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around recent developments and the landscape of previous accomplishments, grouped into three areas workload characterization, performance analysis, and traf c control Physical modeling, which can be viewed as a fourth category, is grouped with workload characterization Workload Characterization The original focus of self-similar burstiness in local area and wide area network traf c has expanded into the generalized framework of workload modeling, which captures source behavior and structural properties of network systems, not necessarily restricted to network and link layers This stems, in part, from the realization that network performance as measured by packet drop, queueing delay, and jitter at multiplexing points in the network is affected by a multitude of factors including variability of streamed real-time VBR video, connection arrival patterns and their durations, the make-up of les being transported, control actions in the protocol stack, and user behavior that drives network applications.



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Increasingly, these activities transpire under the umbrella of the World Wide Web (WWW), and characterizing the structural properties static and dynamic of the global wired=wireless Internet that impact network performance has become an important goal The research challenge lies in identifying, quantifying, and modeling invariant or ``slowly changing'' system traits, in the midst of a rapidly growing network infrastructure, that are relevant to network performance Performance Analysis Performance analysis of queueing systems with self-similar input has yielded the fundamental insight that queue length distribution decays polynomially vis-a-vis the more accustomed case of exponential decay with Markovian input In the resource provisioning context, this is interpreted to mean that resource dimensioning using buffer sizing is an ineffective policy relative to bandwidth allocation There remain a number of challenges First, the queueing results are asymptotic in nature where buffer capacity in some form is taken to in nity to achieve tractability.





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Systems divided into independent partitions Multiple virtual copies of the operating system within a single virtual (or real) system Scheduling of resources within a single system to control predictability and ensure service level agreements

Little is known about nite buffer systems except for observations on the dependence of packet loss rate on the``effective'' time scale induced by buffer size, and its delimiting impact on correlation structure at larger time scales with respect to its in uence on queueing [28, 60] Second, performance evaluation with self-similar traf c has concentrated on rst-order performance measures that is, packet loss rate and queueing delay which is but one, albeit important, yardstick In the modern network environment with multimedia and other QoS-sensitive traf c streams comprising a growing fraction of network traf c, second-order performance measures in the form of ``jitter'' such as delay variation and packet loss variation are of import to provisioning user-speci ed QoS Selfsimilar burstiness is expected to exert a negative in uence on second-order performance measures and multimedia traf c controls for example, packet-level FEC that are susceptible to concentrated packet loss.

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This chapter isn t meant to be a full tutorial in the configuration aspects of each topic. Rather, we want to relate the issues of resource management to making systems predictable. Going back to our definition of availability as making sure users can get their jobs done, virtualization of machine resources and allocation of those resources to tasks aids in availability planning by offering the ability to cap user-visible latency.

Third, performance analysis is carried out in equilibrium, which may be problematic for self-similar workloads given their slow convergence properties As a related point, the bulk of TCP connections is known to be short-lived, and there is a disconnect between steadystate techniques and performance evaluation of short- and medium-duration ows..

Whether you call them domains, partitions, or logical partitions (LPARs in mainframe parlance), the first level of VM abstraction is to subdivide a large server into several smaller virtual servers. There s no hardware effect in this subdivision; it s all done via software that reroutes the logical connections of the backplane. This soft cabling allocates hardware system resources to create multiple, independent machine instances. Each domain or partition runs its own copy of the operating system, can be booted or crash independently,

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