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distributions of the durations as well as of the rates at which individual connections emit packets are heavy tailed with in nite variance) may become relevant in practice and may see genuine applications in the networking area in the near future. To illustrate, let Xi m denote an on=off-type connection described earlier, where in addition to the duration of the on=off periods, the rate at which the connection emits packets during the on period is also heavy tailed with in nite variance (with tail index , say). Focusing on this modi cation of the renewal model investigated by Mandelbrot [22] and Taqqu and Levy [34], Levy and Taqqu [21] recently showed that when studying the overall traf c rate process X m de ned in Eq. (20.1) that is, aggregating many such independent connections one can obtain a dependent, stationary process that has a stable marginal distribution with in nite variance and that is self-similar as in Eq. (20.5) with self-similarity parameter H given by H 1 : 20:6



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They block broadcast packets and can be used to block other kinds of unicast traffic as well Firewalls aren t just for external defensive positions Gaining a measure of predictability on the network means having more control.





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Here denotes the index characterizing the heaviness of the tail of the traf c rate of the individual connections, and denotes the tail index associated with the distributions of the durations of on and off periods, which we assume for simplicity to be identical Observe that in the nite variance case 2 , relation (206) reduces to the familiar H 3 =2 P 1 ; 1 , which appears in connection with 2 fractional Gaussian noise considered earlier However, in contrast to FGN, the superposition process obtained under the assumption of heavy tails with in nite variance on the durations and rates is not Gaussian but has heavy-tailed marginals instead, implying that there is a much higher probability than in the Gaussian case that the overall traf c rate can differ greatly from the average value and that it can take extreme values (a phenomenon also known as intermittency).

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Being nonGaussian, one of the obstacles at this stage for using these kinds of stable superposition processes in the context of modeling data traf c is that their statistical parameters (which speci es the marginals) and H (Eq (205)) do not de ne them completely; there exist a number of different dependent, stationary increment processes with stable marginals with the same and same self-similarity parameter H see, for example, Samorodnitsky and Taqqu [33] This is in stark contrast to FGN, where knowing the second-order statistical characteristics (ie, variance and Hurst parameter H) uniquely de nes the process, due to Gaussianity 203 THE SMALL-TIME SCALING BEHAVIOR OF NETWORK TRAFFIC The analysis of measured network traf c and resulting understanding of some of its underlying structure outlined in Section 20.

2 have led to the realization that while wide-area traf c is consistent with asymptotic self-similarity or large-time scaling behavior, its small-time scaling features are very different from those observed over large time scales Thus, to provide an adequate and more complete description of.

over what kinds of traffic actually enter and exit the network. This requires careful coordination with applications developers and users, but it s well worth the effort. Use cache servers. Web proxy cache servers do what their name implies cache recently used static web content to reduce network load and improve throughput. They corral traffic and do not require applicationlevel changes. Networking is not a zero-sum game. It s usually a positive-sum game. If you subdivide a network with a 60 percent utilization into three networks, don t expect to see a nice dip to the low 20 percent range on each one. Once you remove that bottleneck, nodes on each subnet will step up their activity, producing more load than before. When you stop choking someone, that person tends to fight back and the same is true for networks. Our focus on end-to-end system reliability necessitates some discussion of network loads, latencies, and failures, but our focus will primarily be on building redundancy into the network infrastructure to tolerate physical failures. In the case of a network that goes open loop, with excessive load, broadcast storms, or other unexplained incidents that send you into an X-Files like state of weirdness, treat the problem like a physical failure and resort to a redundant network while you find the truth that s out there. We ll discuss ways to build in redundancy and handle failover now that you know what you re protecting yourself against.

actual network traf c, it is necessary to deal with these small-time scaling features and to ultimately understand their cause and effects. To this end, we summarize in this section our current understanding of this very recent development in network traf c analysis and modeling by introducing concepts that are novel to the networking area, for example, multifractals, conservative cascades, and multiplicative structure, and illustrate their relevance to networking. 20.3.1 Multifractals

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