate some of
   the side effects of sharing a network queue with an unresponsive
   flow, there is currently no consensus solution to controlling the
   congestion caused by such aggressive flows.  Methods such as
   congestion exposure (ConEx) [RFC6789] offer a framework [CONEX] that
   can update network devices to alleviate these effects.  Significant
   research and engineering will be required before any solution will be
   available.  It is imperative that work to mitigate the impact of
   unresponsive flows is energetically pursued to ensure acceptable
   performance and the future stability of the Internet.




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   Section 4 concludes the memo with a set of recommendations to the
   Internet community on the use of AQM and recommendations for defining
   AQM algorithms.

1.4.  Changes to the Recommendations of RFC 2309

   This memo replaces the recommendations in [RFC2309], which resulted
   from past discussions of end-to-end performance, Internet congestion,
   and RED in the End-to-End Research Group of the Internet Research
   Task Force (IRTF).  It results from experience with RED and other
   algorithms, and the AQM discussion within the IETF [AQM-WG].

   Whereas RFC 2309 described AQM in terms of the length of a queue,
   this memo uses AQM to refer to any method that allows network devices
   to control the queue length and/or the mean time that a packet spends
   in a queue.

   This memo also explicitly obsoletes the recommendation that Random
   Early Detection (RED) be used as the default AQM mechanism for the
   Internet.  This is replaced by a detailed set of recommendations for
   selecting an appropriate AQM algorithm.  As in RFC 2309, this memo
   illustrates the need for continued research.  It also clarifies the
   research needed with examples appropriate at the time that this memo
   is published.

1.5.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in [RFC2119].

2.  The Need for Active Queue Management

   Active Queue Management (AQM) is a method that allows network devices
   to control the queue length or the mean time that a packet spends in
   a queue.  Although AQM can be applied across a range of deployment
   environments, the recommendations in this document are for use in the
   general Internet.  It is expected that the principles and guidance
   are also applicable to a wide range of environments, but they may
   require tuning for specific types of links or networks (e.g., to
   accommodate the traffic patterns found in data centers, the
   challenges of wireless infrastructure, or the higher delay
   encountered on satellite Internet links).  The remainder of this
   section identifies the need for AQM and the advantages of deploying
   AQM methods.






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   The traditional technique for managing the queue length in a network
   device is to set a maximum length (in terms of packets) for each
   queue, accept packets for the queue until the maximum length is
   reached, then reject (drop) subsequent incoming packets until the
   queue decreases because a packet from the queue has been transmitted.
   This technique is known as "tail drop", since the packet that arrived
   most recently (i.e., the one on the tail of the queue) is dropped
   when the queue is full.  This method has served the Internet well for
   years, but it has four important drawbacks:

   1.  Full Queues

       The "tail drop" discipline allows queues to maintain a full (or,
       almost full) status for long periods of time, since tail drop
       signals congestion (via a packet drop) only when the queue has
       become full.  It is important to reduce the steady-state queue
       size, and this is perhaps the most important goal for queue
       management.

       The naive assumption might be that there is a simple trade-off
       between delay and throughput, and that the recommendation that
       queues be maintained in a "non-full" state essentially translates
       to a recommendation that low end-to-end delay is more important
       than high throughput.  However, this does not take into account
       the critical role that packet bursts play in Internet
       performance.  For example, even though TCP constrains the
       congestion window of a flow, packets often arrive at network
       devices in bursts [Leland94].  If the queue is full or almost
       full, an arriving burst will cause multiple packets to be dropped
       from the same flow.  Bursts of loss can result in a global
       synchronization of flows throttling back, followed by a sustained
       period of lowered link utilization, reducing overall throughput
       [Flo94] [Zha90].

       The goal of buffering in the network is to absorb data bursts and
       to transmit them during the (hopefully) ensuing bursts of
       silence.  This is essential to permit transmission of bursts of
       data.  Queues that are normally small are preferred in network
       devices, with sufficient queue capacity to absorb the bursts.
       The counterintuitive result is that maintaining queues that are
       normally small can result in higher throughput as well as lower
       end-to-end delay.  In summary, queue limits should not reflect
       the steady-state queues we want to be maintained in the network;
       instead, they should reflect the size of bursts that a network
       device needs to absorb.






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   2.  Lock-Out

       In some situations tail drop allows a single connection or a few
       flows to monopolize the queue space, thereby starving other
       connections, preventing them from getting room in the queue
       [Flo92].

   3.  Mitigating the Impact of Packet Bursts

       A large burst of packets can delay other packets, disrupting the
       control loop (e.g., the pacing of flows by the TCP ACK clock),
       and reducing the performance of flows that share a common
       bottleneck.

   4.  Control Loop Synchronization

       Congestion control, like other end-to-end mechanisms, introduces
       a control loop between hosts.  Sessions that share a common
       network bottleneck can therefore become synchronized, introducing
       periodic disruption (e.g., jitter/loss).  "Lock-out" is often
       also the result of synchronization or other timing effects

   Besides tail drop, two alternative queue management disciplines that
   can be applied when a queue becomes full are "random drop on full" or
   "head drop on full".  When a new packet arrives at a full queue using
   the "random drop on full" discipline, the network device drops a
   randomly selected packet from the queue (this can be an expensive
   operation, since it naively requires an O(N) walk through the packet
   queue).  When a new packet arrives at a full queue using the "head
   drop on full" discipline, the network device drops the packet at the
   front of the queue [Lakshman96].  Both of these solve the lock-out
   problem, but neither solves the full-queues problem described above.

   In general, we know how to solve the full-queues problem for
   "responsive" flows, i.e., those flows that throttle back in response
   to congestion notification.  In the current Internet, dropped packets
   provide a critical mechanism indicating congestion notification to
   hosts.  The solution to the full-queues problem is for network
   devices to drop or ECN-mark packets before a queue becomes full, so
   that hosts can respond to congestion before buffers overflow.  We
   call such a proactive approach AQM.  By dropping or ECN-marking
   packets before buffers overflow, AQM allows network devices to
   control when and how many packets to drop.








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   In summary, an active queue management mechanism can provide the
   following advantages for responsive flows.

   1.  Reduce number of packets dropped in network devices

       Packet bursts are an unavoidable aspect of packet networks
       [Willinger95].  If all the queue space in a network device is
       already committed to "steady-state" traffic or if the buffer
       space is inadequate, then the network device will have no ability
       to buffer bursts.  By keeping the average queue size small, AQM
       will provide greater capacity to absorb naturally occurring
       bursts without dropping packets.

