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Scheduling multi-task jobs with extra utility in data centers

This paper investigates the problem of maximizing utility for job scheduling where each job consists of multiple tasks, each task has utility and each job also has extra utility if all tasks of that job are completed. We provide a 2-approximation algorithm for the single-machine case and a 2-approxi...

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Detalles Bibliográficos
Autores principales: Fang, Xiaolin, Luo, Junzhou, Gao, Hong, Wu, Weiwei, Li, Yingshu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5701962/
https://www.ncbi.nlm.nih.gov/pubmed/29213279
http://dx.doi.org/10.1186/s13638-017-0986-0
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author Fang, Xiaolin
Luo, Junzhou
Gao, Hong
Wu, Weiwei
Li, Yingshu
author_facet Fang, Xiaolin
Luo, Junzhou
Gao, Hong
Wu, Weiwei
Li, Yingshu
author_sort Fang, Xiaolin
collection PubMed
description This paper investigates the problem of maximizing utility for job scheduling where each job consists of multiple tasks, each task has utility and each job also has extra utility if all tasks of that job are completed. We provide a 2-approximation algorithm for the single-machine case and a 2-approximation algorithm for the multi-machine problem. Both algorithms include two steps. The first step employs the Earliest Deadline First method to compute utility with only extra job utility, and it is proved that it obtains the optimal result for this sub-problem. The second step employs a Dynamic Programming method to compute utility without extra job utility, and it also derives the optimal result. An approximation result can then be obtained by combining the results of the two steps.
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spelling pubmed-57019622017-12-04 Scheduling multi-task jobs with extra utility in data centers Fang, Xiaolin Luo, Junzhou Gao, Hong Wu, Weiwei Li, Yingshu EURASIP J Wirel Commun Netw Research This paper investigates the problem of maximizing utility for job scheduling where each job consists of multiple tasks, each task has utility and each job also has extra utility if all tasks of that job are completed. We provide a 2-approximation algorithm for the single-machine case and a 2-approximation algorithm for the multi-machine problem. Both algorithms include two steps. The first step employs the Earliest Deadline First method to compute utility with only extra job utility, and it is proved that it obtains the optimal result for this sub-problem. The second step employs a Dynamic Programming method to compute utility without extra job utility, and it also derives the optimal result. An approximation result can then be obtained by combining the results of the two steps. Springer International Publishing 2017-11-25 2017 /pmc/articles/PMC5701962/ /pubmed/29213279 http://dx.doi.org/10.1186/s13638-017-0986-0 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Fang, Xiaolin
Luo, Junzhou
Gao, Hong
Wu, Weiwei
Li, Yingshu
Scheduling multi-task jobs with extra utility in data centers
title Scheduling multi-task jobs with extra utility in data centers
title_full Scheduling multi-task jobs with extra utility in data centers
title_fullStr Scheduling multi-task jobs with extra utility in data centers
title_full_unstemmed Scheduling multi-task jobs with extra utility in data centers
title_short Scheduling multi-task jobs with extra utility in data centers
title_sort scheduling multi-task jobs with extra utility in data centers
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5701962/
https://www.ncbi.nlm.nih.gov/pubmed/29213279
http://dx.doi.org/10.1186/s13638-017-0986-0
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