Article (Scientific journals)
Hopfield neural network for simultaneous job scheduling and data replication in grids
Taheri, Javid; Zomaya, Albert; Bouvry, Pascal et al.
2013In Future Generation Computer Systems, 29 (8), p. 1885-1900
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Keywords :
scheduling; grid computing; data migration
Abstract :
[en] This paper presents a novel heuristic approach, named JDS-HNN, to simultaneously schedule jobs and replicate data files to different entities of a grid system so that the overall makespan of executing all jobs as well as the overall delivery time of all data files to their dependent jobs is concurrently minimized. JDS-HNN is inspired by a natural distribution of a variety of stones among different jars and utilizes a Hopfield Neural Network in one of its optimization stages to achieve its goals. The performance of JDS-HNN has been measured by using several benchmarks varying from medium- to very-large-sized systems. JDS-HNN’s results are compared against the performance of other algorithms to show its superiority under different working conditions. These results also provide invaluable insights into scheduling and replicating dependent jobs and data files as well as their performance related issues for various grid environments.
Disciplines :
Computer science
Author, co-author :
Taheri, Javid;  University of Sydney
Zomaya, Albert
Bouvry, Pascal ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Khan, Samee;  NDSU
External co-authors :
yes
Language :
English
Title :
Hopfield neural network for simultaneous job scheduling and data replication in grids
Publication date :
October 2013
Journal title :
Future Generation Computer Systems
ISSN :
0167-739X
eISSN :
1872-7115
Publisher :
Elsevier Science
Volume :
29
Issue :
8
Pages :
1885-1900
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Security, Reliability and Trust
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since 15 March 2017

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