An innovative self-adaptive configuration optimization system in cloud computing

Jiang, J; Lu, J; Zhang, G

An innovative self-adaptive configuration optimization system in cloud computing

Jiang, J

Lu, J

Zhang, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE 9th International Conference on Dependable, Autonomic and Secure Computing, DASC 2011, 2011, pp. 621 - 627
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Jiang, J https://orcid.org/0000-0001-5301-7779	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings - IEEE 9th International Conference on Dependable, Autonomic and Secure Computing, DASC 2011, 2011, pp. 621 - 627	en_US
dc.identifier.isbn	9780769546124	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29980
dc.description.abstract	Cloud computing has emerging as an extremely popular and cost-effective computational service model using pay-as-you-go executing environments that scale transparently to the user. However, cloud providers should tackle the challenge of configuring their systems to provide maximal performance while minimizing customer's cost of computing resources, which satisfy the customers' various workload requirements. To solve the above challenge, in this paper, we propose an innovative architecture of self-adaptive configuration optimization system which supports dynamic reconfiguration when workloads change. In addition, we develop an optimization algorithm by using genetic algorithm for this system. By using queuing theory and statistic techniques, we model and compute the SLAs metrics which are defined as the fitness function in the optimization algorithm. This optimization system can guide cloud customers to purchase appropriate resources and make decision of deployment configuration such as scale, scheduling and capacity. © 2011 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP110103733
dc.relation.ispartof	Proceedings - IEEE 9th International Conference on Dependable, Autonomic and Secure Computing, DASC 2011	en_US
dc.relation.isbasedon	10.1109/DASC.2011.112	en_US
dc.title	An innovative self-adaptive configuration optimization system in cloud computing	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	en_US
dc.location.activity	Sydney, Australia	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Cloud computing has emerging as an extremely popular and cost-effective computational service model using pay-as-you-go executing environments that scale transparently to the user. However, cloud providers should tackle the challenge of configuring their systems to provide maximal performance while minimizing customer's cost of computing resources, which satisfy the customers' various workload requirements. To solve the above challenge, in this paper, we propose an innovative architecture of self-adaptive configuration optimization system which supports dynamic reconfiguration when workloads change. In addition, we develop an optimization algorithm by using genetic algorithm for this system. By using queuing theory and statistic techniques, we model and compute the SLAs metrics which are defined as the fitness function in the optimization algorithm. This optimization system can guide cloud customers to purchase appropriate resources and make decision of deployment configuration such as scale, scheduling and capacity. © 2011 IEEE.

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http://hdl.handle.net/10453/29980