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A High Performance Load Balance Strategy for Real-Time Multicore Systems

Finding ways to distribute workloads to each processor core and efficiently reduce power consumption is of vital importance, especially for real-time systems. In this paper, a novel scheduling algorithm is proposed for real-time multicore systems to balance the computation loads and save power. The...

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Detalles Bibliográficos
Autores principales: Cho, Keng-Mao, Tsai, Chun-Wei, Chiu, Yi-Shiuan, Yang, Chu-Sing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4009124/
https://www.ncbi.nlm.nih.gov/pubmed/24955382
http://dx.doi.org/10.1155/2014/101529
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author Cho, Keng-Mao
Tsai, Chun-Wei
Chiu, Yi-Shiuan
Yang, Chu-Sing
author_facet Cho, Keng-Mao
Tsai, Chun-Wei
Chiu, Yi-Shiuan
Yang, Chu-Sing
author_sort Cho, Keng-Mao
collection PubMed
description Finding ways to distribute workloads to each processor core and efficiently reduce power consumption is of vital importance, especially for real-time systems. In this paper, a novel scheduling algorithm is proposed for real-time multicore systems to balance the computation loads and save power. The developed algorithm simultaneously considers multiple criteria, a novel factor, and task deadline, and is called power and deadline-aware multicore scheduling (PDAMS). Experiment results show that the proposed algorithm can greatly reduce energy consumption by up to 54.2% and the deadline times missed, as compared to the other scheduling algorithms outlined in this paper.
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spelling pubmed-40091242014-06-22 A High Performance Load Balance Strategy for Real-Time Multicore Systems Cho, Keng-Mao Tsai, Chun-Wei Chiu, Yi-Shiuan Yang, Chu-Sing ScientificWorldJournal Research Article Finding ways to distribute workloads to each processor core and efficiently reduce power consumption is of vital importance, especially for real-time systems. In this paper, a novel scheduling algorithm is proposed for real-time multicore systems to balance the computation loads and save power. The developed algorithm simultaneously considers multiple criteria, a novel factor, and task deadline, and is called power and deadline-aware multicore scheduling (PDAMS). Experiment results show that the proposed algorithm can greatly reduce energy consumption by up to 54.2% and the deadline times missed, as compared to the other scheduling algorithms outlined in this paper. Hindawi Publishing Corporation 2014 2014-04-14 /pmc/articles/PMC4009124/ /pubmed/24955382 http://dx.doi.org/10.1155/2014/101529 Text en Copyright © 2014 Keng-Mao Cho et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Cho, Keng-Mao
Tsai, Chun-Wei
Chiu, Yi-Shiuan
Yang, Chu-Sing
A High Performance Load Balance Strategy for Real-Time Multicore Systems
title A High Performance Load Balance Strategy for Real-Time Multicore Systems
title_full A High Performance Load Balance Strategy for Real-Time Multicore Systems
title_fullStr A High Performance Load Balance Strategy for Real-Time Multicore Systems
title_full_unstemmed A High Performance Load Balance Strategy for Real-Time Multicore Systems
title_short A High Performance Load Balance Strategy for Real-Time Multicore Systems
title_sort high performance load balance strategy for real-time multicore systems
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4009124/
https://www.ncbi.nlm.nih.gov/pubmed/24955382
http://dx.doi.org/10.1155/2014/101529
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