The greedy multi-cluster scheduler: performance bounds and parametric sensitivity

Abstract

Most schedulers in parallel job scheduling do not put (job) schedulability into consideration when prioritizing jobs. Performance evaluation is mostly done using average values of the measurement metric. Using the average metric value may conceal relative job starvation details hence giving a shallower understanding of scheduler performance. We propose a greedy multi-cluster scheduler that uses the (estimate of) job schedulability and the time a job has spent in the queue to compute its priority.We compare the performance of our scheduler with that of Fit Processor First Served (FPFS) scheduler. We also study the sensitivity of its performance to parameter changes. We observe that (i) within some parameter ranges, our scheduler outperforms FPFS; (ii) for big jobs, our scheduler outperforms FPFS; for small jobs, FPFS outperforms our scheduler and (iii) our scheduler is fairer than FPFS.