Dynamic provisioning of cloud resources based on workload prediction

Bhagavathiperumal, S; Goyal, M

Dynamic provisioning of cloud resources based on workload prediction

Bhagavathiperumal, S Goyal, M

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Publication Type:: Conference Proceeding
Citation:: 2019, 75 pp. 41 - 49
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Bhagavathiperumal, S	en_US
dc.contributor.author	Goyal, M https://orcid.org/0000-0003-2853-9393	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	2019, 75 pp. 41 - 49	en_US
dc.identifier.issn	2367-3370	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134546
dc.description.abstract	© Springer Nature Singapore Pte Ltd. 2019. Most of the businesses nowadays have started using cloud platforms to host their software applications. A cloud platform is a shared resource that provides various services like software as a service (SAAS), infrastructure as a service (IAAS) or anything as a service (XAAS) that is required to develop and deploy any business application. These cloud services are provided as virtual machines (VM) that can handle the end-user’s requirements. The cloud providers have to ensure efficient resource handling mechanisms for different time intervals to avoid wastage of resources. Auto-scaling mechanisms would take care of using these resources appropriately along with providing an excellent quality of service. The researchers have used various approaches to perform auto-scaling. In this paper, a framework based on dynamic provisioning of cloud resources using workload prediction is discussed.	en_US
dc.relation.isbasedon	10.1007/978-981-13-7150-9_5	en_US
dc.title	Dynamic provisioning of cloud resources based on workload prediction	en_US
dc.type	Conference Proceeding
utslib.citation.volume	75	en_US
utslib.for	0805 Distributed Computing	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
pubs.volume	75	en_US

Abstract:

© Springer Nature Singapore Pte Ltd. 2019. Most of the businesses nowadays have started using cloud platforms to host their software applications. A cloud platform is a shared resource that provides various services like software as a service (SAAS), infrastructure as a service (IAAS) or anything as a service (XAAS) that is required to develop and deploy any business application. These cloud services are provided as virtual machines (VM) that can handle the end-user’s requirements. The cloud providers have to ensure efficient resource handling mechanisms for different time intervals to avoid wastage of resources. Auto-scaling mechanisms would take care of using these resources appropriately along with providing an excellent quality of service. The researchers have used various approaches to perform auto-scaling. In this paper, a framework based on dynamic provisioning of cloud resources using workload prediction is discussed.

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http://hdl.handle.net/10453/134546