Risk-aware checkpoint selection in cloud-based scientific workflow

Wang, M; Zhu, L; Chen, J

Risk-aware checkpoint selection in cloud-based scientific workflow

Wang, M Zhu, L Chen, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2nd International Conference on Cloud and Green Computing and 2nd International Conference on Social Computing and Its Applications, CGC/SCA 2012, 2012, pp. 137 - 144
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Wang, M	en_US
dc.contributor.author	Zhu, L	en_US
dc.contributor.author	Chen, J	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	Proceedings - 2nd International Conference on Cloud and Green Computing and 2nd International Conference on Social Computing and Its Applications, CGC/SCA 2012, 2012, pp. 137 - 144	en_US
dc.identifier.isbn	9780769548647	en_US
dc.identifier.uri	http://hdl.handle.net/10453/23004
dc.description.abstract	Scientific workflows are generally computing- and data-intensive with large volume of data generated during their execution. Therefore, some of the data should be saved to avoid the expensive re-execution of tasks in case of exceptions. However, cloud-based data storage services come at some expense. In this paper, we extend the risk evaluation model, which assigns different weights to tasks based on their ordering relationship, to decide the occasion to perform backup or checkpoint service after the completion of a task. The proposed method computes and compares the potential loss with and without data backup to achieve the tradeoff between overhead of check pointing and re-execution after exceptions. We also design the utility function with the model and apply a genetic algorithm to find the optimized schedule. The results show that the robustness of the schedule is increased while the possible risk of failure is minimized, especially when the generated data is not large. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2nd International Conference on Cloud and Green Computing and 2nd International Conference on Social Computing and Its Applications, CGC/SCA 2012	en_US
dc.relation.isbasedon	10.1109/CGC.2012.46	en_US
dc.title	Risk-aware checkpoint selection in cloud-based scientific workflow	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	en_US
dc.location.activity	Xiangtan, China	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Scientific workflows are generally computing- and data-intensive with large volume of data generated during their execution. Therefore, some of the data should be saved to avoid the expensive re-execution of tasks in case of exceptions. However, cloud-based data storage services come at some expense. In this paper, we extend the risk evaluation model, which assigns different weights to tasks based on their ordering relationship, to decide the occasion to perform backup or checkpoint service after the completion of a task. The proposed method computes and compares the potential loss with and without data backup to achieve the tradeoff between overhead of check pointing and re-execution after exceptions. We also design the utility function with the model and apply a genetic algorithm to find the optimized schedule. The results show that the robustness of the schedule is increased while the possible risk of failure is minimized, especially when the generated data is not large. © 2012 IEEE.

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http://hdl.handle.net/10453/23004