Speed scaling on parallel processors

Susanne Albers, Fabian Müller, Swen Schmelzer

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

In this paper we investigate dynamic speed scaling, a technique to reduce energy consumption in variable-speed microprocessors. While prior research has focused mostly on single processor environments, in this paper we investigate multiprocessor settings. We study the basic problem of scheduling a set of jobs, each specified by a release date, a deadline and a processing volume, on variable-speed processors so as to minimize the total energy consumption. We first settle the problem complexity if unit size jobs have to be scheduled. More specifically, we devise a polynomial time algorithm for jobs with agreeable deadlines and prove NP-hardness results if jobs have arbitrary deadlines. For the latter setting we also develop a polynomial time algorithm achieving a constant factor approximation guarantee. Additionally, we study problem settings where jobs have arbitrary processing requirements and, again, develop constant factor approximation algorithms. We finally transform our offline algorithms into constant competitive online strategies.

Original languageEnglish
Pages (from-to)404-425
Number of pages22
JournalAlgorithmica
Volume68
Issue number2
DOIs
StatePublished - Feb 2014
Externally publishedYes

Keywords

  • Approximation algorithm
  • Dynamic speed scaling
  • Energy efficiency
  • NP-hardness
  • Online algorithm
  • Scheduling

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