A Fully Polynomial-Time Approximation Scheme for Speed Scaling with Sleep State

Antonios Antoniadis, Chien-Chung Huang, Sebastian Ott

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

21 Citations (Scopus)

Abstract

We study classical deadline-based preemptive scheduling of jobs in a computing environment equipped with both dynamic speed scaling and sleep state capabilities: Each job is specified by a release time, a deadline and a processing volume, and has to be scheduled on a single, speed-scalable processor that is supplied with a sleep state. In the sleep state, the processor consumes no energy, but a constant wake-up cost is required to transition back to the active state. In contrast to speed scaling alone, the addition of a sleep state makes it sometimes beneficial to accelerate the processing of jobs in order to transition the processor to the sleep state for longer amounts of time and incur further energy savings. The goal is to output a feasible schedule that minimizes the energy consumption. Since the introduction of the problem by Irani et al. [17], its exact computational complexity has been repeatedly posed as an open question (see e.g. [2,9,16]). The currently best known upper and lower bounds are a 4/3-approximation algorithm and NP-hardness due to [2] and [2,18], respectively.

We close the aforementioned gap between the upper and lower bound on the computational complexity of speed scaling with sleep state by presenting a fully polynomial-time approximation scheme for the problem. The scheme is based on a transformation to a non-preemptive variant of the problem, and a discretization that exploits a carefully defined lexicographical ordering among schedules.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Sixth Annual Symposium on Discrete Algorithms 2015
EditorsPiotr Indyk
PublisherSIAM
Number of pages12
ISBN (Electronic)978-1-61197-373-0
ISBN (Print)978-1-61197-374-7
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event26th Annual ACM-SIAM Symposium on Discrete Algorithms 2015 - San Diego, United States
Duration: 4 Jan 20156 Jan 2015
Conference number: 26

Conference

Conference26th Annual ACM-SIAM Symposium on Discrete Algorithms 2015
CountryUnited States
CitySan Diego
Period4/01/156/01/15

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