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Scalable resource allocation for multi-processor QoS optimization

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4 Author(s)
S. Ghosh ; Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA ; R. Rajkumar ; J. Hansen ; J. Lehoczky

We present scalable QoS optimization algorithms for allocating resources to tasks in a multi-processor environment. Given a set of tasks, each of which is capable of running at one of several different QoS levels, the algorithms can select a QoS operating point, the number of replicas for fault-tolerance and the processors on which to run the replicas so as to maximize overall system QoS. The algorithms are extensions of Q-RAM (QoS-based Resource Allocation Model) [5] and fix two deficiencies with the basic algorithm. The first is that the existing algorithm is weak in making resource trade-off decisions such as to which processor to map a task. The second was that it was not scalable to very large numbers of resources such as in a large multi-processor system. In this paper we present two new algorithms which significantly enhance the ability of Q-RAM to make resource tradeoff decisions. We also introduce a hierarchical decomposition scheme which enables QoS optimization to be performed on problems with thousands of resources and thousands of tasks.

Published in:

Distributed Computing Systems, 2003. Proceedings. 23rd International Conference on

Date of Conference:

19-22 May 2003