Provably Efficient Online Non-clairvoyant Adaptive Scheduling

Scheduling competing jobs on multiprocessors has always been an important issue for parallel and distributed systems. The challenge is to ensure global, system-wide efficiency while offering a level of fairness to user jobs. Various degrees of successes have been achieved over years. However, few existing schemes address both efficiency and fairness over a wide range of work loads. Moreover, in order to obtain analytical results, most of them require prior information about jobs, which may be difficult to obtain in real applications. This paper presents a novel adaptive scheduling algorithm GRAD that ensures fair allocation under all levels of workload, and it offers provable efficiency without requiring prior information of job's parallelism. Moreover, it provides effective control over the scheduling overhead and ensures efficient utilization of processors. Specifically, we show that GRAD is O(1)-competitive against an optimal offline scheduling algorithm with respect to both mean response time and makespan for batched jobs and non-batched jobs respectively. To the best of our knowledge, GRAD is the first non-clairvoyant scheduling algorithm that offers such guarantees. We also believe that our new approach of resource request-allotment protocol deserves further exploration. The simulation results show that, for non-batched jobs, the makespan produced by GRAD is no more than 1.39 times of the optimal on average. For batched jobs, the mean response time produced by GRAD is no more than 2.37 times of the optimal on average.

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