Greedy Scheduling with Complex Obejectives

We present a methodology for automatically generating an online scheduling process for an arbitrary objective with the help of evolution strategies. The scheduling problem comprises independent parallel jobs and multiple identical machines and occurs in many real massively parallel processing systems. The system owner defines the objective that may consider job waiting times and priorities of user groups. Our scheduling process is a variant of the simple and commonly used greedy scheduling algorithm in combination with a repeated sorting of the waiting queue. This sorting uses a criterion whose parameters are evolutionary optimized. We evaluate our new scheduling process with real workload data and compare it to the best offline solutions and to the online results of the standard EASY backfill algorithm. To this end, we partition the user of the workloads into groups and select an exemplary objective that prioritizes some of those groups over others