       Furthermore, without AQM, more packets will be dropped when a
       queue does overflow.  This is undesirable for several reasons.
       First, with a shared queue and the "tail drop" discipline, this
       can result in unnecessary global synchronization of flows,
       resulting in lowered average link utilization and, hence, lowered
       network throughput.  Second, unnecessary packet drops represent a
       waste of network capacity on the path before the drop point.

       While AQM can manage queue lengths and reduce end-to-end latency
       even in the absence of end-to-end congestion control, it will be
       able to reduce packet drops only in an environment that continues
       to be dominated by end-to-end congestion control.

   2.  Provide a lower-delay interactive service

       By keeping a small average queue size, AQM will reduce the delays
       experienced by flows.  This is particularly important for
       interactive applications such as short web transfers, POP/IMAP,
       DNS, terminal traffic (Telnet, SSH, Mosh, RDP, etc.), gaming or
       interactive audio-video sessions, whose subjective (and
       objective) performance is better when the end-to-end delay is
       low.

   3.  Avoid lock-out behavior

       AQM can prevent lock-out behavior by ensuring that there will
       almost always be a buffer available for an incoming packet.  For
       the same reason, AQM can prevent a bias against low-capacity, but
       highly bursty, flows.

       Lock-out is undesirable because it constitutes a gross unfairness
       among groups of flows.  However, we stop short of calling this
       benefit "increased fairness", because general fairness among
       flows requires per-flow state, which is not provided by queue
       management.  For example, in a network device using AQM with only



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       FIFO scheduling, two TCP flows may receive very different shares
       of the network capacity simply because they have different RTTs
       [Floyd91], and a flow that does not use congestion control may
       receive more capacity than a flow that does.  AQM can therefore
       be combined with a scheduling mechanism that divides network
       traffic between multiple queues (Section 2.1).

   4.  Reduce the probability of control loop synchronization

       The probability of network control loop synchronization can be
       reduced if network devices introduce randomness in the AQM
       functions that trigger congestion avoidance at the sending host.

2.1.  AQM and Multiple Queues

   A network device may use per-flow or per-class queueing with a
   scheduling algorithm to either prioritize certain applications or
   classes of traffic, limit the rate of transmission, or provide
   isolation between different traffic flows within a common class.  For
   example, a router may maintain per-flow state to achieve general
   fairness by a per-flow scheduling algorithm such as various forms of
   Fair Queueing (FQ) [Dem90] [Sut99], including Weighted Fair Queueing
   (WFQ), Stochastic Fairness Queueing (SFQ) [McK90], Deficit Round
   Robin (DRR) [Shr96] [Nic12], and/or a Class-Based Queue scheduling
   algorithm such as CBQ [Floyd95].  Hierarchical queues may also be
   used, e.g., as a part of a Hierarchical Token Bucket (HTB) or
   Hierarchical Fair Service Curve (HFSC) [Sto97].  These methods are
   also used to realize a range of Quality of Service (QoS) behaviors
   designed to meet the need of traffic classes (e.g., using the
   integrated or differentiated service models).

   AQM is needed even for network devices that use per-flow or per-class
   queueing, because scheduling algorithms by themselves do not control
   the overall queue size or the sizes of individual queues.  AQM
   mechanisms might need to control the overall queue sizes to ensure
   that arriving bursts can be accommodated without dropping packets.
   AQM should also be used to control the queue size for each individual
   flow or class, so that they do not experience unnecessarily high
   delay.  Using a combination of AQM and scheduling between multiple
   queues has been shown to offer good results in experimental use and
   some types of operational use.

   In short, scheduling algorithms and queue management should be seen
   as complementary, not as replacements for each other.







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2.2.  AQM and Explicit Congestion Marking (ECN)

   An AQM method may use Explicit Congestion Notification (ECN)
   [RFC3168] instead of dropping to mark packets under mild or moderate
   congestion.  ECN-marking can allow a network device to signal
   congestion at a point before a transport experiences congestion loss
   or additional queueing delay [ECN-Benefit].  Section 4.2.1 describes
   some of the benefits of using ECN with AQM.

2.3.  AQM and Buffer Size

   It is important to differentiate the choice of buffer size for a
   queue in a switch/router or other network device, and the
   threshold(s) and other parameters that determine how and when an AQM
   algorithm operates.  The optimum buffer size is a function of
   operational requirements and should generally be sized to be
   sufficient to buffer the largest normal traffic burst that is
   expected.  This size depends on the amount and burstiness of traffic
   arriving at the queue and the rate at which traffic leaves the queue.

   One objective of AQM is to minimize the effect of lock-out, where one
   flow prevents other flows from effectively gaining capacity.  This
   need can be illustrated by a simple example of drop-tail queueing
   when a new TCP flow injects packets into a queue that happens to be
   almost full.  A TCP flow's congestion control algorithm [RFC5681]
   increases the flow rate to maximize its effective window.  This
   builds a queue in the network, inducing latency in the flow and other
   flows that share this queue.  Once a drop-tail queue fills, there
   will also be loss.  A new flow, sending its initial burst, has an
   enhanced probability of filling the remaining queue and dropping
   packets.  As a result, the new flow can be prevented from effectively
   sharing the queue for a period of many RTTs.  In contrast, AQM can
   minimize the mean queue depth and therefore reduce the probability
   that competing sessions can materially prevent each other from
   performing well.

   AQM frees a designer from having to limit the buffer space assigned
   to a queue to achieve acceptable performance, allowing allocation of
   sufficient buffering to satisfy the needs of the particular traffic
   pattern.  Different types of traffic and deployment scenarios will
   lead to different requirements.  The choice of AQM algorithm and
   associated parameters is therefore a function of the way in which
   congestion is experienced and the required reaction to achieve
   acceptable performance.  The latter is the primary topic of the
   following sections.






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3.  Managing Aggressive Flows

   One of the keys to the success of the Internet has been the
   congestion avoidance mechanisms of TCP.  Because TCP "backs off"
   during congestion, a large number of TCP connections can share a
   single, congested link in such a way that link bandwidth is shared
   reasonably equitably among similarly situated flows.  The equitable
   sharing of bandwidth among flows depends on all flows running
   compatible congestion avoidance algorithms, i.e., methods conformant
   with the current TCP specification [RFC5681].

