Evaluation of an Unfair Decider Mechanism for the Self-Tuning dynP Job Scheduler

In this paper we present a new decider mechanism for the self-tuning dynP job scheduler for modern resource management systems. This scheduler switches the active scheduling policy dynamically during run time, in order to reflect changing characteristics of waiting jobs. The new decider explicitly prefers a single scheduling policy instead of being fair to all available policies. We use discrete event simulations to evaluate the achieved slowdown and utilization and compare the results with the fair decider mechanism and the static basic scheduling policies. The evaluation shows, that the self-tuning dynP scheduler in combination with the preferred decider achieves good results and that it is superior to common static scheduling approaches, which use only a single policy