Workstation capacity tuning using reinforcement learning
Aharon Bar-Hillel, Amir Di-Nur, Liat Ein‐Dor, Ran Gilad-Bachrach, Yossi Ittach · 2007
Computer grids are complex, heterogeneous, and dynamic systems, whose behavior is governed by hundreds of manually-tuned parameters. As the complexity of these systems grows, automating the procedure of parameter tuning becomes indispensable. In this paper, we consider the problem of auto-tuning 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.