Increasing waiting time satisfaction in parallel job scheduling via a flexible MILP approach
Summary form only given. We present a comparison of CPLEX-computed job schedules with the self-tuning dynP scheduler. This scheduler switches the active scheduling policy dynamically during run time, in order to reject changing characteristics of waiting jobs. Each times the self-tuning dynP scheduler checks for a new policy a quasi offline scheduling is done as the numbers of jobs are fixed. Two questions arise from this fact: what is the optimal schedule in each self-tuning step? And what is the performance difference between the optimal schedule and the best schedule generated with one of the scheduling policies? For that we model the scheduling problem as an integer problem, which is then solved with the well-known CPLEX library. Due to the size of the problem, we apply time-scaling, i.e. the schedule is computed on a larger than one second precise scale. We use the CTC job trace as input for a discrete event simulation and evaluate the performance difference between the CPLEX-computed schedules and the schedules generated by the self-tuning dynP scheduler. The results show, that the performance of the self-tuning dynP scheduler is close to solutions computed by CPLEX. However, the self-tuning dynP scheduler needs much less time for generating the schedules than CPLEX.