Internet Draft R. Pan
Network Working Group P. Natarajan, C. Piglione,M. Prabhu
Intended Status: Informational V. Subramanian, F. Baker, B. V. Steeg
Cisco Systems
Expires: June 2, 2013 December 10, 2012
PIE: A Lightweight Control Scheme To Address the
Bufferbloat Problem
draft-pan-tsvwg-pie-00
Abstract
Bufferbloat is a phenomenon where excess buffers in the network cause
high latency and jitter. As more and more interactive applications
(e.g. voice over IP, real time video streaming and financial
transactions) run in the Internet, high latency and jitter degrade
application performance. There is a pressing need to design
intelligent queue management schemes that can control latency and
jitter; and hence provide desirable quality of service to users.
We present here a lightweight design, PIE(Proportional Integral
controller Enhanced) that can effectively control the average
queueing latency to a target value. Simulation results, theoretical
analysis and Linux testbed results have shown that PIE can ensure low
latency and achieve high link utilization under various congestion
situations. The design does not require per-packet timestamp, so it
incurs very small overhead and is simple enough to implement in both
hardware and software.
Status of this Memo
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3. Design Goals . . . . . . . . . . . . . . . . . . . . . . . . . 4
4. The PIE Scheme . . . . . . . . . . . . . . . . . . . . . . . . 5
4.1 Random Dropping . . . . . . . . . . . . . . . . . . . . . . 5
4.2 Drop Probability Calculation . . . . . . . . . . . . . . . . 6
4.3 Departure Rate Estimation . . . . . . . . . . . . . . . . . 7
4.4 Handling Bursts . . . . . . . . . . . . . . . . . . . . . . 8
5. Comments and Discussions . . . . . . . . . . . . . . . . . . . 9
6. Incremental Deployment . . . . . . . . . . . . . . . . . . . . 10
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . . 10
8. References . . . . . . . . . . . . . . . . . . . . . . . . . . 10
8.1 Normative References . . . . . . . . . . . . . . . . . . . 10
8.2 Informative References . . . . . . . . . . . . . . . . . . 10
8.3 Other References . . . . . . . . . . . . . . . . . . . . . 10
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 11
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1. Introduction
The explosion of smart phones, tablets and video traffic in the
Internet brings about a unique set of challenges for congestion
control. To avoid packet drops, many service providers or data center
operators require vendors to put in as much buffer as possible. With
rapid decrease in memory chip prices, these requests are easily
accommodated to keep customers happy. However, the above solution of
large buffer fails to take into account the nature of the TCP
protocol, the dominant transport protocol running in the Internet.
The TCP protocol continuously increases its sending rate and causes
network buffers to fill up. TCP cuts its rate only when it receives a
packet drop or mark that is interpreted as a congestion signal.
However, drops and marks usually occur when network buffers are full
or almost full. As a result, excess buffers, initially designed to
avoid packet drops, would lead to highly elevated queueing latency
and jitter. It is a delicate balancing act to design a queue
management scheme that not only allows short-term burst to smoothly
pass, but also controls the average latency when long-term congestion
persists.
Active queue management (AQM) schemes, such as Random Early Discard
(RED), have been around for well over a decade. AQM schemes could
potentially solve the aforementioned problem. RFC 2309[RFC2309]
strongly recommends the adoption of AQM schemes in the network to
improve the performance of the Internet. RED is implemented in a wide
variety of network devices, both in hardware and software.
Unfortunately, due to the fact that RED needs careful tuning of its
parameters for various network conditions, most network operators
don't turn RED on. In addition, RED is designed to control the queue
length which would affect delay implicitly. It does not control
latency directly. Hence, the Internet today still lacks an effective
design that can control buffer latency to improve the quality of
experience to latency-sensitive applications.
Recently, a new AQM scheme, CoDel[CoDel], was proposed to control
the latency directly to address the bufferbloat problem. CoDel
requires per packet timestamps. Also, packets are dropped at the
dequeue function after they have been enqueued for a while. Both of
these requirements consume excessive processing and infrastructure
resources. This consumption will make CoDel expensive to implement
and operate, especially in hardware.
