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Job Scheduling Using Successive Linear Programming Approximations of a Sparse Model
[chapter]
2012
Lecture Notes in Computer Science
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, i.e., 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. We introduce a novel approach based on successive linear programming (LP) approximations of a sparse model. The idea is to relax an
doi:10.1007/978-3-642-32820-6_14
fatcat:lq4ev4yysvaczm7tu3dwfjzmmu