   In this document, a flow is known as "TCP-friendly" when it has a
   congestion response that approximates the average response expected
   of a TCP flow.  One example method of a TCP-friendly scheme is the
   TCP-Friendly Rate Control algorithm [RFC5348].  In this document, the
   term is used more generally to describe this and other algorithms
   that meet these goals.

   There are a variety of types of network flow.  Some convenient
   classes that describe flows are: (1) TCP-friendly flows, (2)
   unresponsive flows, i.e., flows that do not slow down when congestion
   occurs, and (3) flows that are responsive but are less responsive to
   congestion than TCP.  The last two classes contain more aggressive
   flows that can pose significant threats to Internet performance.

   1.  TCP-friendly flows

       A TCP-friendly flow responds to congestion notification within a
       small number of path RTTs, and in steady-state it uses no more
       capacity than a conformant TCP running under comparable
       conditions (drop rate, RTT, packet size, etc.).  This is
       described in the remainder of the document.

   2.  Non-responsive flows

       A non-responsive flow does not adjust its rate in response to
       congestion notification within a small number of path RTTs; it
       can also use more capacity than a conformant TCP running under
       comparable conditions.  There is a growing set of applications
       whose congestion avoidance algorithms are inadequate or
       nonexistent (i.e., a flow that does not throttle its sending rate
       when it experiences congestion).

       The User Datagram Protocol (UDP) [RFC768] provides a minimal,
       best-effort transport to applications and upper-layer protocols
       (both simply called "applications" in the remainder of this
       document) and does not itself provide mechanisms to prevent
       congestion collapse or establish a degree of fairness [RFC5405].



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       Examples that use UDP include some streaming applications for
       packet voice and video, and some multicast bulk data transport.
       Other traffic, when aggregated, may also become unresponsive to
       congestion notification.  If no action is taken, such
       unresponsive flows could lead to a new congestion collapse
       [RFC2914].  Some applications can even increase their traffic
       volume in response to congestion (e.g., by adding Forward Error
       Correction when loss is experienced), with the possibility that
       they contribute to congestion collapse.

       In general, applications need to incorporate effective congestion
       avoidance mechanisms [RFC5405].  Research continues to be needed
       to identify and develop ways to accomplish congestion avoidance
       for presently unresponsive applications.  Network devices need to
       be able to protect themselves against unresponsive flows, and
       mechanisms to accomplish this must be developed and deployed.
       Deployment of such mechanisms would provide an incentive for all
       applications to become responsive by either using a congestion-
       controlled transport (e.g., TCP, SCTP [RFC4960], and DCCP
       [RFC4340]) or incorporating their own congestion control in the
       application [RFC5405] [RFC6679].

   3.  Transport flows that are less responsive than TCP

       A second threat is posed by transport protocol implementations
       that are responsive to congestion, but, either deliberately or
       through faulty implementation, reduce the effective window less
       than a TCP flow would have done in response to congestion.  This
       covers a spectrum of behaviors between (1) and (2).  If
       applications are not sufficiently responsive to congestion
       signals, they may gain an unfair share of the available network
       capacity.

       For example, the popularity of the Internet has caused a
       proliferation in the number of TCP implementations.  Some of
       these may fail to implement the TCP congestion avoidance
       mechanisms correctly because of poor implementation.  Others may
       deliberately be implemented with congestion avoidance algorithms
       that are more aggressive in their use of capacity than other TCP
       implementations; this would allow a vendor to claim to have a
       "faster TCP".  The logical consequence of such implementations
       would be a spiral of increasingly aggressive TCP implementations,
       leading back to the point where there is effectively no
       congestion avoidance and the Internet is chronically congested.

       Another example could be an RTP/UDP video flow that uses an
       adaptive codec, but responds incompletely to indications of
       congestion or responds over an excessively long time period.



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       Such flows are unlikely to be responsive to congestion signals in
       a time frame comparable to a small number of end-to-end
       transmission delays.  However, over a longer timescale, perhaps
       seconds in duration, they could moderate their speed, or increase
       their speed if they determine capacity to be available.

       Tunneled traffic aggregates carrying multiple (short) TCP flows
       can be more aggressive than standard bulk TCP.  Applications
       (e.g., web browsers primarily supporting HTTP 1.1 and peer-to-
       peer file-sharing) have exploited this by opening multiple
       connections to the same endpoint.

       Lastly, some applications (e.g., web browsers primarily
       supporting HTTP 1.1) open a large numbers of successive short TCP
       flows for a single session.  This can lead to each individual
       flow spending the majority of time in the exponential TCP slow
       start phase, rather than in TCP congestion avoidance.  The
       resulting traffic aggregate can therefore be much less responsive
       than a single standard TCP flow.

   The projected increase in the fraction of total Internet traffic for
   more aggressive flows in classes 2 and 3 could pose a threat to the
   performance of the future Internet.  There is therefore an urgent
   need for measurements of current conditions and for further research
   into the ways of managing such flows.  This raises many difficult
   issues in finding methods with an acceptable overhead cost that can
   identify and isolate unresponsive flows or flows that are less
   responsive than TCP.  Finally, there is as yet little measurement or
   simulation evidence available about the rate at which these threats
   are likely to be realized or about the expected benefit of algorithms
   for managing such flows.

   Another topic requiring consideration is the appropriate granularity
   of a "flow" when considering a queue management method.  There are a
   few "natural" answers: 1) a transport (e.g., TCP or UDP) flow (source
   address/port, destination address/port, protocol); 2) Differentiated
   Services Code Point, DSCP; 3) a source/destination host pair (IP
   address); 4) a given source host or a given destination host, or
   various combinations of the above; 5) a subscriber or site receiving
   the Internet service (enterprise or residential).

   The source/destination host pair gives an appropriate granularity in
   many circumstances.  However, different vendors/providers use
   different granularities for defining a flow (as a way of
   "distinguishing" themselves from one another), and different
   granularities may be chosen for different places in the network.  It
   may be the case that the granularity is less important than the fact
   that a network device needs to be able to deal with more unresponsive



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   flows at *some* granularity.  The granularity of flows for congestion
   management is, at least in part, a question of policy that needs to
   be addressed in the wider IETF community.

4.  Conclusions and Recommendations

   The IRTF, in producing [RFC2309], and the IETF in subsequent
   discussion, have developed a set of specific recommendations
   regarding the implementation and operational use of AQM procedures.
   The recommendations provided by this document are summarized as:

   1.  Network devices SHOULD implement some AQM mechanism to manage
       queue lengths, reduce end-to-end latency, and avoid lock-out
       phenomena within the Internet.

