Parallel Models and Job Characterization for System Scheduling
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Non-clair voy ant multiprocessor scheduling of jobs with changing execution characteristics
Journal of Scheduling - Special issue: On-line scheduling
Fair Scheduling Algorithms in Grids
IEEE Transactions on Parallel and Distributed Systems
Using application information to drive adaptive grid middleware scheduling decisions
Proceedings of the 2nd workshop on Middleware-application interaction: affiliated with the DisCoTec federated conferences 2008
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We study the problem of processor scheduling for n parallel jobs applying the method of competitive analysis. We prove that for jobs with a single phase of parallelism, a preemptive scheduling algorithm without information about job execution time can achieve a mean completion time within $2-{2\over n+1}$ times the optimum. In other words, we prove a competitive ratio of $2-{2\over n+1}$. The result is extended to jobs with multiple phases of parallelism (which can be used to model jobs with sublinear speedup) and to interactive jobs (with phases during which the job has no CPU requirements) to derive solutions guaranteed to be within $4-{4\over n+1}$ times the optimum. In comparison with previous work, our assumption that job execution times are unknown prior to their completion is more realistic, our multiphased job model is more general, and our approximation ratio (for jobs with a single phase of parallelism) is tighter and cannot be improved. While this work presents theoretical results obtained using competitive analysis, we believe that the results provide insight into the performance of practical multiprocessor scheduling algorithms that operate in the absence of complete information.