article

An Adaptive Scoring Job Scheduling algorithm for grid computing

Authors:

Ruay-Shiung Chang,

Chun-Fu LinAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 207

Pages 79 - 89

https://doi.org/10.1016/j.ins.2012.04.019

Published: 01 November 2012 Publication History

Abstract

When human culture advances, current problems in science and engineering become more complicated and need more computing power to tackle and analyze. A supercomputer is not the only choice for solving complex problems any more as a result of the speed-up of personal computers and networks. Grid technology, which connects a number of personal computer clusters with high speed networks, can achieve the same computing power as a supercomputer does, also with a lower cost. However, grid is a heterogeneous system. Scheduling independent tasks on it is more complicated. In order to utilize the power of grid completely, we need an efficient job scheduling algorithm to assign jobs to resources in a grid. In this paper, we propose an Adaptive Scoring Job Scheduling algorithm (ASJS) for the grid environment. Compared to other methods, it can decrease the completion time of submitted jobs, which may compose of computing-intensive jobs and data-intensive jobs.

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Cited By

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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 207, Issue

November, 2012

98 pages

ISSN:0020-0255

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Copyright © Elsevier Inc. © 2012.

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Elsevier Science Inc.

United States

Publication History

Published: 01 November 2012

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Cited By

Ankita Sahana S(2022)Ba-PSO: A Balanced PSO to solve multi-objective grid scheduling problemApplied Intelligence10.1007/s10489-021-02625-752:4(4015-4027)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10489-021-02625-7
Juneja K(2018)An Intelligent GbMFPA Model for Sales Optimization in Distributed Grid-MarketWireless Personal Communications: An International Journal10.1007/s11277-018-5918-8103:3(2403-2421)Online publication date: 1-Dec-2018
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Khan SNazir BAhmed Khan IShamshirband SChronopoulos A(2017)Load balancing in grid computingJournal of Network and Computer Applications10.1016/j.jnca.2017.02.01388:C(99-111)Online publication date: 15-Jun-2017
https://dl.acm.org/doi/10.1016/j.jnca.2017.02.013
Peng JChen JZhi XQiu MXie X(2017)Research on application classification method in cloud computing environmentThe Journal of Supercomputing10.1007/s11227-016-1663-573:8(3488-3507)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1007/s11227-016-1663-5
Kim SChoi JKim Y(2016)Adaptive application-aware job scheduling optimization strategy in heterogeneous infrastructuresCluster Computing10.1007/s10586-016-0588-319:3(1515-1526)Online publication date: 1-Sep-2016
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Zhang LLi KXu YMei JZhang FLi K(2015)Maximizing reliability with energy conservation for parallel task scheduling in a heterogeneous clusterInformation Sciences: an International Journal10.5555/2794084.2794133319:C(113-131)Online publication date: 20-Oct-2015
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Wenzhong Guo Jie Li Guolong Chen Yuzhen Niu Chengyu Chen (2015)A PSO-Optimized Real-Time Fault-Tolerant Task Allocation Algorithm in Wireless Sensor NetworksIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2014.238634326:12(3236-3249)Online publication date: 1-Dec-2015
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Chen HGuo W(2015)Real-Time Task Scheduling Algorithm for Cloud Computing Based on Particle Swarm OptimizationRevised Selected Papers of the Second International Conference on Cloud Computing and Big Data - Volume 910610.1007/978-3-319-28430-9_11(141-152)Online publication date: 17-Jun-2015
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