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Recent measurements of local-area and wide-area traf c [8, 28, 42] have shown that network traf c exhibits variability at a wide range of scales. What is striking is the ubiquitousness of the phenomenon, which has been observed in diverse networking contexts, from Ethernet to ATM, LAN and WAN, compressed video, and HTTPbased WWW traf c [8, 15, 23, 42]. Such scale-invariant variability is in strong contrast to traditional models of network traf c, which show burstiness at short time scales but are essentially smooth at large time scales; that is, they lack long-range dependence. Since scale-invariant burstiness can exert a signi cant impact on network performance, understanding the causes and effects of traf c self-similarity is an important problem. In previous work [33, 34], we have investigated the causal and performance aspects of traf c self-similarity, and we have shown that self-similar traf c ow is an intrinsic property of networked client=server systems with heavy-tailed le size distributions, and conjoint provision of low delay and high throughput is adversely affected by scale-invariant burstiness. From a queueing theory perspective, the principal distinguishing characteristic of long-range-dependent (LRD) traf c is that the queue length distribution decays much more slowly that is, polynomially visa-vis short-range-dependent (SRD) traf c sources such as Poisson sources, which exhibit exponential decay. A number of performance studies [1, 2, 11, 29, 32, 34]

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Self-Similar Network Traf c and Performance Evaluation, Edited by Kihong Park and Walter Willinger ISBN 0-471-31974-0 Copyright # 2000 by John Wiley & Sons, Inc.

Copyright 1999 Gary McGraw and Edward Felten. All rights reserved. Published by John Wiley & Sons, Inc.

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have shown that self-similarity has a detrimental effect on network performance, leading to increased delay and packet loss rate In Grossglauser and Bolot [18] and Ryu and Elwalid [37], the point is advanced that for small buffer sizes or short time scales, long-range dependence has only a marginal impact This is, in part, due to a saturation effect that arises when resources are overextended, whereby the burstiness associated with short-range-dependent traf c is suf cient and, in many cases, dominant to cause signi cant buffer over ow What is still in its infancy, however, is the problem of controlling self-similar network traf c By the control of self-similar traf c, we mean the problem of modulating traf c ow such that network performance including throughput is optimized.

Scale-invariant burstiness introduces new complexities into the picture, which make the task of providing quality of service (QoS) while achieving high utilization signi cantly more dif cult First and foremost, scale-invariant burstiness implies the existence of concentrated periods of high activity at a wide range of time scales which adversely affects congestion control Burstiness at ne time scales is commensurate with burstiness observed for traditional short-range dependent traf c models The distinguishing feature is burstiness at coarser time scales, which induces extended periods of either overload or underutilization and degrades overall performance However, on the ip side, long-range dependence, by de nition, implies the existence of nontrivial correlation structure, which may be exploitable for congestion control purposes, information to which current algorithms are impervious.

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CHAPTER SECTIONS: 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8

In this chapter, we show the feasibility of ``predicting the future'' under selfsimilar traf c conditions with suf cient reliability such that the information can be effectively utilized for congestion control purposes First, we show that long-range dependence can be on-line detected to predict future traf c levels and contention at time scales above and beyond the time scale of the feedback congestion control Second, we present a traf c modulation mechanism based on multiple time scale congestion control framework (MTSC) [46] and show that it is able to effectively exploit this information to improve network performance, in particular, throughput The congestion control mechanism works by selectively applying aggressiveness using the predicted future when it is warranted, throttling the data rate upward if the predicted future contention level is low, being more aggressive the lower the predicted contention level.

We show that the selective agressiveness mechanism is of bene t even for short-range-dependent traf c; however, being signi cantly more effective for long-range dependent traf c, leading to comparatively large performance gains We also show that as the number of connections engaging in selective aggressiveness control (SAC) increases, both fairness and ef ciency are preserved The latter refers to the total throughput achieved across all SAC-controlled connections The rest of the chapter is organized as follows In Section 182, we give a brief overview of self-similar network traf c and the speci c setup employed in this chapter In Section 183, we describe the predictability mechanism and its ef cacy at extracting the correlation structure present in long-range dependent traf c This is followed by Section 184, where we describe the SAC protocol and a re nement of.

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