PIE aims to combine the benefits of both RED and CoDel: easy to
implement like RED and directly control latency like CoDel. Similar
to RED, PIE randomly drops a packet at the onset of the congestion.
The congestion detection, however, is based on the queueing latency
like CoDel instead of the queue length like RED. Furthermore, PIE
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also uses the latency moving trends: latency increasing or
decreasing, to help determine congestion levels. The design
parameters of PIE are chosen via stability analysis. While these
parameters can be fixed to work in various traffic conditions, they
could be made self-tuning to optimize system performance.
In addition, we assume any delay-based AQM scheme would be applied
over a Fair Queueing (FQ) structure or its approximate design, Class
Based Queueing (CBQ). FQ is one of the most studied scheduling
algorithms since it was first proposed in 1985 [RFC970]. CBQ has been
a standard feature in most network devices today[CBQ]. These designs
help flows/classes achieve max-min fairness and help mitigate bias
against long flows with long round trip times(RTT). Any AQM scheme
that is built on top of FQ or CBQ could benefit from these
advantages. Furthermore, we believe that these advantages such as per
flow/class fairness are orthogonal to the AQM design whose primary
goal is to control latency for a given queue. For flows that are
classified into the same class and put into the same queue, we need
to ensure their latency is better controlled and their fairness is
not worse than those under the standard DropTail or RED design.
This draft describes the overall design goals, system elements and
implementation details of PIE. We will also discuss various design
considerations, including how auto-tuning can be done.
2. Terminology
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
3. Design Goals
We explore a queue management framework where we aim to improve the
performance of interactive and delay-sensitive applications. The
design of our scheme follows a few basic criteria.
* First, we directly control queueing latency instead of
controlling queue length. Queue sizes change with queue draining
rates and various flows' round trip times. Delay bloat is the
real issue that we need to address as it impairs real time
applications. If latency can be controlled, bufferbloat is not
an issue. As a matter of fact, we would allow more buffers for
sporadic bursts as long as the latency is under control.
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* Secondly, we aim to attain high link utilization. The goal of
low latency shall be achieved without suffering link under-
utilization or losing network efficiency. An early congestion
signal could cause TCP to back off and avoid queue building up.
On the other hand, however, TCP's rate reduction could result in
link udner-utilization. There is a delicate balance between
achieving high link utilization and low latency.
* Furthermore, the scheme should be simple to implement and
easily scalable in both hardware and software. The wide adoption
of RED over a variety of network devices is a testament to the
power of simple random early dropping/marking. We strive to
maintain similar design simplicity.
* Finally, the scheme should ensure system stability for various
network topologies and scale well with arbitrary number streams.
Design parameters shall be set automatically. Users only need to
set performance-related parameters such as target queue delay,
not design parameters.
In the following, we will elaborate on the design of PIE and its
operation.
4. The PIE Scheme
As illustrated in Fig. 1, our scheme comprises three simple components:
a) random dropping at enqueuing; b) periodic drop probability update; c)
dequeuing rate estimation.
The following sections describe these components in further detail, and
explain how they interact with each other. At the end of this section,
we will discuss how the scheme can be easily augmented to precisely
control bursts.
4.1 Random Dropping
Like any state-of-the-art AQM scheme, PIE would drop packets randomly
according to a drop probability, p, that is obtained from the drop-
probability-calculation component:
* upon a packet arrival
randomly drop a packet with a probability p.
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Random Drop
/ --------------
-------/ --------------> | | | | | -------------->
/|\ | | | | | |
| -------------- |
| Queue Buffer |
| | | Departure bytes
| |queue |
| |length |
| | |
| \|/ \|/
| ----------------- -------------------
| | Drop | | |
-----<-----| Probability |<---| Departure Rate |
| Calculation | | Estimation |
----------------- -------------------
Figure 1. The PIE Structure
4.2 Drop Probability Calculation
The PIE algorithm periodically updates the drop probability as follows:
* estimate current queueing delay using Little's law:
est_del = qlen/depart_rate;
* calculate drop probability p as:
p = p + alpha*(est_del-target_del) + beta*(est_del-est_del_old);
est_del_old = est_del.