   2.  Deployed AQM algorithms SHOULD support Explicit Congestion
       Notification (ECN) as well as loss to signal congestion to
       endpoints.

   3.  AQM algorithms SHOULD NOT require tuning of initial or
       configuration parameters in common use cases.

   4.  AQM algorithms SHOULD respond to measured congestion, not
       application profiles.

   5.  AQM algorithms SHOULD NOT interpret specific transport protocol
       behaviors.

   6.  Congestion control algorithms for transport protocols SHOULD
       maximize their use of available capacity (when there is data to
       send) without incurring undue loss or undue round-trip delay.

   7.  Research, engineering, and measurement efforts are needed
       regarding the design of mechanisms to deal with flows that are
       unresponsive to congestion notification or are responsive, but
       are more aggressive than present TCP.

   These recommendations are expressed using the word "SHOULD".  This is
   in recognition that there may be use cases that have not been
   envisaged in this document in which the recommendation does not
   apply.  Therefore, care should be taken in concluding that one's use
   case falls in that category; during the life of the Internet, such
   use cases have been rarely, if ever, observed and reported.  To the
   contrary, available research [Choi04] says that even high-speed links
   in network cores that are normally very stable in depth and behavior
   experience occasional issues that need moderation.  The
   recommendations are detailed in the following sections.




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4.1.  Operational Deployments SHOULD Use AQM Procedures

   AQM procedures are designed to minimize the delay and buffer
   exhaustion induced in the network by queues that have filled as a
   result of host behavior.  Marking and loss behaviors provide a signal
   that buffers within network devices are becoming unnecessarily full
   and that the sender would do well to moderate its behavior.

   The use of scheduling mechanisms, such as priority queueing, classful
   queueing, and fair queueing, is often effective in networks to help a
   network serve the needs of a range of applications.  Network
   operators can use these methods to manage traffic passing a choke
   point.  This is discussed in [RFC2474] and [RFC2475].  When
   scheduling is used, AQM should be applied across the classes or flows
   as well as within each class or flow:

   o  AQM mechanisms need to control the overall queue sizes to ensure
      that arriving bursts can be accommodated without dropping packets.

   o  AQM mechanisms need to allow combination with other mechanisms,
      such as scheduling, to allow implementation of policies for
      providing fairness between different flows.

   o  AQM should be used to control the queue size for each individual
      flow or class, so that they do not experience unnecessarily high
      delay.

4.2.  Signaling to the Transport Endpoints

   There are a number of ways a network device may signal to the
   endpoint that the network is becoming congested and trigger a
   reduction in rate.  The signaling methods include:

   o  Delaying transport segments (packets) in flight, such as in a
      queue.

   o  Dropping transport segments (packets) in transit.

   o  Marking transport segments (packets), such as using Explicit
      Congestion Control [RFC3168] [RFC4301] [RFC4774] [RFC6040]
      [RFC6679].

   Increased network latency is used as an implicit signal of
   congestion.  For example, in TCP, additional delay can affect ACK
   clocking and has the result of reducing the rate of transmission of
   new data.  In the Real-time Transport Protocol (RTP), network latency
   impacts the RTCP-reported RTT, and increased latency can trigger a
   sender to adjust its rate.  Methods such as Low Extra Delay



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   Background Transport (LEDBAT) [RFC6817] assume increased latency as a
   primary signal of congestion.  Appropriate use of delay-based methods
   and the implications of AQM presently remain an area for further
   research.

   It is essential that all Internet hosts respond to loss [RFC5681]
   [RFC5405] [RFC4960] [RFC4340].  Packet dropping by network devices
   that are under load has two effects: It protects the network, which
   is the primary reason that network devices drop packets.  The
   detection of loss also provides a signal to a reliable transport
   (e.g., TCP, SCTP) that there is incipient congestion, using a
   pragmatic but ambiguous heuristic.  Whereas, when the network
   discards a message in flight, the loss may imply the presence of
   faulty equipment or media in a path, or it may imply the presence of
   congestion.  To be conservative, a transport must assume it may be
   the latter.  Applications using unreliable transports (e.g., using
   UDP) need to similarly react to loss [RFC5405].

   Network devices SHOULD use an AQM algorithm to measure local
   congestion and to determine the packets to mark or drop so that the
   congestion is managed.

   In general, dropping multiple packets from the same sessions in the
   same RTT is ineffective and can reduce throughput.  Also, dropping or
   marking packets from multiple sessions simultaneously can have the
   effect of synchronizing them, resulting in increasing peaks and
   troughs in the subsequent traffic load.  Hence, AQM algorithms SHOULD
   randomize dropping in time, to reduce the probability that congestion
   indications are only experienced by a small proportion of the active
   flows.

   Loss due to dropping also has an effect on the efficiency of a flow
   and can significantly impact some classes of application.  In
   reliable transports, the dropped data must be subsequently
   retransmitted.  While other applications/transports may adapt to the
   absence of lost data, this still implies inefficient use of available
   capacity, and the dropped traffic can affect other flows.  Hence,
   congestion signaling by loss is not entirely positive; it is a
   necessary evil.

4.2.1.  AQM and ECN

   Explicit Congestion Notification (ECN) [RFC4301] [RFC4774] [RFC6040]
   [RFC6679] is a network-layer function that allows a transport to
   receive network congestion information from a network device without
   incurring the unintended consequences of loss.  ECN includes both





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   transport mechanisms and functions implemented in network devices;
   the latter rely upon using AQM to decide when and whether to ECN-
   mark.

   Congestion for ECN-capable transports is signaled by a network device
   setting the "Congestion Experienced (CE)" codepoint in the IP header.
   This codepoint is noted by the remote receiving endpoint and signaled
   back to the sender using a transport protocol mechanism, allowing the
   sender to trigger timely congestion control.  The decision to set the
   CE codepoint requires an AQM algorithm configured with a threshold.
   Non-ECN capable flows (the default) are dropped under congestion.

   Network devices SHOULD use an AQM algorithm that marks ECN-capable
   traffic when making decisions about the response to congestion.
   Network devices need to implement this method by marking ECN-capable
   traffic or by dropping non-ECN-capable traffic.

   Safe deployment of ECN requires that network devices drop excessive
   traffic, even when marked as originating from an ECN-capable
   transport.  This is a necessary safety precaution because:

   1.  A non-conformant, broken, or malicious receiver could conceal an
       ECN mark and not report this to the sender;

   2.  A non-conformant, broken, or malicious sender could ignore a
       reported ECN mark, as it could ignore a loss without using ECN;

   3.  A malfunctioning or non-conforming network device may "hide" an
       ECN mark (or fail to correctly set the ECN codepoint at an egress
       of a network tunnel).

