RFC 7567
IETF Recommendations Regarding Active Queue Management
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 ManagementAbstract
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.
Copyright Notice Copyright (c) 2015 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. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License. This document may contain material from IETF Documents or IETF Contributions published or made publicly available before November 10, 2008. The person(s) controlling the copyright in some of this material may not have granted the IETF Trust the right to allow modifications of such material outside the IETF Standards Process. Without obtaining an adequate license from the person(s) controlling the copyright in such materials, this document may not be modified outside the IETF Standards Process, and derivative works of it may not be created outside the IETF Standards Process, except to format it for publication as an RFC or to translate it into languages other than English.
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
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
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.
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.
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.
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.
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.
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
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.
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.
(next page on part 2)
