Autonomic Computing, International Conference on (2005)
June 13, 2005 to June 16, 2005
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICAC.2005.4
David Vengerov , Sun Microsystems Laboratories
Nikolai Iakovlev , Sun Microsystems Laboratories
This paper addresses the problem of dynamic resource allocation among multiple entities sharing a common set of resources. A solution approach is presented based on combining the reinforcement learning methodology with function approximation architectures. An implementation of this approach in Solaris 10 demonstrated 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.
D. Vengerov and N. Iakovlev, "A Reinforcement Learning Framework for Dynamic Resource Allocation: First Results.," Autonomic Computing, International Conference on(ICAC), Seattle, Washington, 2005, pp. 339-340.