Here, the current queue length is denoted by qlen. The draining rate of
the queue, depart_rate, is obtained from the departure-rate-estimation
block. Variables, est_del and est_del_old, represent the current and
previous estimation of the queueing delay. The target latency value is
expressed in target_del. The update interval is denoted as Tupdate.
Note that the calculation of drop probability is based not only on the
current estimation of the queueing delay, but also on the direction
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where the delay is moving, i.e., whether the delay is getting longer or
shorter. This direction can simply be measured as the difference between
est_del and est_del_old. This is the classic Proportional Integral
controller design that is adopted here for controlling queueing latency.
The controller parameters, in the unit of hz, are designed using
feedback loop analysis where TCP's behaviors are modeled using the
results from well-studied prior art[TCP-Models].
We would like to point out that this type of controller has been studied
before for controlling the queue length [PI, QCN]. PIE adopts the
Proportional Integral controller for controlling delay and makes the
scheme auto-tuning. The theoretical analysis of PIE is under paper
submission and its reference will be included in this draft once it
becomes available. Nonetheless, we will discuss the intuitions for these
parameters in Section 5.
4.3 Departure Rate Estimation
The draining rate of a queue in the network often varies either because
other queues are sharing the same link, or the link capacity fluctuates.
Rate fluctuation is particularly common in wireless networks. Hence, we
decide to measure the departure rate directly as follows.
* we are in a measurement cycle if we have enough data in the queue:
qlen > deq_threshold
* if in a measurement cycle:
upon a packet departure
dq_count = dq_count + deque_pkt_size;
* if dq_count > deq_threshold then
depart_rate = dq_count/(now-start);
dq_count = 0;
start = now;
We only measure the departure rate when there are sufficient data in the
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buffer, i.e., when the queue length is over a certain threshold,
dq_threshold. Short, non-persistent bursts of packets result in empty
queues from time to time, this would make the measurement less accurate.
The parameter, dq_count, represents the number of bytes departed since
the last measurement. Once dq_count is over a certain threshold,
deq_threshold, we obtain a measurement sample. The threshold is
recommended to be set to 10KB assuming a typical packet size of around
1KB or 1.5KB. This threshold would allow us a long enough period to
obtain an average draining rate but also fast enough to reflect sudden
changes in the draining rate. Note that this threshold is not crucial
for the system's stability.
4.4 Handling Bursts
The above three components form the basis of the PIE algorithm. Although
we aim to control the average latency of a congested queue, the scheme
should allow short term bursts to pass through the system without
hurting them. We would like to discuss how PIE manages bursts in this
section.
Bursts are well tolerated in the basic scheme for the following reasons:
first, the drop probability is updated periodically. Any short term
burst that occurs within this period could pass through without
incurring extra drops as it would not trigger a new drop probability
calculation. Secondly, PIE's drop probability calculation is done
incrementally. A single update would only lead to a small incremental
change in the probability. So if it happens that a burst does occur at
the exact instant that the probability is being calculated, the
incremental nature of the calculation would ensure its impact is kept
small.
Nonetheless, we would like to give users a precise control of the burst.
We introduce a parameter, max_burst, that is similar to the burst
tolerance in the token bucket design. By default, the parameter is set
to be 100ms. Users can certainly modify it according to their
application scenarios. The burst allowance is added into the basic PIE
design as follows:
* if p == 0 and est_del < del_ref and est_del_old < del_ref
burst_allowance = max_burst;
* upon packet arrival
if burst_allowance > 0 enqueue packet;
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* upon probability update
burst_allowance = burst_allowance - Tupdate;
The burst allowance, noted by burst_allowance, is initialized to
max_burst. As long as burst_allowance is above zero, an incoming packet
will be enqueued bypassing the random drop process. During each update
instance, the value of burst_allowance is decremented by the update
period, Tupdate. When the congestion goes away, defined by us as p
equals to 0 and both the current and previous samples of estimated delay
are less than target_del, we reset burst_allowance to max_burst.
5. Comments and Discussions
While the formal analysis will be included later, we would like to
discuss the intuitions regarding how to determine the key parameters.
Although the PIE algorithm would set them automatically, they are not
meant to be magic numbers. We hope to give enough explanations here to
help demystify them so that users can experiment and explore on their
own.