   In normal operation, such cases should be very uncommon; however,
   overload protection is desirable to protect traffic from
   misconfigured or malicious use of ECN (e.g., a denial-of-service
   attack that generates ECN-capable traffic that is unresponsive to CE-
   marking).

   When ECN is added to a scheme, the ECN support MAY define a separate
   set of parameters from those used for controlling packet drop.  The
   AQM algorithm SHOULD still auto-tune these ECN-specific parameters.
   These parameters SHOULD also be manually configurable.

   Network devices SHOULD use an algorithm to drop excessive traffic
   (e.g., at some level above the threshold for CE-marking), even when
   the packets are marked as originating from an ECN-capable transport.






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4.3.  AQM Algorithm Deployment SHOULD NOT Require Operational Tuning

   A number of AQM algorithms have been proposed.  Many require some
   form of tuning or setting of parameters for initial network
   conditions.  This can make these algorithms difficult to use in
   operational networks.

   AQM algorithms need to consider both "initial conditions" and
   "operational conditions".  The former includes values that exist
   before any experience is gathered about the use of the algorithm,
   such as the configured speed of interface, support for full-duplex
   communication, interface MTU, and other properties of the link.
   Other properties include information observed from monitoring the
   size of the queue, the queueing delay experienced, rate of packet
   discard, etc.

   This document therefore specifies that AQM algorithms that are
   proposed for deployment in the Internet have the following
   properties:

   o  AQM algorithm deployment SHOULD NOT require tuning.  An algorithm
      MUST provide a default behavior that auto-tunes to a reasonable
      performance for typical network operational conditions.  This is
      expected to ease deployment and operation.  Initial conditions,
      such as the interface rate and MTU size or other values derived
      from these, MAY be required by an AQM algorithm.

   o  AQM algorithm deployment MAY support further manual tuning that
      could improve performance in a specific deployed network.
      Algorithms that lack such variables are acceptable, but, if such
      variables exist, they SHOULD be externalized (made visible to the
      operator).  The specification should identify any cases in which
      auto-tuning is unlikely to achieve acceptable performance and give
      guidance on the parametric adjustments necessary.  For example,
      the expected response of an algorithm may need to be configured to
      accommodate the largest expected Path RTT, since this value cannot
      be known at initialization.  This guidance is expected to enable
      the algorithm to be deployed in networks that have specific
      characteristics (paths with variable or larger delay, networks
      where capacity is impacted by interactions with lower-layer
      mechanisms, etc).










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   o  AQM algorithm deployment MAY provide logging and alarm signals to
      assist in identifying if an algorithm using manual or auto-tuning
      is functioning as expected.  (For example, this could be based on
      an internal consistency check between input, output, and mark/drop
      rates over time.)  This is expected to encourage deployment by
      default and allow operators to identify potential interactions
      with other network functions.

   Hence, self-tuning algorithms are to be preferred.  Algorithms
   recommended for general Internet deployment by the IETF need to be
   designed so that they do not require operational (especially manual)
   configuration or tuning.

4.4.  AQM Algorithms SHOULD Respond to Measured Congestion, Not
      Application Profiles

   Not all applications transmit packets of the same size.  Although
   applications may be characterized by particular profiles of packet
   size, this should not be used as the basis for AQM (see Section 4.5).
   Other methods exist, e.g., Differentiated Services queueing, Pre-
   Congestion Notification (PCN) [RFC5559], that can be used to
   differentiate and police classes of application.  Network devices may
   combine AQM with these traffic classification mechanisms and perform
   AQM only on specific queues within a network device.

   An AQM algorithm should not deliberately try to prejudice the size of
   packet that performs best (i.e., preferentially drop/mark based only
   on packet size).  Procedures for selecting packets to drop/mark
   SHOULD observe the actual or projected time that a packet is in a
   queue (bytes at a rate being an analog to time).  When an AQM
   algorithm decides whether to drop (or mark) a packet, it is
   RECOMMENDED that the size of the particular packet not be taken into
   account [RFC7141].

   Applications (or transports) generally know the packet size that they
   are using and can hence make their judgments about whether to use
   small or large packets based on the data they wish to send and the
   expected impact on the delay, throughput, or other performance
   parameter.  When a transport or application responds to a dropped or
   marked packet, the size of the rate reduction should be proportionate
   to the size of the packet that was sent [RFC7141].

   An AQM-enabled system MAY instantiate different instances of an AQM
   algorithm to be applied within the same traffic class.  Traffic
   classes may be differentiated based on an Access Control List (ACL),
   the packet DSCP [RFC5559], enabling use of the ECN field (i.e., any
   of ECT(0), ECT(1) or CE) [RFC3168] [RFC4774], a multi-field (MF)
   classifier that combines the values of a set of protocol fields



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   (e.g., IP address, transport, ports), or an equivalent codepoint at a
   lower layer.  This recommendation goes beyond what is defined in RFC
   3168 by allowing that an implementation MAY use more than one
   instance of an AQM algorithm to handle both ECN-capable and non-ECN-
   capable packets.

4.5.  AQM Algorithms SHOULD NOT Be Dependent on Specific Transport
      Protocol Behaviors

   In deploying AQM, network devices need to support a range of Internet
   traffic and SHOULD NOT make implicit assumptions about the
   characteristics desired by the set of transports/applications the
   network supports.  That is, AQM methods should be opaque to the
   choice of transport and application.

   AQM algorithms are often evaluated by considering TCP [RFC793] with a
   limited number of applications.  Although TCP is the predominant
   transport in the Internet today, this no longer represents a
   sufficient selection of traffic for verification.  There is
   significant use of UDP [RFC768] in voice and video services, and some
   applications find utility in SCTP [RFC4960] and DCCP [RFC4340].
   Hence, AQM algorithms should demonstrate operation with transports
   other than TCP and need to consider a variety of applications.  When
   selecting AQM algorithms, the use of tunnel encapsulations that may
   carry traffic aggregates needs to be considered.

   AQM algorithms SHOULD NOT target or derive implicit assumptions about
   the characteristics desired by specific transports/applications.
   Transports and applications need to respond to the congestion signals
   provided by AQM (i.e., dropping or ECN-marking) in a timely manner
   (within a few RTTs at the latest).

