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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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the predictability mechanism for on-line, per-connection estimation. In Section 18.5 we show performance results of SAC and show its ef cacy under different longrange dependence conditions and when the number of SAC connections is varied. We conclude with a discussion of current results and future work.





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There are two kinds of disks in any failover configuration. There are the private disks that contain each host s private system information. These disks are dedicated to one, and only one, server, so they should be electrically independent from the shared disks, which can be accessed by both servers in the pair. The shared disks contain the critical data that is needed by the applications for which the servers were made highly available in the first place. To maximize system availability, all disks must have a level of redundancy, as discussed in 7, Highly Available Data Management. Ideally, all disks should be mirrored from one disk controller to another, and from one disk cabinet to another. If the system is important enough to cluster, then the system is important enough to have its disks mirrored.

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Let Xt tPZ be a time series, which, for example, represents the trace of data ow at a bottleneck link measured at some xed time granularity. We de ne the aggregated series Xi m as Xi m 1 X Xim : m im m 1

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That is, Xt is partitioned into blocks of size m, their values are averaged, and i is used to index these blocks. Let r k and r m k denote the autocorrelation functions of Xt and Xi m , respectively. Xt is self-similar more precisely, asymptotically second-order selfsimilar if the following conditions hold: r k $ const k b ; r m k $ r k ; 18:1 18:2

it must be located on the private disks These disks are generally located inside each server, although that is not a requirement In fact, it is better that the private (or system) disks be physically located external to a server This way, if a server fails, no surgery to extract the disks from one server and to install them in the other will be required, thereby delivering a quicker MTTR Private disks, by definition, cannot be connected to multiple hosts Only one server can and should ever see the data on these disks The entire contents of the private disks should be mirrored Some may argue that swap space need not be mirrored This is simply not true If the disk containing critical application data in swap fails, then at the very least, the application will stop working.

for k and m large, where 0 < b < 1. That is, Xt is ``self-similar'' in the sense that the correlation structure is preserved with P respect to time aggregation relation (18.2) and r k behaves hyperbolically with I r k I as implied by Eq. (18.1). The k 0 latter property is referred to as long-range dependence. Let H 1 b=2. H is called the Hurst parameter, and by the range of b, 1 < H < 1. It follows from Eq. (18.1) that the farther H is away from 1 the more 2 2 long-range dependent Xt is, and vice versa. Thus, the Hurst parameter acts as an indicator of the degree of self-similarity. A test for long-range dependence can be obtained by checking whether H signi cantly deviates from 1 or not. We use two methods for testing this condition. 2 The rst method, the variance time plot, is based on the slowly decaying variance of a self-similar time series. The second method, the R=S plot, use the fact that for a self-similar time series, the rescaled range or R=S statistic grows according to a power law with exponent H as a function of the number of points included. Thus, the plot of R=S against this number on a log log scale has a slope that is an estimate of H. A comprehensive discussion of the estimation methods can be found in Beran [4] and Taqqu et al. [39].

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