In this paper, we present an experimental study of deterministic non-preemptive multiple workflow scheduling strategies on a Grid. We distinguish twenty five strategies depending on the type and amount of information they require. We analyze scheduling strategies that consist of two and four stages: labeling, adaptive allocation, prioritization, and parallel machine scheduling. We apply these strategies in the context of executing the Cybershake, Epigenomics, Genome, Inspiral, LIGO, Montage, and SIPHT workflows applications. In order to provide performance comparison, we performed a joint analysis considering three metrics. A case study is given and corresponding results indicate that well known DAG scheduling algorithms designed for single DAG and single machine settings are not well suited for Grid scheduling scenarios, where user run time estimates are available. We show that the proposed new strategies outperform other strategies in terms of approximation factor, mean critical path waiting time, and critical path slowdown. The robustness of these strategies is also discussed.

We address non-preemptive nonclairvoyant online scheduling of parallel jobs on a Grid. We consider a Grid scheduling model with two stages. At the first stage, jobs are allocated to a suitable Grid site, while at the second stage, local scheduling is independently applied to each site. We analyze allocation strategies depending on the type and amount of information they require. We conduct a comprehensive performance evaluation study using simulation and demonstrate that our strategies perform well with respect to several metrics that reflect both user-and system-centric goals. Unfortunately, user run time estimates and information on local schedules does not help to significantly improve the outcome of the allocation strategies. When examining the overall Grid performance based on real data, we determined that an appropriate distribution of job processor requirements over the Grid has a higher performance than an allocation of jobs based on user run time estimates and information on local schedules. In general, our experiments showed that rather simple schedulers with minimal information requirements can provide a good performance.

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