A Reinforcement Learning Framework for Dynamic Resource Allocation: First Results.

Authors:
David Vengerov;Nikolai Iakovlev
Affiliations:
Sun Microsystems Laboratories;Sun Microsystems Laboratories
Venue:
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Year:
2005

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Abstract

This paper addresses the problem of dynamic resource allocation among multiple entities sharing a common setof resources. A solution approach is presented based on combining the reinforcement learning methodology with function approximation architectures. An implementation of this approach in Solaris 10demonstrated a robust near-optimal performance on a simple problem of transferring CPUs among resource partitions so as to match the stochastically changing workload in each partition, both for large and small CPU migration costs.