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postgraduate thesis: Online VNF scaling with network uncertainties

TitleOnline VNF scaling with network uncertainties
Authors
Advisors
Advisor(s):Lau, FCMWu, C
Issue Date2016
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Wang, X. [汪晓可]. (2016). Online VNF scaling with network uncertainties. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.
AbstractVirtualized Network Functions (VNFs) are gaining a lot of attention from both the industry and the academia, as a promising technology to enable rapid network service composition/innovation, energy reduction and cost minimization for network operators. The VNFs typically run on virtual machine instances in a cloud infrastructure, where the virtualization technology enables dynamic provisioning of VNF instances, to process the fluctuating traffic that needs to go through the network functions in a network service. To optimally operate VNFs, it is of key importance that VNFs be scaled dynamically in response to traffic changes. We target dynamic provisioning of enterprise network services in datacenters, and design efficient online algorithms that would not require any information on future traffic rates. In the meantime, most of the works on VNF scaling assume the availability of precise network information beforehand, whereas in reality, network bandwidth fluctuates and the only way we could gain more accurate information is to do trials. We address the problem by a novel combination of an online algorithm and a bandit optimization framework. Specifically, we adopt an online algorithm to minimize the cost to provision VNF instances and a multi-armed bandit algorithm which makes use of the output of the online algorithm to minimize the congestion of the datacenter network. We demonstrate the effectiveness of our algorithms via solid theoretical analyses and trace-driven simulations.
DegreeMaster of Philosophy
SubjectComputer network architectures
Cloud computing
Dept/ProgramComputer Science
Persistent Identifierhttp://hdl.handle.net/10722/244319

 

DC FieldValueLanguage
dc.contributor.advisorLau, FCM-
dc.contributor.advisorWu, C-
dc.contributor.authorWang, Xiaoke-
dc.contributor.author汪晓可-
dc.date.accessioned2017-09-14T04:42:18Z-
dc.date.available2017-09-14T04:42:18Z-
dc.date.issued2016-
dc.identifier.citationWang, X. [汪晓可]. (2016). Online VNF scaling with network uncertainties. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.-
dc.identifier.urihttp://hdl.handle.net/10722/244319-
dc.description.abstractVirtualized Network Functions (VNFs) are gaining a lot of attention from both the industry and the academia, as a promising technology to enable rapid network service composition/innovation, energy reduction and cost minimization for network operators. The VNFs typically run on virtual machine instances in a cloud infrastructure, where the virtualization technology enables dynamic provisioning of VNF instances, to process the fluctuating traffic that needs to go through the network functions in a network service. To optimally operate VNFs, it is of key importance that VNFs be scaled dynamically in response to traffic changes. We target dynamic provisioning of enterprise network services in datacenters, and design efficient online algorithms that would not require any information on future traffic rates. In the meantime, most of the works on VNF scaling assume the availability of precise network information beforehand, whereas in reality, network bandwidth fluctuates and the only way we could gain more accurate information is to do trials. We address the problem by a novel combination of an online algorithm and a bandit optimization framework. Specifically, we adopt an online algorithm to minimize the cost to provision VNF instances and a multi-armed bandit algorithm which makes use of the output of the online algorithm to minimize the congestion of the datacenter network. We demonstrate the effectiveness of our algorithms via solid theoretical analyses and trace-driven simulations.-
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.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subject.lcshComputer network architectures-
dc.subject.lcshCloud computing-
dc.titleOnline VNF scaling with network uncertainties-
dc.typePG_Thesis-
dc.description.thesisnameMaster of Philosophy-
dc.description.thesislevelMaster-
dc.description.thesisdisciplineComputer Science-
dc.description.naturepublished_or_final_version-
dc.date.hkucongregation2017-
dc.identifier.mmsid991043953697103414-

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