As it is obvious from the above, the crucial equation in the PIE
algorithm is
p = p + alpha*(est_del-target_del) + beta*(est_del-est_del_old).
The value of alpha determines how the deviation of current latency from
the target value affects the drop probability. The beta term exerts
additional adjustments depending on whether the latency is trending up
or down. Note that the drop probability is reached incrementally, not
through a single step. To avoid big swings in adjustments which often
leads to instability, we would like to tune p in small increments.
Suppose that p is in the range of 1%. Then we would want the value of
alpha and beta to be small enough, say 0.1%, adjustment in each step. If
p is in the higher range, say above 10%, then the situation would
warrant a higher single step tuning, for example 1%. Finally, the drop
probability would only be stabilized when the latency is stable, i.e.
est_del equals est_del_old; and the value of the latency is equal to
target_del. The relative weight between alpha and beta determines the
final balance between latency offset and latency jitter.
The update interval, Tupdate, also plays a key role in stability. Given
the same alpha and beta values, the faster the update is, the higher the
loop gain will be. As it is not showing explicitly in the above
equation, it can become an oversight. Notice also that alpha and beta
have a unit of hz.
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As a further extension, we could introduce weights for flows that are
classified into the same queue to achieve differential dropping. For
example, the dropping probability for flow i could be p(i) =
p/weight(i). Flows with higher weights would receive proportionally less
drops; and vice versa. Adding FQ on top, FQ_PIE, is another alternative.
Also, we have discussed congestion notification via the form of packet
drops. The algorithm can be easily applied to networks codes where Early
Congestion Notification (ECN) is enabled. The drop probability, p, above
would become marking probability.
6. Incremental Deployment
One nice property of the AQM design is that it can be independently
designed and operated without the requirement of being inter-operable.
Although all network nodes can not be changed altogether to adopt
latency-based AQM schemes, we envision a gradual adoption which would
eventually lead to end-to-end low latency service for real time
applications.
7. IANA Considerations
There are no actions for IANA.
8. References
8.1 Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
8.2 Informative References
[RFC970] Nagle, J., "On Packet Switches With Infinite
Storage",RFC970, December 1985.
8.3 Other References
[CoDel] Nichols, K., Jacobson, V., "Controlling Queue Delay", ACM
Queue. ACM Publishing. doi:10.1145/2209249.22W.09264.
[CBQ] Cisco White Paper, "http://www.cisco.com/en/US/docs/12_0t
/12_0tfeature/guide/cbwfq.html".
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[TCP-Models] Misra, V., Gong, W., and Towsley, D., "Fluid-based
Analysis of a Network of AQM Routers Supporting TCP
Flows with an Application to RED", SIGCOMM 2000.
[PI] Hollot, C.V., Misra, V., Towsley, D. and Gong, W.,
"On Designing Improved Controller for AQM Routers
Supporting TCP Flows", Infocom 2001.
[QCN] "Data Center Bridging - Congestion Notification",
http://www.ieee802.org/1/pages/802.1au.html.
Authors' Addresses
Rong Pan
Cisco Systems
510 McCarthy Blvd,
Milpitas, CA 95134, USA
Email: ropan@cisco.com
Preethi Natarajan,
Cisco Systems
510 McCarthy Blvd,
Milpitas, CA 95134, USA
Email: prenatar@cisco.com
Chiara Piglione
Cisco Systems
510 McCarthy Blvd,
Milpitas, CA 95134, USA
Email: cpiglion@cisco.com
Mythili Prabhu
Cisco Systems
510 McCarthy Blvd,
Milpitas, CA 95134, USA
Email: mysuryan@cisco.com
Vijay Subramanian
Cisco Systems
510 McCarthy Blvd,
Milpitas, CA 95134, USA
Email: vijaynsu@cisco.com
Fred Baker
Cisco Systems
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510 McCarthy Blvd,
Milpitas, CA 95134, USA
Email: fred@cisco.com
Bill Ver Steeg
Cisco Systems
5030 Sugarloaf Parkway
Lawrenceville, GA, 30044, USA
Email: versteb@cisco.com
Pan et al. Expires April 17, 2013 [Page 12]
