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short-range versus long-range dependence issues) short-range structure can dominate performance. Re ned traf c modeling, in general, if not checked with respect to its potential to advance fundamental understanding, can become a ``data tting'' activity the subject of time series analysis yielding limited new networking insights. The standards required of re ned traf c modeling work must therefore be evermore stringent. 21.2.3 Spatial Workload Characterization
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Physical modeling [15, 51], which reduces the root cause of self-similarity in network traf c to heavy-tailed le size distributions on le systems and Web servers is a form of spatial workload modeling. That is, the temporal property of network traf c which is a primary factor determining performance is related to the spatial or structural property of networked distributed systems. Following are a number of extensions to the spatial workload modeling theme that may exhibit features related to ``correlation at a distance,'' a characteristic of self-similarity in network traf c. Mobility Model In an integrated wired=wireless network environment, understanding the movement pattern of mobiles is relevant for effective resource management and performance evaluation. Current models are derived from transportation studies [19, 34, 40], which possess a coarse measurement resolution or, more commonly, make a range of user mobility assumptions including random walk, Poisson number of base stations=cells visited as a function of time, and exponential stay durations whose validity is insuf ciently justi ed. It would not be too surprising to nd correlation structure at large time and=or space scales a user, after the morning commute, may stay at her of ce for the remainder of the day except for brief excursions, students on a campus move from class to class at regular intervals and in clusters, users congregate in small regions (e.g., to take in a baseball game at a stadium) in numbers signi cantly exceeding the average density, traf c obeys predictable ow patterns which, in turn, can impact performance due to sustained load on base stations connected to wireline networks. A measurement-based mobility model (and tools for effective tracing [48]) that accurately characterizes user mobility is an important component of future workload modeling. Logical Information Access Pattern With the Internet and the World Wide Web becoming interwoven in the socioeconomic fabric of everyday life, it becomes relevant to characterize the information access pattern by information content (in addition to geographical location) so as to facilitate ef cient access and dissemination. Popular Web sites that is, URLs may be accessed more frequently than less popular URLs in a statistically regular fashion, for example, with access frequency obeying power laws as a function of some popularity index (e.g., ranking). Hypertext documents and hyperlinks can be viewed as forming a directed graph, and the resulting graph structure of the World Wide Web can be analyzed with respect to its connectivity in an analogous manner as has been carried out recently for Internet network topology [22]. An information topology project that parallels efforts in
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Internet topology and distance map discovery (e.g., IDMaps [26, 37]), and identi es how logical information is organized on the World Wide Web including possible invariant scaling features in its connectivity structure and access pattern may have bearing on network load=temporal traf c properties and, consequently, network performance. User Behavior Most network applications are driven by users for example, via interaction with a Web browser GUI and thus the connection, session, or call arrival process is intimately tied with user behavior, in particular, as it relates to network state. Starting with the time-of-day, user behavior may be a function of network congestion leading to self-regulation (a user may choose to continue his Web sur ng activities at a later time if overall response time is exceedingly high, a form of backoff), congestion pricing may assign costs above and beyond those exacted by performance degradation, users may switch between different service classes in a multiservice network [10, 20], users may perform network access and control decisions cooperatively or sel shly leading to a noncooperative network environment characteristic of the Internet, users may observe behavioral patterns when navigating the Web, and so forth. The challenge lies in identifying robust, invariant behavioral traits possibly exhibiting scaling phenomena and quantifying their in uence on network performance. Scaling Phenomena in Network Architecture The recent discovery of power law scaling in network topology [22] points toward the fact that scaling may not be limited to network traf c and system workloads. On the other hand, power law scaling in the connectivity structure of the Internet stretches the meaning of ``workload characterization'' if it is to be included under the same umbrella. More importantly, it is unclear whether the diffusive connectivity structure implied by power laws affects temporal traf c properties and network performance in unexpected, nontrivial ways. For example, routing in graphs with exponential scaling in their connectivity structure is different from routing in graphs with power law scaling, but that is not to say that this has implications for traf c characterization and performance above and beyond its immediate scope of in uence number of paths between a pair of nodes, their make-up, and generation of ``realistic'' network topologies for benchmarking. If the distribution of link capacities were to obey a power law, then it is conceivable that this may exert a traf c shaping effect in the form of variable stretching-in-time of a transmission, which can inject heavy tailedness in transmission or connection duration that is not present in the original workload. The challenge in architectural characterization lies in identifying robust, invariant properties exhibiting scaling behavior and relating these properties to network traf c, load, and performance where a novel and robust relationship is established. 21.2.4 Synthetic Workload Generation
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An integral component of workload modeling is synthetic workload generation. In many instances, in particular, those where the workload model is constructive in
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