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postgraduate thesis: Cost-aware online VM purchasing for cloud-based application service providers with arbitrary demands

TitleCost-aware online VM purchasing for cloud-based application service providers with arbitrary demands
Authors
Advisors
Advisor(s):Wu, C
Issue Date2014
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Shi, S. [石晟恺]. (2014). Cost-aware online VM purchasing for cloud-based application service providers with arbitrary demands. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5351041
AbstractRecent years witness the proliferation of Infrastructure-as-a-Service (IaaS) cloud services, which provide on-demand resources (CPU, RAM, disk, etc.) in the form of virtual machines (VMs) for hosting services of third parties. As such, the way of enabling scalable and dynamic Internet applications has been remarkably revolutionized. More and more Application Service Providers (ASPs) are launching their applications in clouds, eliminating the need to construct and operate their owned IT hardware and software. Given the state-of-the-art IaaS offerings, it is still a problem of fundamental importance how the ASPs should rent VMs from the clouds to serve their application needs, in order to minimize the cost while meeting their job demands over a long run. Cloud providers offer different pricing options to meet computing requirements of a variety of applications. The commonly adopted cloud pricing schemes are (1) reserved instance pricing, (2) on-demand instance pricing, and (3) spot instance pricing. However, the challenge facing an ASP is how these pricing schemes can be blended to accommodate arbitrary demands at the optimal cost. In this thesis, we seek to integrate all available pricing options and design effective online algorithms for the long-term operation of ASPs. We formulate the long-term-averaged VM cost minimization problem of an ASP with time-varying and delay-tolerant workloads in a stochastic optimization model. An efficient online VM purchasing algorithm is designed to guide the VM purchasing decisions of the ASP based on the Lyapunov optimization technique. In stark contrast with the existing studies, our online VM purchasing algorithm does not require any a priori knowledge of the workload or any future information. Moreover, it addresses the possible job interruption due to uncertain availability of spot instances. Rigorous analysis shows that our algorithm can achieve a time-averaged VM purchasing cost with a constant gap from its offline minimum. Trace-driven simulations further verify the efficacy of our algorithm.
DegreeMaster of Philosophy
SubjectVirtual computer systems
Application service providers
Cloud computing
Dept/ProgramComputer Science
Persistent Identifierhttp://hdl.handle.net/10722/208014

 

DC FieldValueLanguage
dc.contributor.advisorWu, C-
dc.contributor.authorShi, Shengkai-
dc.contributor.author石晟恺-
dc.date.accessioned2015-02-06T14:19:34Z-
dc.date.available2015-02-06T14:19:34Z-
dc.date.issued2014-
dc.identifier.citationShi, S. [石晟恺]. (2014). Cost-aware online VM purchasing for cloud-based application service providers with arbitrary demands. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5351041-
dc.identifier.urihttp://hdl.handle.net/10722/208014-
dc.description.abstractRecent years witness the proliferation of Infrastructure-as-a-Service (IaaS) cloud services, which provide on-demand resources (CPU, RAM, disk, etc.) in the form of virtual machines (VMs) for hosting services of third parties. As such, the way of enabling scalable and dynamic Internet applications has been remarkably revolutionized. More and more Application Service Providers (ASPs) are launching their applications in clouds, eliminating the need to construct and operate their owned IT hardware and software. Given the state-of-the-art IaaS offerings, it is still a problem of fundamental importance how the ASPs should rent VMs from the clouds to serve their application needs, in order to minimize the cost while meeting their job demands over a long run. Cloud providers offer different pricing options to meet computing requirements of a variety of applications. The commonly adopted cloud pricing schemes are (1) reserved instance pricing, (2) on-demand instance pricing, and (3) spot instance pricing. However, the challenge facing an ASP is how these pricing schemes can be blended to accommodate arbitrary demands at the optimal cost. In this thesis, we seek to integrate all available pricing options and design effective online algorithms for the long-term operation of ASPs. We formulate the long-term-averaged VM cost minimization problem of an ASP with time-varying and delay-tolerant workloads in a stochastic optimization model. An efficient online VM purchasing algorithm is designed to guide the VM purchasing decisions of the ASP based on the Lyapunov optimization technique. In stark contrast with the existing studies, our online VM purchasing algorithm does not require any a priori knowledge of the workload or any future information. Moreover, it addresses the possible job interruption due to uncertain availability of spot instances. Rigorous analysis shows that our algorithm can achieve a time-averaged VM purchasing cost with a constant gap from its offline minimum. Trace-driven simulations further verify the efficacy of our algorithm.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subject.lcshVirtual computer systems-
dc.subject.lcshApplication service providers-
dc.subject.lcshCloud computing-
dc.titleCost-aware online VM purchasing for cloud-based application service providers with arbitrary demands-
dc.typePG_Thesis-
dc.identifier.hkulb5351041-
dc.description.thesisnameMaster of Philosophy-
dc.description.thesislevelMaster-
dc.description.thesisdisciplineComputer Science-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5353/th_b5351041-

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