Improving First-Come-First-Serve Job Scheduling by Gang Scheduling

We present a new scheduling method for batch jobs on massively parallel processor architectures. This method is based on the First-come-first-serve strategy and emphasizes the notion of fairness. Severe fragmentation is prevented by using gang scheduling which is only initiated by highly parallel jobs. Good worst-case behavior of the scheduling approach has already been proven by theoretical analysis. In this paper we show by simulation with real workload data that the algorithm is also suitable to be applied in real parallel computers. This holds for several different scheduling criteria like makespan or sum of the flow times. Simulation is also used for determination of the best parameter set for the new method.

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