Adaptive intelligent grid scheduling system

Grid technologies are established to share the large-scale heterogeneous resources over multiple administrative domains for processing the application. In these technologies, the grid scheduling problem is crucial that must be solved in order to achieve multiple objectives within different stakehold...

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Main Author:	Lorpunmanee, Siriluck
Format:	Thesis
Language:	English
Published:	2010
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/18741/20/SiriluckLorpunmaneePFSKSM2010.pdf
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id	my-utm-ep.18741
record_format	uketd_dc
spelling	my-utm-ep.187412017-09-13T01:14:02Z Adaptive intelligent grid scheduling system 2010 Lorpunmanee, Siriluck QA75 Electronic computers. Computer science Grid technologies are established to share the large-scale heterogeneous resources over multiple administrative domains for processing the application. In these technologies, the grid scheduling problem is crucial that must be solved in order to achieve multiple objectives within different stakeholders (end-users, owner resources and administrators) preferences. The aim of this research is to design and implement the Adaptive Intelligent Grid Scheduling System (AIGSS) in order to achieve multiple objectives named Makespan Time, Grid Efficiency and Total delayed jobs. The popular meta-heuristic algorithms, namely Ant Colony Optimization (ACO) and Tabu Search (TS) algorithms are proposed and developed to maintain the selecting appropriate grid resource to execute each job within the different job inter-arrival times and grid resources. Additionally, the clustering technique named Fuzzy C-Means (FCM) algorithm is proposed for clustering the groups of grid resources as well as jobs based on the degree of characteristic similarity. Moreover, a popular discrete event simulation tool, namely, GridSim toolkit and Alea simulation, is extended by developing the service modules on top of it. Therefore, the experiment is simulated as realistic grid environment in order to measure the proposed system. The experimental results show that the AIGSS provides reasonable multiple objectives to stakeholders within different job interarrival times and machines in grid system. In addition to the experimental results, the proposed system performs better than the other algorithms for different goals of each stakeholder. The performance of AIGSS is compared with the common and heuristic algorithms such as First-Come-First-Serve (FCFS) with Optimization, Earliest Deadline First (EDF), Minimum Tardiness Earliest Due Date (MTEDD), Minimum Completion Time (MCT), Opportunistic Load Balancing (OLB), MIN-MIN, Hill Climbing, EASY Backfilling, Simulated Annealing (SA), and Tabu Searching (TS). 2010 Thesis http://eprints.utm.my/id/eprint/18741/ http://eprints.utm.my/id/eprint/18741/20/SiriluckLorpunmaneePFSKSM2010.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Lorpunmanee, Siriluck Adaptive intelligent grid scheduling system
description	Grid technologies are established to share the large-scale heterogeneous resources over multiple administrative domains for processing the application. In these technologies, the grid scheduling problem is crucial that must be solved in order to achieve multiple objectives within different stakeholders (end-users, owner resources and administrators) preferences. The aim of this research is to design and implement the Adaptive Intelligent Grid Scheduling System (AIGSS) in order to achieve multiple objectives named Makespan Time, Grid Efficiency and Total delayed jobs. The popular meta-heuristic algorithms, namely Ant Colony Optimization (ACO) and Tabu Search (TS) algorithms are proposed and developed to maintain the selecting appropriate grid resource to execute each job within the different job inter-arrival times and grid resources. Additionally, the clustering technique named Fuzzy C-Means (FCM) algorithm is proposed for clustering the groups of grid resources as well as jobs based on the degree of characteristic similarity. Moreover, a popular discrete event simulation tool, namely, GridSim toolkit and Alea simulation, is extended by developing the service modules on top of it. Therefore, the experiment is simulated as realistic grid environment in order to measure the proposed system. The experimental results show that the AIGSS provides reasonable multiple objectives to stakeholders within different job interarrival times and machines in grid system. In addition to the experimental results, the proposed system performs better than the other algorithms for different goals of each stakeholder. The performance of AIGSS is compared with the common and heuristic algorithms such as First-Come-First-Serve (FCFS) with Optimization, Earliest Deadline First (EDF), Minimum Tardiness Earliest Due Date (MTEDD), Minimum Completion Time (MCT), Opportunistic Load Balancing (OLB), MIN-MIN, Hill Climbing, EASY Backfilling, Simulated Annealing (SA), and Tabu Searching (TS).
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Lorpunmanee, Siriluck
author_facet	Lorpunmanee, Siriluck
author_sort	Lorpunmanee, Siriluck
title	Adaptive intelligent grid scheduling system
title_short	Adaptive intelligent grid scheduling system
title_full	Adaptive intelligent grid scheduling system
title_fullStr	Adaptive intelligent grid scheduling system
title_full_unstemmed	Adaptive intelligent grid scheduling system
title_sort	adaptive intelligent grid scheduling system
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department	Faculty of Computer Science and Information System
publishDate	2010
url	http://eprints.utm.my/id/eprint/18741/20/SiriluckLorpunmaneePFSKSM2010.pdf
_version_	1747815347498516480

Adaptive intelligent grid scheduling system

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