Performance of resource management algorithms for "Processable Bulk Data Transfer" Tasks in Grid Environments

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Performance of resource management algorithms for "Processable Bulk Data Transfer" Tasks in Grid Environments

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Workshop on Software and Performance archive
Proceedings of the 7th international workshop on Software and performance table of contents

Princeton, NJ, USA

SESSION: Enhancing run-time performance table of contents

Pages 177-188

Year of Publication: 2008

ISBN:978-1-59593-873-2

Authors

Imran Ahmad	Carleton University, Ottawa, Canada
Shikharesh Majumdar	Carleton University, Ottawa, Canada

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 6, Downloads (12 Months): 66, Citation Count: 0

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ABSTRACT

Processable Bulk Data Transfer (PBDT) tasks are resource intensive concurrent tasks which involve transfer of a very large amount of data that has to be processed in some way before it can be used at a remote set of destination nodes called the sink nodes. A distributed computing environment, such as the Grid is a popular way to perform these tasks. Focusing on the execution of PBDT tasks in a Grid computing environment, this paper presents an efficient resource allocation mechanism. Due to the resource thirsty nature of these tasks, an efficient resource allocation is essential to perform these tasks while achieving satisfactory performance. The time-complexity of the resource allocation algorithm rises sharply as the available number of resources in the given Grid computing environment is increased making efficient resource allocation a challenge. To meet this challenge, this paper investigates the use of approximate algorithms for the resource allocation. The benefits obtained by using the reduced complexity of the algorithm are weighed against the increased costs incurred during the task execution (due to the inaccuracies in resource allocation introduced by the approximations). This paper describes a number of approximations and discusses under which circumstances such approximations are to be used. The techniques presented in this research can be extended to non-PBDT tasks and other distributed computing environments.

REFERENCES

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