A prediction module to optimize scheduling in a grid computing environment

Heterogeneous computing environment such as grid computing allows sharing and aggregation of a wide variety of geographically distributed computational resources (such as supercomputers, clusters, data sources, people and storage systems) and present them as a single, unified resource for solvi...

Full description

Bibliographic Details
Main Authors:	Maleeha , Kiran, Hassan Abdalla Hashim, Aisha, Yap Yee, Jiun, Lim Mei , Kuan
Format:	Conference or Workshop Item
Language:	English
Published:	IEEE 2008
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://irep.iium.edu.my/50162/ http://irep.iium.edu.my/50162/ http://irep.iium.edu.my/50162/ http://irep.iium.edu.my/50162/1/atrizedcopy.pdf

id	iium-50162
recordtype	eprints
spelling	iium-501622016-07-20T05:46:55Z http://irep.iium.edu.my/50162/ A prediction module to optimize scheduling in a grid computing environment Maleeha , Kiran Hassan Abdalla Hashim, Aisha Yap Yee, Jiun Lim Mei , Kuan TK Electrical engineering. Electronics Nuclear engineering Heterogeneous computing environment such as grid computing allows sharing and aggregation of a wide variety of geographically distributed computational resources (such as supercomputers, clusters, data sources, people and storage systems) and present them as a single, unified resource for solving large-scale and data-intensive computing applications. A common problem arising in grid computing is to select the most efficient resource to run a particular program. Also users are required to reserve in advance the resources needed to run their program on the grid. At present the execution time of any program submission depends on guesswork by the user. This leads to inefficient use of resources, incurring extra operation costs such as idling queues or machines. Thus a prediction module was designed and developed to aid the user. This module estimates the execution time of a program by using aspects of static analysis, analytical benchmarking and compiler based approach. It consists of 4 main stages; each with its own functionality. An incoming program is categorized accordingly, parsed and then broken down into smaller units known as tokens. The complexity and relationship amongst these tokens are then analyzed and finally the execution time is estimated for the entire program that was submitted. IEEE 2008-05-13 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/50162/1/atrizedcopy.pdf Maleeha , Kiran and Hassan Abdalla Hashim, Aisha and Yap Yee, Jiun and Lim Mei , Kuan (2008) A prediction module to optimize scheduling in a grid computing environment. In: Proceedings of the International Conference on Computer and Communication Engineering 2008 May 13-15, 2008 Kuala Lumpur, Malaysia, May 13-15, 2008 , Kuala Lumpur, Malaysia. http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4580733&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D4580733 10.1109/ICCCE.2008.4580733
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Maleeha , Kiran Hassan Abdalla Hashim, Aisha Yap Yee, Jiun Lim Mei , Kuan A prediction module to optimize scheduling in a grid computing environment
description	Heterogeneous computing environment such as grid computing allows sharing and aggregation of a wide variety of geographically distributed computational resources (such as supercomputers, clusters, data sources, people and storage systems) and present them as a single, unified resource for solving large-scale and data-intensive computing applications. A common problem arising in grid computing is to select the most efficient resource to run a particular program. Also users are required to reserve in advance the resources needed to run their program on the grid. At present the execution time of any program submission depends on guesswork by the user. This leads to inefficient use of resources, incurring extra operation costs such as idling queues or machines. Thus a prediction module was designed and developed to aid the user. This module estimates the execution time of a program by using aspects of static analysis, analytical benchmarking and compiler based approach. It consists of 4 main stages; each with its own functionality. An incoming program is categorized accordingly, parsed and then broken down into smaller units known as tokens. The complexity and relationship amongst these tokens are then analyzed and finally the execution time is estimated for the entire program that was submitted.
format	Conference or Workshop Item
author	Maleeha , Kiran Hassan Abdalla Hashim, Aisha Yap Yee, Jiun Lim Mei , Kuan
author_facet	Maleeha , Kiran Hassan Abdalla Hashim, Aisha Yap Yee, Jiun Lim Mei , Kuan
author_sort	Maleeha , Kiran
title	A prediction module to optimize scheduling in a grid computing environment
title_short	A prediction module to optimize scheduling in a grid computing environment
title_full	A prediction module to optimize scheduling in a grid computing environment
title_fullStr	A prediction module to optimize scheduling in a grid computing environment
title_full_unstemmed	A prediction module to optimize scheduling in a grid computing environment
title_sort	prediction module to optimize scheduling in a grid computing environment
publisher	IEEE
publishDate	2008
url	http://irep.iium.edu.my/50162/ http://irep.iium.edu.my/50162/ http://irep.iium.edu.my/50162/ http://irep.iium.edu.my/50162/1/atrizedcopy.pdf
first_indexed	2023-09-18T21:10:52Z
last_indexed	2023-09-18T21:10:52Z
_version_	1777411243906170880

A prediction module to optimize scheduling in a grid computing environment

Similar Items