February 22, 2017

37.Multimedia by John G. Webster (Editor)

By John G. Webster (Editor)

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Assume a process that generates requests for objects in which object oj is requested with probability heat(oj) (all independent). We assume no advance knowledge of the possible permutation of requests for different objects. Let F denote the set of objects with a disk resident fraction except the one that is referenced by the pending request, size(F ) ϭ ͚xʦF DISK(X). Moreover, assuming a new request arrives referencing object Z (F ǟ F Ϫ ͕Z͖), we define free_disk_space as C Ϫ (size(F ) ϩ DISK(Z)).

This rate is normalized by RD. 40 means that the consumption rate of the object referenced by t is 40% of RD, the cluster bandwidth. • p(t): The cluster that contains the first block of the object referenced by t, 1 Յ p(t) Յ D. It determines the placement of the object referenced by t on the clusters. We denote a task ti as a quadruple: ͗r(ti), ᐉ(ti), c(ti), p(ti)͘. 741 User interface Display schedule Logical abstraction Storage manager Retrieval schedule Focus Figure 21. Three levels of abstraction.

741 User interface Display schedule Logical abstraction Storage manager Retrieval schedule Focus Figure 21. Three levels of abstraction. , no retrieval contention). Moreover, ARS should satisfy an optimization objective. Depending on the application, this objective could be minimizing either (1) the average startup latency of the tasks, or (2) the total duration of scheduling for a set of tasks (maximizing throughput). Movieon-demand is one sample application. Definition 1: The problem of ARS is to find a schedule ␴ (where ␴:T Ǟ N) for a set T , such that (1) it minimizes the finishing time (52) w, where w is the least time at which all tasks of T have been completed, and (2) satisfies the following constraints: • ᭙t ʦ T ␴(t) Ն r(t).

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