Using a Sparse Promoting Method in Linear Programming Approximations to Schedule Parallel Jobs.
Résumé
In this paper, we tackle the well‐known problem of scheduling a
collection of parallel jobs on a set of processors either in a cluster
or in a multiprocessor computer. For the makespan objective, that is,
the completion time of the last job, this problem has been shown to be
NP‐hard, and several heuristics have already been proposed to minimize
the execution time. In this paper, we consider both rigid and moldable
jobs. Our main contribution is the introduction of a new approach to the
scheduling problem, based on the recent discoveries in the field of
compressed sensing. In the proposed approach, all possible positions and
shapes of the jobs are encoded into a matrix, and the scheduling is
performed by selecting the best columns under natural constraints. Thus,
the solution to the new scheduling formulation is naturally sparse, and
we may use appropriate relaxations to achieve the optimization task in
the quickest possible way. Among many possible relaxation strategies, we
choose to minimize the
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Soumis le : lundi 10 mai 2021-08:44:32
Dernière modification le : mardi 12 novembre 2024-15:20:06
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Identifiants
- HAL Id : hal-03221858 , version 1
Citer
Stéphane Chrétien, Jean-Marc Nicod, Laurent Philippe, Veronika Sonigo, Lamiel Toch. Using a Sparse Promoting Method in Linear Programming Approximations to Schedule Parallel Jobs.. Concurrency and Computation: Practice and Experience, 2015, 27 (14), pp.3561 - 3586. ⟨hal-03221858⟩
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