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Enhanced ant colony optimization for grid load balancing

Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources. Stagnation in grid computing system may occur when all jobs are assigned to the same resources which lead to the resources having high workland and longer processing time. Thi...

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Main Authors: Mohamed Din, Aniza, Ku-Mahamud, Ku Ruhana, Abdul Nasir, Husna Jamal
Format: Conference or Workshop Item
Language:English
Published: 2011
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://repo.uum.edu.my/5536/1/Azniza_Mohamed_Din%2C_dll..pdf
http://repo.uum.edu.my/5536/
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spelling my.uum.repo.55362014-09-14T06:29:27Z http://repo.uum.edu.my/5536/ Enhanced ant colony optimization for grid load balancing Mohamed Din, Aniza Ku-Mahamud, Ku Ruhana Abdul Nasir, Husna Jamal QA75 Electronic computers. Computer science Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources. Stagnation in grid computing system may occur when all jobs are assigned to the same resources which lead to the resources having high workland and longer processing time. This paper proposes an Enhanced Ant Colony Optimization (EACO) algorithm for dynamic schedulling and load balancing in a grid computer system. The proposed algorithm can determine the best resource to process a job in order to balance the load among resources in a grid environment. Three new mechanisms are used in organizing the work of an ant colony which are initial pheromone value mechanism, resource selection mechanism and pheromone update mechanism. The initial pheromone value is calculated based on the estimated transmission time and execution time of a given job. Global pheromone update is performed to reduce the pheromone value of resources. A simulation environment was developed to test the performance of the algorithm against another ant based algorithm in terms of resource utilization and to determine how different values of evaporation rate resource utilization. From the experiments, the best evaporation rate value will be determined for a specific number of jobs and resources. 2011 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/5536/1/Azniza_Mohamed_Din%2C_dll..pdf Mohamed Din, Aniza and Ku-Mahamud, Ku Ruhana and Abdul Nasir, Husna Jamal (2011) Enhanced ant colony optimization for grid load balancing. In: International Soft Science Conference 2011 (ISSC 2011), 23-25 November 2011, Ho Chi Minh, Vietnam. (Unpublished)
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Mohamed Din, Aniza
Ku-Mahamud, Ku Ruhana
Abdul Nasir, Husna Jamal
Enhanced ant colony optimization for grid load balancing
description Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources. Stagnation in grid computing system may occur when all jobs are assigned to the same resources which lead to the resources having high workland and longer processing time. This paper proposes an Enhanced Ant Colony Optimization (EACO) algorithm for dynamic schedulling and load balancing in a grid computer system. The proposed algorithm can determine the best resource to process a job in order to balance the load among resources in a grid environment. Three new mechanisms are used in organizing the work of an ant colony which are initial pheromone value mechanism, resource selection mechanism and pheromone update mechanism. The initial pheromone value is calculated based on the estimated transmission time and execution time of a given job. Global pheromone update is performed to reduce the pheromone value of resources. A simulation environment was developed to test the performance of the algorithm against another ant based algorithm in terms of resource utilization and to determine how different values of evaporation rate resource utilization. From the experiments, the best evaporation rate value will be determined for a specific number of jobs and resources.
format Conference or Workshop Item
author Mohamed Din, Aniza
Ku-Mahamud, Ku Ruhana
Abdul Nasir, Husna Jamal
author_facet Mohamed Din, Aniza
Ku-Mahamud, Ku Ruhana
Abdul Nasir, Husna Jamal
author_sort Mohamed Din, Aniza
title Enhanced ant colony optimization for grid load balancing
title_short Enhanced ant colony optimization for grid load balancing
title_full Enhanced ant colony optimization for grid load balancing
title_fullStr Enhanced ant colony optimization for grid load balancing
title_full_unstemmed Enhanced ant colony optimization for grid load balancing
title_sort enhanced ant colony optimization for grid load balancing
publishDate 2011
url http://repo.uum.edu.my/5536/1/Azniza_Mohamed_Din%2C_dll..pdf
http://repo.uum.edu.my/5536/
_version_ 1644279040429785088
score 13.211869

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