Paper Info
Title
Job scheduling using successive linear programming approximations of a sparse model
Abstract
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 integer linear program and use ℓp norm-based operators to force the solver to find almost-integer solutions that can be assimilated to an integer solution. We consider the case where jobs are either rigid or moldable. A rigid parallel job is performed with a predefined number of processors while a moldable job can define the number of processors that it is using just before it starts its execution. We compare the scheduling approach with the classic Largest Task First list based algorithm and we show that our approach provides good results for small instances of the problem. The contributions of this paper are both the integration of mathematical methods in the scheduling world and the design of a promising approach which gives good results for scheduling problems with less than a hundred processors.
Year
DOI
Venue
2012
10.1007/978-3-642-32820-6_14
international conference on parallel processing
Keywords
DocType
Volume
promising approach,rigid parallel job,last job,moldable job,parallel job,sparse model,successive linear programming approximation,novel approach,scheduling approach,job scheduling,well-known problem,good result,scheduling world
Conference
7484
ISSN
Citations 
PageRank 
0302-9743
2
0.40
References 
Authors
15
5
Name
Order
Citations
PageRank
Stephane Chretien1111.65
Jean-Marc Nicod29518.10
Laurent Philippe37112.95
Veronika Rehn-sonigo4629.62
Lamiel Toch5132.40