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Ideally, we should minimize the amount of required buffer. Definition 7: The problem of RIC is to resolve the internal contention for a composite task ␪ (as defined in Def. 6) while minimizing the amount of required memory. OPTIMIZATION TECHNIQUES In this section, we discuss some techniques to improve the utilization of a continuous media server. First, two techniques to reduce startup latency are explained. Next, three methods to improve the system throughput are described. , composite objects are described earlier).

V W Q S Heat decreases (a) knob = 0 Lemma 2: The optimal value for knob is C/Aug_Slice1. Proof: Let U be the total number of unique objects that are referenced over a period of time. We define Avg Slice1 = X Y U heat(x) ∗ size(Sx,1 ) x ... V W Q S Heat decreases heat(x) 1 = U U Avg Least = knob ∗ Avg Heat ∗ Avg Slice1 Avg Heat = ... x (b) knob = C/Avg_Slicel Figure 20. Status of the first slice of objects. The ideal case is when the LEAST of almost all the objects that constitute the database are disk resident (C is the total number of disk blocks): C ≈ U ∗ Avg Least ≈ U ∗ knob ∗ 1 ∗ Avg Slice1 U Solving for knob, we obtain: knob Ȃ C/Avg_Slice1.

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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