Speed scaling on parallel processors with migration

Eric Angel (), Evripidis Bampis (), Fadi Kacem () and Dimitrios Letsios ()
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Eric Angel: Université d’Évry Val d’Essonne
Evripidis Bampis: Sorbonne Université
Fadi Kacem: Carthage University
Dimitrios Letsios: Technische Universität München

Journal of Combinatorial Optimization, 2019, vol. 37, issue 4, No 10, 1266-1282

Abstract: Abstract We study the problem of scheduling a set of jobs with release dates, deadlines and processing requirements (or works) on parallel speed scalable processors so as to minimize the total energy consumption. We consider that both preemptions and migrations of jobs are allowed. For this problem, there exists an optimal polynomial-time algorithm which uses as a black box an algorithm for linear programming. Here, we formulate the problem as a convex program and we propose a combinatorial polynomial-time algorithm which is based on finding maximum flows. Our algorithm runs in $$O({ nf}(n)\log U)$$ O ( nf ( n ) log U ) time, where n is the number of jobs, U is the range of all possible values of processors’ speeds divided by the desired accuracy and f(n) is the time needed for computing a maximum flow in a layered graph with O(n) vertices.

Keywords: Energy efficient scheduling; Speed scaling; Network flows; Convex programming (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10878-018-0352-0

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