4.6.  Interactions with Congestion Control Algorithms

   Applications and transports need to react to received implicit or
   explicit signals that indicate the presence of congestion.  This
   section identifies issues that can impact the design of transport
   protocols when using paths that use AQM.

   Transport protocols and applications need timely signals of
   congestion.  The time taken to detect and respond to congestion is
   increased when network devices queue packets in buffers.  It can be
   difficult to detect tail losses at a higher layer, and this may
   sometimes require transport timers or probe packets to detect and
   respond to such loss.  Loss patterns may also impact timely
   detection, e.g., the time may be reduced when network devices do not
   drop long runs of packets from the same flow.




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   A common objective of an elastic transport congestion control
   protocol is to allow an application to deliver the maximum rate of
   data without inducing excessive delays when packets are queued in
   buffers within the network.  To achieve this, a transport should try
   to operate at rate below the inflection point of the load/delay curve
   (the bend of what is sometimes called a "hockey stick" curve)
   [Jain94].  When the congestion window allows the load to approach
   this bend, the end-to-end delay starts to rise -- a result of
   congestion, as packets probabilistically arrive at non-overlapping
   times.  On the one hand, a transport that operates above this point
   can experience congestion loss and could also trigger operator
   activities, such as those discussed in [RFC6057].  On the other hand,
   a flow may achieve both near-maximum throughput and low latency when
   it operates close to this knee point, with minimal contribution to
   router congestion.  Choice of an appropriate rate/congestion window
   can therefore significantly impact the loss and delay experienced by
   a flow and will impact other flows that share a common network queue.

   Some applications may send data at a lower rate or keep less segments
   outstanding at any given time.  Examples include multimedia codecs
   that stream at some natural rate (or set of rates) or an application
   that is naturally interactive (e.g., some web applications,
   interactive server-based gaming, transaction-based protocols).  Such
   applications may have different objectives.  They may not wish to
   maximize throughput, but may desire a lower loss rate or bounded
   delay.

   The correct operation of an AQM-enabled network device MUST NOT rely
   upon specific transport responses to congestion signals.

4.7.  The Need for Further Research

   The second recommendation of [RFC2309] called for further research
   into the interaction between network queues and host applications,
   and the means of signaling between them.  This research has occurred,
   and we as a community have learned a lot.  However, we are not done.

   We have learned that the problems of congestion, latency, and buffer-
   sizing have not gone away and are becoming more important to many
   users.  A number of self-tuning AQM algorithms have been found that
   offer significant advantages for deployed networks.  There is also
   renewed interest in deploying AQM and the potential of ECN.

   Traffic patterns can depend on the network deployment scenario, and
   Internet research therefore needs to consider the implications of a
   diverse range of application interactions.  This includes ensuring





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   that combinations of mechanisms, as well as combinations of traffic
   patterns, do not interact and result in either significantly reduced
   flow throughput or significantly increased latency.

   At the time of writing (in 2015), an obvious example of further
   research is the need to consider the many-to-one communication
   patterns found in data centers, known as incast [Ren12], (e.g.,
   produced by Map/Reduce applications).  Such analysis needs to study
   not only each application traffic type but also combinations of types
   of traffic.

   Research also needs to consider the need to extend our taxonomy of
   transport sessions to include not only "mice" and "elephants", but
   "lemmings".  Here, "lemmings" are flash crowds of "mice" that the
   network inadvertently tries to signal to as if they were "elephant"
   flows, resulting in head-of-line blocking in a data center deployment
   scenario.

   Examples of other required research include:

   o  new AQM and scheduling algorithms

   o  appropriate use of delay-based methods and the implications of AQM

   o  suitable algorithms for marking ECN-capable packets that do not
      require operational configuration or tuning for common use

   o  experience in the deployment of ECN alongside AQM

   o  tools for enabling AQM (and ECN) deployment and measuring the
      performance

   o  methods for mitigating the impact of non-conformant and malicious
      flows

   o  implications on applications of using new network and transport
      methods

   Hence, this document reiterates the call of RFC 2309: we need
   continuing research as applications develop.











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5.  Security Considerations

   While security is a very important issue, it is largely orthogonal to
   the performance issues discussed in this memo.

   This recommendation requires algorithms to be independent of specific
   transport or application behaviors.  Therefore, a network device does
   not require visibility or access to upper-layer protocol information
   to implement an AQM algorithm.  This ability to operate in an
   application-agnostic fashion is an example of a privacy-enhancing
   feature.

   Many deployed network devices use queueing methods that allow
   unresponsive traffic to capture network capacity, denying access to
   other traffic flows.  This could potentially be used as a denial-of-
   service attack.  This threat could be reduced in network devices that
   deploy AQM or some form of scheduling.  We note, however, that a
   denial-of-service attack that results in unresponsive traffic flows
   may be indistinguishable from other traffic flows (e.g., tunnels
   carrying aggregates of short flows, high-rate isochronous
   applications).  New methods therefore may remain vulnerable, and this
   document recommends that ongoing research consider ways to mitigate
   such attacks.

6.  Privacy Considerations

   This document, by itself, presents no new privacy issues.

7.  References

7.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <http://www.rfc-editor.org/info/rfc2119>.

   [RFC3168]  Ramakrishnan, K., Floyd, S., and D. Black, "The Addition
              of Explicit Congestion Notification (ECN) to IP",
              RFC 3168, DOI 10.17487/RFC3168, September 2001,
              <http://www.rfc-editor.org/info/rfc3168>.

   [RFC4301]  Kent, S. and K. Seo, "Security Architecture for the
              Internet Protocol", RFC 4301, DOI 10.17487/RFC4301,
              December 2005, <http://www.rfc-editor.org/info/rfc4301>.






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   [RFC4774]  Floyd, S., "Specifying Alternate Semantics for the
              Explicit Congestion Notification (ECN) Field", BCP 124,
              RFC 4774, DOI 10.17487/RFC4774, November 2006,
              <http://www.rfc-editor.org/info/rfc4774>.

   [RFC5405]  Eggert, L. and G. Fairhurst, "Unicast UDP Usage Guidelines
              for Application Designers", BCP 145, RFC 5405, DOI
              10.17487/RFC5405, November 2008,
              <http://www.rfc-editor.org/info/rfc5405>.

   [RFC5681]  Allman, M., Paxson, V., and E. Blanton, "TCP Congestion
              Control", RFC 5681, DOI 10.17487/RFC5681, September 2009,
              <http://www.rfc-editor.org/info/rfc5681>.

