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DVS scheduling in a line or a star network of processors

  • City University of Hong Kong

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

Abstract

Dynamic Voltage Scaling (DVS) is a technique which allows the processors to change speed when executing jobs. Most of the previous works either study single processor or multiple parallel processors. In this paper, we consider a network of DVS enabled processors. Every job needs to go along a certain path in the network and has a certain workload finished on any processor it goes through before it moves on to the next processor. Our objective is to minimize the total energy consumption while finishing every job before its deadline. Due to the intrinsic complexity of this problem, we only focus on line networks with two nodes and a simple one-level tree network (a star). We show that in some of these simple cases, the optimal schedule can be computed efficiently and interleaving is not needed to achieve optimality. However, in both types of networks, how to find the optimal sequence of execution remains a big challenge for jobs with general workloads.

Original languageEnglish
Title of host publicationComputing and Combinatorics - 19th International Conference, COCOON 2013, Proceedings
Pages101-113
Number of pages13
DOIs
StatePublished - 2013
Externally publishedYes
Event19th International Computing and Combinatorics Conference, COCOON 2013 - Hangzhou, China
Duration: 21 Jun 201321 Jun 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7936 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Computing and Combinatorics Conference, COCOON 2013
Country/TerritoryChina
CityHangzhou
Period21/06/1321/06/13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

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