A learning approach to processor allocation in parallel systems

Authors:
Amy W. Apon;Thomas D. Wagner;Lawrence W. Dowdy
Affiliations:
Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR;Electrical Engineering and Computer Science, United States Military Academy, West Point, NY;Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN
Venue:
Proceedings of the eighth international conference on Information and knowledge management
Year:
1999

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Abstract

Given a typical parallel system and a collection of applications that are to execute on the system, a common problem is determining an effective allocation of processors among the applications. In this paper a learning approach is applied to processor allocation. The approach is to use a stochastic learning automaton (SLA) as a decision tool. An SLA uses values of the current state description, makes an allocation decision, evaluates its decision at some later time, modifies its decision making process, and tries to find the best allocation strategy by learning from its previous mistakes. The method is applied to the problem of allocating processors to parallel applications in a distributed system such as a cluster of workstations, and is validated through simulation. The result of this study show that a learning approach that utilizes a stochastic learning automaton is effective at making processor allocation decisions in a parallel system.