Grouping and deploying fine-grained tasks on grid by learning performance data

Muthuvelu, Nithiapidary (2011) Grouping and deploying fine-grained tasks on grid by learning performance data. PhD thesis, Multimedia University.

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Official URL: http://library.mmu.edu.my/diglib/onlinedb/dig_lib....

Abstract

When deciding the size or the granularity of a batch, one should consider the utilisation constraints imposed on the resources by their respective providers; e.g. the maximum time allowed for task execution and the maximum allowed storage space. In addition, the size of the batch should not overload the interconnecting network. The main objective of this thesis is to study the factors involved in deciding a batch size and design the relevant batch resizing policies and techniques. The policies and techniques are then developed and experimented in a small-scale grid environment. Throughout the conduct of this thesis, the batch resizing policies and techniques were aligned accordingly to support various purposes which led to several following major findings and contributions

Item Type: Thesis (PhD)
Additional Information: Call No.: QA76.9.C58 N58 2011
Subjects: Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science > QA76.75-76.765 Computer software
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 05 Feb 2015 01:45
Last Modified: 05 Feb 2015 01:45
URI: http://shdl.mmu.edu.my/id/eprint/5952

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