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21.2 OPEN PROBLEMS IN WORKLOAD CHARACTERIZATION
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The same problem exists when using simulation as the principal performance evaluation tool. Traf c Control Traf c control for self-similar traf c has been explored on two fronts: (1) as an extension of performance analysis in the resource provisioning context, and (2) from the multiple time scale traf c control perspective where correlation structure at large time scales is actively exploited to improve network performance. The resource provisioning aspect advocates a small buffer=large bandwidth resource dimensioning policy, which when coupled with the central limit theorem yields predictable multiplexing gains when a large number of independent ows are aggregated. Whereas resource provisioning is open-loop in nature, multiple time scale traf c control seeks to achieve performance gains by exploiting correlation structure in self-similar traf c at time scales exceeding the time horizon of the feedback loop to impart proactivity to reactive controls (e.g., TCP). This is relevant in broadband wide area networks where the delay bandwidth product problem is especially severe, and mitigating the performance degradation due to outdated feedback is critical to facilitating scalable, adaptive traf c control. The initial success of this approach [62, 67 69] (see 18 for an application to rate-based congestion control) leads to a generalization to workload-sensitive traf c control, where facilitation of workload sensitivity is expanded along several traf c control dimensions including the two core features for harnessing predictability at large time scales: long-range correlation in network traf c and heavy-tailedness of connection durations. Workload-sensitive traf c control is a broad area that can bene t from concerted efforts at several fronts, spanning novel mechanisms for detecting and exploiting large time scale predictability structure, short-duration connection management, packet scheduling, end system support, and dynamic admission control with self-similar call arrivals and=or heavy-tailed connection durations.
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OPEN PROBLEMS IN WORKLOAD CHARACTERIZATION Physical Modeling
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Unlike many systems of study including economic, social, and certain physical sciences (e.g., astronomy, earth and atmospheric science), network systems admit to design, implementation, and controlled experimentation of the underlying physical system at nontrivial scales for example, protocol deployment in autonomous systems belonging to a single service provider which facilitates an intimate, mechanistic understanding of the system at hand. Model selection is not bound by ``black box'' evaluations, and physical models that can explicate traf c characteristics in terms of elementary, veri able system properties and network mechanics, in addition to data tting, provide an opportunity to be exploited. The challenge lies in combining relevant features from workload modeling, network architecture protocols and transmission technology user behavior, and analytical modeling into a
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consistent, effective description of network systems, in particular, the Internet. As such, physical modeling is a research program that transcends workload modeling, encompassing both performance analysis and traf c control. 21.2.2 Multifractal Traf c Characterization
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Since the collection and analysis of the Bellcore Ethernet LAN data [41], follow-up works [1, 15, 27, 57] have shown the robustness of self-similar burstiness in network traf c. This has led to the heuristic description: Poisson connection arrivals with heavy-tailed connection duration times lead to self-similar burstiness in multiplexed network traf c. This is a rough, `` rst-order'' description of the empirical facts for example, TCP connection arrivals exhibit self-similarity (see 15 on TCP workload modeling) which serves to point toward the principal causal attribute of self-similarity: heavy-tailed activity durations. Recent analysis of WAN IP traf c [23, 24] has revealed multifractal structure in the form of nonuniform scaling across short and long time scales (see 20 for a comprehensive discussion). That is, on top of the monofractal picture captured by the heuristic statement above, there exists further variability within each connection in particular, heavy-tailed TCP connection lifetimes that fall outside the scope of monofractal self-similarity, which principally concerns large time scale structure in network traf c. The re ned, short time scale structure can be described by cascade constructions also used in the generation of deterministic fractals such as two-dimensional grey-scale fractal images [5] where variability (within a connection) is obtained by recursive application of ``measure redistribution'' according to some xed rule (cf. 1, Fig. 1.2 (middle)). Several problems remain unsolved. Multiplicative Scaling and Causality What causes multiplicative scaling observed for short-range correlation structure Is it related to fragmentation in the TCP=IP protocol stack (including the MAC layer) TCP's feedback control (ARQ and window-based congestion control) ACK compression Topological considerations If a combination, are there dominant factors Cascades although suggestive of certain physical causes are ultimately a data modeling construct and fall short of establishing a mechanistic description of the underlying workload. From a workload generation or synthesis perspective, given the possible dependence of multiplicative scaling in short time scale traf c structure on feedback control, an open-loop generation of traf c may be unsatisfactory for closed-loop traf c control and its performance evaluation purposes. Impact of Re ned Short Time Scale Modeling Is multiplicative scaling a robust, invariant phenomenon as self-similarity is for large time scale structure Can modeling of short time scale structure lead to a better understanding of dynamic properties of network protocols Does a re ned model of short-range structure lead to a more accurate prediction of network performance In other words, is re ned modeling of short time scale structure in network traf c a ``relevant'' research activity It is clear that in some contexts (see, e.g., 12 for a discussion of
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