   [RFC6040]  Briscoe, B., "Tunnelling of Explicit Congestion
              Notification", RFC 6040, DOI 10.17487/RFC6040, November
              2010, <http://www.rfc-editor.org/info/rfc6040>.

   [RFC6679]  Westerlund, M., Johansson, I., Perkins, C., O'Hanlon, P.,
              and K. Carlberg, "Explicit Congestion Notification (ECN)
              for RTP over UDP", RFC 6679, DOI 10.17487/RFC6679, August
              2012, <http://www.rfc-editor.org/info/rfc6679>.

   [RFC7141]  Briscoe, B. and J. Manner, "Byte and Packet Congestion
              Notification", BCP 41, RFC 7141, DOI 10.17487/RFC7141,
              February 2014, <http://www.rfc-editor.org/info/rfc7141>.

7.2.  Informative References

   [AQM-WG]   IETF, "Active Queue Management and Packet Scheduling (aqm)
              WG", <http://datatracker.ietf.org/wg/aqm/charter/>.

   [Bri15]    Briscoe, B., Brunstrom, A., Petlund, A., Hayes, D., Ros,
              D., Tsang, I., Gjessing, S., Fairhurst, G., Griwodz, C.,
              and M. Welzl, "Reducing Internet Latency: A Survey of
              Techniques and their Merit", IEEE Communications Surveys &
              Tutorials, 2015.

   [Choi04]   Choi, B., Moon, S., Zhang, Z., Papagiannaki, K., and C.
              Diot, "Analysis of Point-To-Point Packet Delay In an
              Operational Network", March 2004.

   [CONEX]    Mathis, M. and B. Briscoe, "Congestion Exposure (ConEx)
              Concepts, Abstract Mechanism and Requirements", Work in
              Progress, draft-ietf-conex-abstract-mech-13, October 2014.






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RFC 7567         Active Queue Management Recommendations       July 2015


   [Dem90]    Demers, A., Keshav, S., and S. Shenker, "Analysis and
              Simulation of a Fair Queueing Algorithm, Internetworking:
              Research and Experience", SIGCOMM Symposium proceedings on
              Communications architectures and protocols, 1990.

   [ECN-Benefit]
              Fairhurst, G. and M. Welzl, "The Benefits of using
              Explicit Congestion Notification (ECN)", Work in Progress,
              draft-ietf-aqm-ecn-benefits-05, June 2015.

   [Flo92]    Floyd, S. and V. Jacobsen, "On Traffic Phase Effects in
              Packet-Switched Gateways", 1992,
              <http://www.icir.org/floyd/papers/phase.pdf>.

   [Flo94]    Floyd, S. and V. Jacobsen, "The Synchronization of
              Periodic Routing Messages", 1994,
              <http://ee.lbl.gov/papers/sync_94.pdf>.

   [Floyd91]  Floyd, S., "Connections with Multiple Congested Gateways
              in Packet-Switched Networks Part 1: One-way Traffic.",
              Computer Communications Review , October 1991.

   [Floyd95]  Floyd, S. and V. Jacobson, "Link-sharing and Resource
              Management Models for Packet Networks", IEEE/ACM
              Transactions on Networking, August 1995.

   [Jacobson88]
              Jacobson, V., "Congestion Avoidance and Control", SIGCOMM
              Symposium proceedings on Communications architectures and
              protocols, August 1988.

   [Jain94]   Jain, R., Ramakrishnan, KK., and C. Dah-Ming, "Congestion
              avoidance scheme for computer networks", US Patent Office
              5377327, December 1994.

   [Lakshman96]
              Lakshman, TV., Neidhardt, A., and T. Ott, "The Drop From
              Front Strategy in TCP Over ATM and Its Interworking with
              Other Control Features", IEEE Infocomm, 1996.

   [Leland94] Leland, W., Taqqu, M., Willinger, W., and D. Wilson, "On
              the Self-Similar Nature of Ethernet Traffic (Extended
              Version)", IEEE/ACM Transactions on Networking, February
              1994.







Baker & Fairhurst         Best Current Practice                [Page 27]


RFC 7567         Active Queue Management Recommendations       July 2015


   [McK90]    McKenney, PE. and G. Varghese, "Stochastic Fairness
              Queuing", 1990,
              <http://www2.rdrop.com/~paulmck/scalability/paper/
              sfq.2002.06.04.pdf>.

   [Nic12]    Nichols, K. and V. Jacobson, "Controlling Queue Delay",
              Communications of the ACM, Vol. 55, Issue 7, pp. 42-50,
              July 2012.

   [Ren12]    Ren, Y., Zhao, Y., and P. Liu, "A survey on TCP Incast in
              data center networks", International Journal of
              Communication Systems, Volumes 27, Issue 8, pages 116-117,
              1990.

   [RFC768]   Postel, J., "User Datagram Protocol", STD 6, RFC 768,
              DOI 10.17487/RFC0768, August 1980,
              <http://www.rfc-editor.org/info/rfc768>.

   [RFC791]   Postel, J., "Internet Protocol", STD 5, RFC 791,
              DOI 10.17487/RFC0791, September 1981,
              <http://www.rfc-editor.org/info/rfc791>.

   [RFC793]   Postel, J., "Transmission Control Protocol", STD 7,
              RFC 793, DOI 10.17487/RFC0793, September 1981,
              <http://www.rfc-editor.org/info/rfc793>.

   [RFC896]   Nagle, J., "Congestion Control in IP/TCP Internetworks",
              RFC 896, DOI 10.17487/RFC0896, January 1984,
              <http://www.rfc-editor.org/info/rfc896>.

   [RFC970]   Nagle, J., "On Packet Switches With Infinite Storage",
              RFC 970, DOI 10.17487/RFC0970, December 1985,
              <http://www.rfc-editor.org/info/rfc970>.

   [RFC1122]  Braden, R., Ed., "Requirements for Internet Hosts -
              Communication Layers", STD 3, RFC 1122,
              DOI 10.17487/RFC1122, October 1989,
              <http://www.rfc-editor.org/info/rfc1122>.

   [RFC1633]  Braden, R., Clark, D., and S. Shenker, "Integrated
              Services in the Internet Architecture: an Overview",
              RFC 1633, DOI 10.17487/RFC1633, June 1994,
              <http://www.rfc-editor.org/info/rfc1633>.








