Optimal cloud resource auto-scaling for web applications

Jiang, J; Lu, J; Zhang, G; Long, G

Optimal cloud resource auto-scaling for web applications

Jiang, J

Lu, J

Zhang, G

Long, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2013, 2013, pp. 58 - 65
Issue Date:: 2013-08-14

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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.contributor.author	Long, G https://orcid.org/0000-0003-3740-9515	en_US
dc.date.issued	2013-08-14	en_US
dc.identifier.citation	Proceedings - 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2013, 2013, pp. 58 - 65	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28351
dc.description.abstract	In the on-demand cloud environment, web application providers have the potential to scale virtual resources up or down to achieve cost-effective outcomes. True elasticity and cost-effectiveness in the pay-per-use cloud business model, however, have not yet been achieved. To address this challenge, we propose a novel cloud resource auto-scaling scheme at the virtual machine (VM) level for web application providers. The scheme automatically predicts the number of web requests and discovers an optimal cloud resource demand with cost-latency trade-off. Based on this demand, the scheme makes a resource scaling decision that is up or down or NOP (no operation) in each time-unit re-allocation. We have implemented the scheme on the Amazon cloud platform and evaluated it using three real-world web log datasets. Our experiment results demonstrate that the proposed scheme achieves resource auto-scaling with an optimal cost-latency trade-off, as well as low SLA violations. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings - 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2013	en_US
dc.relation.isbasedon	10.1109/CCGrid.2013.73	en_US
dc.title	Optimal cloud resource auto-scaling for web applications	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
dc.location.activity	Delft, Netherlands	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	open_access
pubs.publication-status	Published	en_US

Abstract:

In the on-demand cloud environment, web application providers have the potential to scale virtual resources up or down to achieve cost-effective outcomes. True elasticity and cost-effectiveness in the pay-per-use cloud business model, however, have not yet been achieved. To address this challenge, we propose a novel cloud resource auto-scaling scheme at the virtual machine (VM) level for web application providers. The scheme automatically predicts the number of web requests and discovers an optimal cloud resource demand with cost-latency trade-off. Based on this demand, the scheme makes a resource scaling decision that is up or down or NOP (no operation) in each time-unit re-allocation. We have implemented the scheme on the Amazon cloud platform and evaluated it using three real-world web log datasets. Our experiment results demonstrate that the proposed scheme achieves resource auto-scaling with an optimal cost-latency trade-off, as well as low SLA violations. © 2013 IEEE.

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http://hdl.handle.net/10453/28351