Workstation capacity tuning using reinforcement learning

Computer grids are complex, heterogeneous, and dynamic systems, whose behavior is governed by hundreds of manuallytuned parameters. As the complexity of these systems grows, automating the procedure of parameter tuning becomes indispensable. fn this paper, we consider the problem of autotuning server capacity, i.e. the number of jobs a server runs in parallel. We present three different reinforcement learning algorithms, which generate a dynamic policy by changing the number of concurrent running jobs according to the job types and machine state. The algorithms outperform manually-tuned policies for the entire range of checked workloads, with average throughput improvement greater than 20%. On multi-core servers, the average throughput improvement is approximately 40%, which hints at the enormous improvement potential of such a tuning mechanism with the gradual transition to multi-core machines. (c) 2007 ACM.