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   [RFC2309]  Braden, B., Clark, D., Crowcroft, J., Davie, B., Deering,
              S., Estrin, D., Floyd, S., Jacobson, V., Minshall, G.,
              Partridge, C., Peterson, L., Ramakrishnan, K., Shenker,
              S., Wroclawski, J., and L. Zhang, "Recommendations on
              Queue Management and Congestion Avoidance in the
              Internet", RFC 2309, DOI 10.17487/RFC2309, April 1998,
              <http://www.rfc-editor.org/info/rfc2309>.

   [RFC2460]  Deering, S. and R. Hinden, "Internet Protocol, Version 6
              (IPv6) Specification", RFC 2460, DOI 10.17487/RFC2460,
              December 1998, <http://www.rfc-editor.org/info/rfc2460>.

   [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,
              DOI 10.17487/RFC2474, December 1998,
              <http://www.rfc-editor.org/info/rfc2474>.

   [RFC2475]  Blake, S., Black, D., Carlson, M., Davies, E., Wang, Z.,
              and W. Weiss, "An Architecture for Differentiated
              Services", RFC 2475, DOI 10.17487/RFC2475, December 1998,
              <http://www.rfc-editor.org/info/rfc2475>.

   [RFC2914]  Floyd, S., "Congestion Control Principles", BCP 41,
              RFC 2914, DOI 10.17487/RFC2914, September 2000,
              <http://www.rfc-editor.org/info/rfc2914>.

   [RFC4340]  Kohler, E., Handley, M., and S. Floyd, "Datagram
              Congestion Control Protocol (DCCP)", RFC 4340,
              DOI 10.17487/RFC4340, March 2006,
              <http://www.rfc-editor.org/info/rfc4340>.

   [RFC4960]  Stewart, R., Ed., "Stream Control Transmission Protocol",
              RFC 4960, DOI 10.17487/RFC4960, September 2007,
              <http://www.rfc-editor.org/info/rfc4960>.

   [RFC5348]  Floyd, S., Handley, M., Padhye, J., and J. Widmer, "TCP
              Friendly Rate Control (TFRC): Protocol Specification",
              RFC 5348, DOI 10.17487/RFC5348, September 2008,
              <http://www.rfc-editor.org/info/rfc5348>.

   [RFC5559]  Eardley, P., Ed., "Pre-Congestion Notification (PCN)
              Architecture", RFC 5559, DOI 10.17487/RFC5559, June 2009,
              <http://www.rfc-editor.org/info/rfc5559>.







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RFC 7567         Active Queue Management Recommendations       July 2015


   [RFC6057]  Bastian, C., Klieber, T., Livingood, J., Mills, J., and R.
              Woundy, "Comcast's Protocol-Agnostic Congestion Management
              System", RFC 6057, DOI 10.17487/RFC6057, December 2010,
              <http://www.rfc-editor.org/info/rfc6057>.

   [RFC6789]  Briscoe, B., Ed., Woundy, R., Ed., and A. Cooper, Ed.,
              "Congestion Exposure (ConEx) Concepts and Use Cases",
              RFC 6789, DOI 10.17487/RFC6789, December 2012,
              <http://www.rfc-editor.org/info/rfc6789>.

   [RFC6817]  Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind,
              "Low Extra Delay Background Transport (LEDBAT)", RFC 6817,
              DOI 10.17487/RFC6817, December 2012,
              <http://www.rfc-editor.org/info/rfc6817>.

   [RFC7414]  Duke, M., Braden, R., Eddy, W., Blanton, E., and A.
              Zimmermann, "A Roadmap for Transmission Control Protocol
              (TCP) Specification Documents", RFC 7414,
              DOI 10.17487/RFC7414, February 2015,
              <http://www.rfc-editor.org/info/rfc7414>.

   [Shr96]    Shreedhar, M. and G. Varghese, "Efficient Fair Queueing
              Using Deficit Round Robin", IEEE/ACM Transactions on
              Networking, Vol. 4, No. 3, July 1996.

   [Sto97]    Stoica, I. and H. Zhang, "A Hierarchical Fair Service
              Curve algorithm for Link sharing, real-time and priority
              services", ACM SIGCOMM, 1997.

   [Sut99]    Suter, B., "Buffer Management Schemes for Supporting TCP
              in Gigabit Routers with Per-flow Queueing", IEEE Journal
              on Selected Areas in Communications, Vol. 17, Issue 6, pp.
              1159-1169, June 1999.

   [Willinger95]
              Willinger, W., Taqqu, M., Sherman, R., Wilson, D., and V.
              Jacobson, "Self-Similarity Through High-Variability:
              Statistical Analysis of Ethernet LAN Traffic at the Source
              Level", SIGCOMM Symposium proceedings on Communications
              architectures and protocols, August 1995.

   [Zha90]    Zhang, L. and D. Clark, "Oscillating Behavior of Network
              Traffic: A Case Study Simulation", 1990,
              <http://groups.csail.mit.edu/ana/Publications/Zhang-DDC-
              Oscillating-Behavior-of-Network-Traffic-1990.pdf>.






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Acknowledgements

   The original draft of this document describing best current practice
   was based on [RFC2309], an Informational RFC.  It was written by the
   End-to-End Research Group, which is to say Bob Braden, Dave Clark,
   Jon Crowcroft, Bruce Davie, Steve Deering, Deborah Estrin, Sally
   Floyd, Van Jacobson, Greg Minshall, Craig Partridge, Larry Peterson,
   KK Ramakrishnan, Scott Shenker, John Wroclawski, and Lixia Zhang.
   Although there are important differences, many of the key arguments
   in the present document remain unchanged from those in RFC 2309.

   The need for an updated document was agreed to in the TSV area
   meeting at IETF 86.  This document was reviewed on the aqm@ietf.org
   list.  Comments were received from Colin Perkins, Richard
   Scheffenegger, Dave Taht, John Leslie, David Collier-Brown, and many
   others.

   Gorry Fairhurst was in part supported by the European Community under
   its Seventh Framework Programme through the Reducing Internet
   Transport Latency (RITE) project (ICT-317700).

Authors' Addresses

   Fred Baker (editor)
   Cisco Systems
   Santa Barbara, California  93117
   United States

   Email: fred@cisco.com


   Godred Fairhurst (editor)
   University of Aberdeen
   School of Engineering
   Fraser Noble Building
   Aberdeen, Scotland  AB24 3UE
   United Kingdom

   Email: gorry@erg.abdn.ac.uk
   URI:   http://www.erg.abdn.ac.uk











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