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In order to help design and control the emerging high-speed communication networks, we want source traf c models (also called offered load models or bandwidth demand models) that can be both realistically t to data and successfully analyzed. Many recent traf c measurements have shown that network traf c is quite complex, exhibiting phenomena such as heavy-tailed probability distributions, longrange dependence, and self similarity; for example, see Caceres et al. [7], Leland et al. [23], Paxson and Floyd [24], and Crovella and Bestavros [10]. In fact, the heavy-tailed distributions may be the cause of all these phenomena, because they tend to cause long-range dependence and (asymptotic) self-similarity. For example, the input and buffer content processes associated with an on=off source exhibit long-range dependence when the on and off times have heavy-tailed probability distributions; for example, see Section 17.9. Heavy-tailed distributions are known to cause self-similarity in models of (asymptotically) aggregated traf c; see Willinger et al. [27]. In this chapter we propose a way to analyze the performance of a network with multiple on=off sources and more general multilevel sources in which the on-time, off-time, and level-holding-time distributions are allowed to have heavy tails. To do



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But he won t volunteer the critical answer of whether you should bring an umbrella unless you speci cally ask, Do you think it might rain The same is true of marketing especially online marketing where we are data rich and insight poor..

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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.





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Load balancing is a scalability feature, not a reliability solution. Distributing input load over several servers allows horizontal scalability of a service; that is, the service capacity can be increased by adding more hosts that perform the same function (the horizontal notion probably comes from the profile you see entering the machine room, as the new servers increase the width of the server skyline). When a single server in a load-balancing configuration fails, or when one of the intermediate network components fails, recovery is still an issue. Reliability features can be added to load-balanced configurations to improve recovery time and overall uptime, but the simple load-balancing solution by itself does not improve end-to-end reliability. We ll look at horizontal scalability of web services in 14, Data and Web Services. Network access isn t as secure as it should be. Are all of the network interface jacks secured, or can anyone plug a random PC into the building LAN Incorrectly configured hosts cause major network problems without any malicious intent. Network cables are also a common source of failures. Twisted pair cables are indistinguishable from telephone cables unless you look carefully at the RJ45 (or RJ11) connectors on the ends. Once you start running network cables in wiring raceways or near power circuits, you ll eventually find a mechanical failure that leads to a short, an open, or an intermittent failure in the network.

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so we must go be beyond the familiar Markovian analysis. To achieve the required analyzability with this added model complexity, we propose a simpli ed kind of analysis. In particular, we avoid the customary queueing detail (and its focus on buffer content and over ow) and instead concentrate on the instantaneous offered load. We describe the probability that aggregate demand (the input rate from a collection of sources) exceeds capacity (the maximum possible output rate) at any time. Focusing on the probability that aggregate demand exceeds capacity is tantamount to considering a bufferless model, which we believe is often justi ed. By also considering the probability that aggregate demand exceeds other levels, we provide a quite exible performance characterization. This approach also can generate approximations describing loss and delay with nite capacity; for example, see Duf eld and Whitt [14], Section 5. To a large extent, the present chapter is a review of our recent work [14, 15], to which we refer the reader for additional discussion. In Duf eld et al. [16] the model is extended to include a nonhomogeneous Poisson connection arrival process. Then each active connection may generate traf c according to one of the source traf c models presented here. It is signi cant that we are able to obtain useful descriptions of the offered load in the nonstationary context. 17.2 A GENERAL SOURCE MODEL

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