IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Adaptive Processor Allocation for Moldable Jobs in Computational Grid

Adaptive Processor Allocation for Moldable Jobs in Computational Grid
View Sample PDF
Author(s): Kuo-Chan Huang (National Taichung University, Taiwan), Po-Chi Shih (National Tsing Hua University, Taiwan)and Yeh-Ching Chung (National Tsing Hua University, Taiwan)
Copyright: 2009
Volume: 1
Issue: 1
Pages: 12
Source title: International Journal of Grid and High Performance Computing (IJGHPC)
Editor(s)-in-Chief: Emmanuel Udoh (Sullivan University, USA)and Ching-Hsien Hsu (Asia University, Taiwan)
DOI: 10.4018/jghpc.2009010102

Purchase

View Adaptive Processor Allocation for Moldable Jobs in Computational Grid on the publisher's website for pricing and purchasing information.

Abstract

In a computational grid environment, a common practice is try to allocate an entire parallel job onto a single participating site. Sometimes a parallel job, upon its submission, cannot fit in any single site due to the occupation of some resources by running jobs. How the job scheduler handles such situations is an important issue which has the potential to further improve the utilization of grid resources as well as the performance of parallel jobs. This article develops adaptive processor allocation policies based on the moldable property of parallel jobs to deal with such situations in a heterogeneous computational grid environment. The proposed policies are evaluated through a series of simulations using real workload traces. The results indicate that the proposed adaptive processor allocation policies can further improve the system performance of a heterogeneous computational grid significantly.

Related Content

Honglong Xu, Zhonghao Liang, Kaide Huang, Guoshun Huang, Yan He. © 2024. 17 pages.
Sherin Eliyas, P. Ranjana. © 2024. 10 pages.
Shuang Li, Xiaoguo Yao. © 2024. 16 pages.
Jialan Sun. © 2024. 21 pages.
Mei Gong, Bingli Mo. © 2024. 15 pages.
Qian He, Ke Wang. © 2024. 19 pages.
Sunil Kumar, Rashmi Mishra, Tanvi Jain, Achyut Shankar. © 2024. 12 pages.
Body Bottom