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Impatient task mapping in elastic cloud using genetic algorithm

Problem statement: Task scheduling is the main factor that determines the performance of any distributed system. Cloud computing comes with a paradigm of distributed datacenters. Each datacenter consists of physical machines that host virtual machines to execute customers' tasks. Resources allo...

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Main Authors: Mehdi, Nawfal A., Mamat, Ali, Ibrahim, Hamidah, K. Subramaniam, Shamala
Format: Article
Language:English
Published: Science Publications 2011
Online Access:http://psasir.upm.edu.my/id/eprint/22481/1/jcssp.2011.877.883.pdf
http://psasir.upm.edu.my/id/eprint/22481/
http://thescipub.com/html/10.3844/jcssp.2011.877.883
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spelling my.upm.eprints.224812016-06-10T08:51:32Z http://psasir.upm.edu.my/id/eprint/22481/ Impatient task mapping in elastic cloud using genetic algorithm Mehdi, Nawfal A. Mamat, Ali Ibrahim, Hamidah K. Subramaniam, Shamala Problem statement: Task scheduling is the main factor that determines the performance of any distributed system. Cloud computing comes with a paradigm of distributed datacenters. Each datacenter consists of physical machines that host virtual machines to execute customers' tasks. Resources allocation on the cloud is different from other paradigms and the mapping algorithms need to be adapted to the new characteristics. This study takes the problem of immediate task scheduling under an intercloud infrastructure using a genetic algorithm. An impatient task needs to be scheduled as soon as it enters the system taking into account the input and output files location and its QoS requirements. Approach: This study proposes an algorithm that can find a fast mapping using genetic algorithms with "exist if satisfy" condition to speed up the mapping process and ensures the respecting of all task deadlines. Cloudsim simulator was used to test the proposed algorithm with real datasets collected as a cloud benchmark. Mapping time and makespan are the performance metrics that are used to evaluate the proposed system. Results: The results show an improvement in the proposed system compared to MCT algorithm as illustrated throughout the study. Conclusion: Batch mapping via genetic algorithms with throughput as a fitness function can be used to map jobs to cloud resources. Science Publications 2011 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/22481/1/jcssp.2011.877.883.pdf Mehdi, Nawfal A. and Mamat, Ali and Ibrahim, Hamidah and K. Subramaniam, Shamala (2011) Impatient task mapping in elastic cloud using genetic algorithm. Journal of Computer Science, 7 (6). pp. 877-883. ISSN 1549-3636; ESSN: 1552-6607 http://thescipub.com/html/10.3844/jcssp.2011.877.883 10.3844/jcssp.2011.877.883
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Problem statement: Task scheduling is the main factor that determines the performance of any distributed system. Cloud computing comes with a paradigm of distributed datacenters. Each datacenter consists of physical machines that host virtual machines to execute customers' tasks. Resources allocation on the cloud is different from other paradigms and the mapping algorithms need to be adapted to the new characteristics. This study takes the problem of immediate task scheduling under an intercloud infrastructure using a genetic algorithm. An impatient task needs to be scheduled as soon as it enters the system taking into account the input and output files location and its QoS requirements. Approach: This study proposes an algorithm that can find a fast mapping using genetic algorithms with "exist if satisfy" condition to speed up the mapping process and ensures the respecting of all task deadlines. Cloudsim simulator was used to test the proposed algorithm with real datasets collected as a cloud benchmark. Mapping time and makespan are the performance metrics that are used to evaluate the proposed system. Results: The results show an improvement in the proposed system compared to MCT algorithm as illustrated throughout the study. Conclusion: Batch mapping via genetic algorithms with throughput as a fitness function can be used to map jobs to cloud resources.
format Article
author Mehdi, Nawfal A.
Mamat, Ali
Ibrahim, Hamidah
K. Subramaniam, Shamala
spellingShingle Mehdi, Nawfal A.
Mamat, Ali
Ibrahim, Hamidah
K. Subramaniam, Shamala
Impatient task mapping in elastic cloud using genetic algorithm
author_facet Mehdi, Nawfal A.
Mamat, Ali
Ibrahim, Hamidah
K. Subramaniam, Shamala
author_sort Mehdi, Nawfal A.
title Impatient task mapping in elastic cloud using genetic algorithm
title_short Impatient task mapping in elastic cloud using genetic algorithm
title_full Impatient task mapping in elastic cloud using genetic algorithm
title_fullStr Impatient task mapping in elastic cloud using genetic algorithm
title_full_unstemmed Impatient task mapping in elastic cloud using genetic algorithm
title_sort impatient task mapping in elastic cloud using genetic algorithm
publisher Science Publications
publishDate 2011
url http://psasir.upm.edu.my/id/eprint/22481/1/jcssp.2011.877.883.pdf
http://psasir.upm.edu.my/id/eprint/22481/
http://thescipub.com/html/10.3844/jcssp.2011.877.883
_version_ 1643827840727121920
score 13.211869

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