Maximum-utility scheduling of operation modes with probabilistic task execution times under energy constraints

Wan Yeon Lee, Hyogon Kim, Heejo Lee

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

We propose a novel scheduling scheme that determines the instant operation modes of multiple tasks. The tasks have probabilistic execution times and are executed on discrete operation modes providing different utilities with different energy consumptions. We first design an optimal offline scheduling scheme that stochastically maximizes the cumulative utility of the tasks under energy constraints, at the cost of heavy computational overhead. Next, the optimal offline scheme is modified to an approximate online scheduling scheme. The online scheme has little runtime overhead and yields almost the maximum utility, with an energy budget that is given at runtime. The difference between the maximum utility and the output utility of the online scheme is bounded by a controllable input value. Extensive evaluation shows that the output utility of the online scheme approaches the maximum utility in most cases, and is much higher than that of existing methods by up to 50% of the largest utility difference among available operation modes.

Original languageEnglish
Article number5247147
Pages (from-to)1531-1544
Number of pages14
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Volume28
Issue number10
DOIs
Publication statusPublished - 2009 Oct 1

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Scheduling
Energy utilization
Optimal design

Keywords

  • Approximate scheduling
  • Energy constraint
  • Maximum utility
  • Optimal scheduling
  • Probabilistic execution time

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Software
  • Electrical and Electronic Engineering

Cite this

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abstract = "We propose a novel scheduling scheme that determines the instant operation modes of multiple tasks. The tasks have probabilistic execution times and are executed on discrete operation modes providing different utilities with different energy consumptions. We first design an optimal offline scheduling scheme that stochastically maximizes the cumulative utility of the tasks under energy constraints, at the cost of heavy computational overhead. Next, the optimal offline scheme is modified to an approximate online scheduling scheme. The online scheme has little runtime overhead and yields almost the maximum utility, with an energy budget that is given at runtime. The difference between the maximum utility and the output utility of the online scheme is bounded by a controllable input value. Extensive evaluation shows that the output utility of the online scheme approaches the maximum utility in most cases, and is much higher than that of existing methods by up to 50{\%} of the largest utility difference among available operation modes.",
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