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

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

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...

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Main Author: Muthuvelu, Nithiapidary
Format: Thesis
Published: 2011
Subjects:
QA76.75-76.765 Computer software
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id my-mmu-ep.5952
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spelling my-mmu-ep.59522015-02-05T01:45:17Z Grouping and deploying fine-grained tasks on grid by learning performance data 2011-06 Muthuvelu, Nithiapidary QA76.75-76.765 Computer software 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 2011-06 Thesis http://shdl.mmu.edu.my/5952/ http://library.mmu.edu.my/diglib/onlinedb/dig_lib.php phd doctoral Multimedia University Faculty of Computing and Informatics
institution Multimedia University
collection MMU Institutional Repository
topic QA76.75-76.765 Computer software
spellingShingle QA76.75-76.765 Computer software
Muthuvelu, Nithiapidary
Grouping and deploying fine-grained tasks on grid by learning performance data
description 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
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Muthuvelu, Nithiapidary
author_facet Muthuvelu, Nithiapidary
author_sort Muthuvelu, Nithiapidary
title Grouping and deploying fine-grained tasks on grid by learning performance data
title_short Grouping and deploying fine-grained tasks on grid by learning performance data
title_full Grouping and deploying fine-grained tasks on grid by learning performance data
title_fullStr Grouping and deploying fine-grained tasks on grid by learning performance data
title_full_unstemmed Grouping and deploying fine-grained tasks on grid by learning performance data
title_sort grouping and deploying fine-grained tasks on grid by learning performance data
granting_institution Multimedia University
granting_department Faculty of Computing and Informatics
publishDate 2011
_version_ 1747829602123776000

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