Execution time prediction of imperative paradigm tasks for grid scheduling optimization

An efficient functioning of a complicated and dynamic grid environment requires a resource manager to monitor and identify the idling resources and to schedule users’ submitted jobs (or programs) accordingly. A common problem arising in grid computing is to select the most efficient resource to run...

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Main Authors:	Kiran, Maleeha, Hassan Abdalla Hashim, Aisha, Lim, Mei Kuan, Yap, Yee Jiun
Format:	Article
Language:	English
Published:	IJCSNS 2009
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/1359/ http://irep.iium.edu.my/1359/ http://irep.iium.edu.my/1359/1/Execution_Time_Prediction_of_Imperative_Paradigm_Tasks_for_Grid_Scheduling_Optimization.pdf

id	iium-1359
recordtype	eprints
spelling	iium-13592017-06-13T05:09:01Z http://irep.iium.edu.my/1359/ Execution time prediction of imperative paradigm tasks for grid scheduling optimization Kiran, Maleeha Hassan Abdalla Hashim, Aisha Lim, Mei Kuan Yap, Yee Jiun TK7885 Computer engineering An efficient functioning of a complicated and dynamic grid environment requires a resource manager to monitor and identify the idling resources and to schedule users’ submitted jobs (or programs) accordingly. A common problem arising in grid computing is to select the most efficient resource to run a particular program. At present the execution time of any program submission depends mostly on guesswork by the user. The inaccuracy of guesswork leads to inefficient resource usage, incurring extra operational costs such as idling queues or machines. Thus, in this paper we propose a job execution time prediction module to aid the user. The proposed system will function as a standalone unit where its services can be offered to users as part of a grid portal. This system focuses on imperative paradigm tasks as they are commonly used in a grid environment. We propose a novel methodology and architecture to predict the execution time of jobs using aspects of static analysis, analytical benchmarking and compiler based approach. Essentially a program is analyzed in segments for execution time and these times are combined together to give the total execution time of the program. The experimental results show that the technique is successful in achieving a prediction accuracy of greater than 80%. Future work may involve handling other paradigms such as object-oriented programming and investigating the possibility of integrating the prediction module into a real grid environment. IJCSNS 2009-02 Article PeerReviewed application/pdf en http://irep.iium.edu.my/1359/1/Execution_Time_Prediction_of_Imperative_Paradigm_Tasks_for_Grid_Scheduling_Optimization.pdf Kiran, Maleeha and Hassan Abdalla Hashim, Aisha and Lim, Mei Kuan and Yap, Yee Jiun (2009) Execution time prediction of imperative paradigm tasks for grid scheduling optimization. International Journal of Computer Science and Network Security (IJCSNS), 9 (2). pp. 155-163. ISSN 1738-7906 http://paper.ijcsns.org/07_book/html/200902/200902020.html
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TK7885 Computer engineering
spellingShingle	TK7885 Computer engineering Kiran, Maleeha Hassan Abdalla Hashim, Aisha Lim, Mei Kuan Yap, Yee Jiun Execution time prediction of imperative paradigm tasks for grid scheduling optimization
description	An efficient functioning of a complicated and dynamic grid environment requires a resource manager to monitor and identify the idling resources and to schedule users’ submitted jobs (or programs) accordingly. A common problem arising in grid computing is to select the most efficient resource to run a particular program. At present the execution time of any program submission depends mostly on guesswork by the user. The inaccuracy of guesswork leads to inefficient resource usage, incurring extra operational costs such as idling queues or machines. Thus, in this paper we propose a job execution time prediction module to aid the user. The proposed system will function as a standalone unit where its services can be offered to users as part of a grid portal. This system focuses on imperative paradigm tasks as they are commonly used in a grid environment. We propose a novel methodology and architecture to predict the execution time of jobs using aspects of static analysis, analytical benchmarking and compiler based approach. Essentially a program is analyzed in segments for execution time and these times are combined together to give the total execution time of the program. The experimental results show that the technique is successful in achieving a prediction accuracy of greater than 80%. Future work may involve handling other paradigms such as object-oriented programming and investigating the possibility of integrating the prediction module into a real grid environment.
format	Article
author	Kiran, Maleeha Hassan Abdalla Hashim, Aisha Lim, Mei Kuan Yap, Yee Jiun
author_facet	Kiran, Maleeha Hassan Abdalla Hashim, Aisha Lim, Mei Kuan Yap, Yee Jiun
author_sort	Kiran, Maleeha
title	Execution time prediction of imperative paradigm tasks for grid scheduling optimization
title_short	Execution time prediction of imperative paradigm tasks for grid scheduling optimization
title_full	Execution time prediction of imperative paradigm tasks for grid scheduling optimization
title_fullStr	Execution time prediction of imperative paradigm tasks for grid scheduling optimization
title_full_unstemmed	Execution time prediction of imperative paradigm tasks for grid scheduling optimization
title_sort	execution time prediction of imperative paradigm tasks for grid scheduling optimization
publisher	IJCSNS
publishDate	2009
url	http://irep.iium.edu.my/1359/ http://irep.iium.edu.my/1359/ http://irep.iium.edu.my/1359/1/Execution_Time_Prediction_of_Imperative_Paradigm_Tasks_for_Grid_Scheduling_Optimization.pdf
first_indexed	2023-09-18T20:08:39Z
last_indexed	2023-09-18T20:08:39Z
_version_	1777407328959594496

Execution time prediction of imperative paradigm tasks for grid scheduling optimization

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