cop5615fa11quiz4key

cop5615fa11quiz4key - If there are more than two processors...

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Quiz 4 COP 5615 Fall 2011 1) Consider the real-time task set V={(2,10),(1,4),(1,3),(1,5)} a) Is V feasible under static priority scheduling? Prove or disprove. b) Is V feasible under dynamic priority scheduling? Prove or disprove. 2) a) In the Process Communication Model heuristic using min-cut approach, why does the approach not scale to more than two processors? Explain. b) Sketch a way adapt the min-cut method to more than two processors. Justify your extension. Where will it fail? KEY 1) a) V is not feasible, since it fails using RMS. This may be shown by the critical instant of (2,10) failing, or by iterating on the response time calculation for (2,10). b) V is feasible with dynamic scheduling, which may be shown by the load L = 1/3 + ¼ + 1/5 + 2/10 < 1, or by showing that (2,10) succeeds at its critical instant. 2) a) For the min-cut cost to reflect the cost of running a task T on a processor P, the edge from T to P should reflect the cost of NOT running T on P.
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Unformatted text preview: If there are more than two processors, and the value of running P on these are not equal, then the cost for this edge is ambiguous. The costs of the severed edges should reflect the cost of running on one processor, but this severing one edge does not define which processor this is. Moreover, with more than two processors, the graph would have to be partitioned into as many components as there are processors, which is beyond basic min-cut. b) An attempt to extend this heuristic would be to have the T-P edge cost equal to 1/(N-1) of the average (or max or min) cost of running T on the other processors (not P). The scaling is needed since N-1 edges from T to the processors will be included in the N-way partition. This approach is likely to overestimate the processing costs (esp. if the cost is infinite for T on more than one processor) or underestimate them (if the min is used)....
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This note was uploaded on 01/18/2012 for the course COP 5615 taught by Professor Staff during the Fall '08 term at University of Florida.

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