Scalable Resource Allocation for Multi-Processor QoS Optimization

Authors:
Sourav Ghosh;Ragunathan (Raj) Rajkumar;Jeffery Hansen;John Lehoczky
Affiliations:
-;-;-;-
Venue:
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Year:
2003

Citing 9
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Abstract

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 atone 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 soas 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. Thefirst is that the existing algorithm is weak in making resourcetrade-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 trade-off decisions. We also introduce a hierarchical decompositionscheme which enables QoS optimization to be performed onproblems with thousands of resources and thousands of tasks.