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Budget constrained execution of multiple Bag-of-Tasks applications on the Cloud

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2015.IEEECloud.pdf (259.3Kb)
Date
27/06/2015
Author
Thai, Long Thanh
Varghese, Blesson
Barker, Adam David
Funder
The Royal Society
EPSRC
Grant ID
EP/K015745/1
Keywords
QA75 Electronic computers. Computer science
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Abstract
Optimising the execution of Bag-of-Tasks (BoT) applications on the cloud is a hard problem due to the trade- offs between performance and monetary cost. The problem can be further complicated when multiple BoT applications need to be executed. In this paper, we propose and implement a heuristic algorithm that schedules tasks of multiple applications onto different cloud virtual machines in order to maximise performance while satisfying a given budget constraint. Current approaches are limited in task scheduling since they place a limit on the number of cloud resources that can be employed by the applications. However, in the proposed algorithm there are no such limits, and in comparison with other approaches, the algorithm on average achieves an improved performance of 10%. The experimental results also highlight that the algorithm yields consistent performance even with low budget constraints which cannot be achieved by competing approaches.
Citation
Thai , L T , Varghese , B & Barker , A D 2015 , Budget constrained execution of multiple Bag-of-Tasks applications on the Cloud . in 2015 IEEE 8th International Conference on Cloud Computing (CLOUD) . IEEE Computer Society , New York , pp. 975-980 , 8th IEEE International Conference on Cloud Computing (CLOUD 2015) , New York , United States , 27/06/15 . https://doi.org/10.1109/CLOUD.2015.131
 
conference
 
Publication
2015 IEEE 8th International Conference on Cloud Computing (CLOUD)
DOI
https://doi.org/10.1109/CLOUD.2015.131
Type
Conference item
Rights
© 2015. IEEE. This is the accepted manuscript of a conference paper originally submitted to the 8th IEEE International Conference on Cloud Computing
Description
This research is supported by the EPSRC grant ‘Working Together: Constraint Programming and Cloud Computing’ (EP/K015745/1), a Royal Society Industry Fellowship ‘Bringing Science to the Cloud’, an EPSRC Impact Acceleration Grant (IAA) and an Amazon Web Services (AWS) Education Research Grant.
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/7150

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