Application of Global and One-Dimensional Local Optimization to Operating System Scheduler Tuning

TL;DR

This study compares global and local optimization methods for tuning the Linux scheduler, finding that global methods achieve better responses while local methods converge faster.

Contribution

It introduces a comparison of Particle Swarm Optimization and Golden Section methods for scheduler tuning, with a novel conversion of the problem to a single parameter.

Findings

Global optimization yields better scheduler performance.

Local optimization converges faster.

Conversion to single parameter simplifies the tuning process.

Abstract

This paper describes a study of comparison of global and one-dimensional local optimization methods to operating system scheduler tuning. The operating system scheduler we use is the Linux 2.6.23 Completely Fair Scheduler (CFS) running in simulator (LinSched). We have ported the Hackbench scheduler benchmark to this simulator and use this as the workload. The global optimization approach we use is Particle Swarm Optimization (PSO). We make use of Response Surface Methodology (RSM) to specify optimal parameters for our PSO implementation. The one-dimensional local optimization approach we use is the Golden Section method. In order to use this approach, we convert the scheduler tuning problem from one involving setting of three parameters to one involving the manipulation of one parameter. Our results show that the global optimization approach yields better response but the one- dimensional optimization approach converges to a solution faster than the global optimization approach.