IIUM Repository

Execution time prediction of imperative paradigm tasks for grid scheduling optimization

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

[img] PDF (Execution time prediction of imperative paradigm tasks for grid scheduling optimization)
Restricted to Registered users only

Download (187kB) | Request a copy


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.

Item Type: Article (Journal)
Additional Information: 2523/1359
Uncontrolled Keywords: Prediction module, Grid scheduling, Job execution time
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: Ms Zati Atiqah Mohamad Tanuri
Date Deposited: 10 Aug 2011 11:51
Last Modified: 10 Dec 2020 11:02
URI: http://irep.iium.edu.my/id/eprint/1359

Actions (login required)

View Item View Item


Downloads per month over past year