Automated Function Placement and Online Optimization of Network Functions Virtualization

Chen, X; Ni, W; Collings, IB; Wang, X; Xu, S

Automated Function Placement and Online Optimization of Network Functions Virtualization

Chen, X Ni, W

Collings, IB Wang, X Xu, S

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Communications, 2019, 67, (2), pp. 1225-1237
Issue Date:: 2019-02-01

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Field	Value	Language
dc.contributor.author	Chen, X
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Collings, IB
dc.contributor.author	Wang, X
dc.contributor.author	Xu, S
dc.date.accessioned	2022-09-17T02:05:54Z
dc.date.available	2022-09-17T02:05:54Z
dc.date.issued	2019-02-01
dc.identifier.citation	IEEE Transactions on Communications, 2019, 67, (2), pp. 1225-1237
dc.identifier.issn	0090-6778
dc.identifier.issn	1558-0857
dc.identifier.uri	http://hdl.handle.net/10453/161866
dc.description.abstract	This paper proposes a new fully decentralized approach to online placement and optimization of virtual machines (VMs) for network functions virtualization (NFV). The approach is of practical value, as network services comprising a chain of virtual network functions (VNFs) are proposed to be queued on the basis of leading unexecuted VNFs at every server, rather than on the typical basis of services, reducing queues per server and facilitating queue management and signaling. It is also non-trivial because the VNFs of network services must be executed correctly in order at different VMs, coupling the optimal decisions of VMs on processing or offloading. Exploiting Lyapunov optimization techniques, we decouple the optimal decisions by deriving and minimizing the instantaneous upper bound of the NFV cost in a distributed fashion, and achieve the asymptotically minimum time-average cost. We also reduce the queue length by allowing individual VMs to (un)install VNFs based on local knowledge, achieving stable redeployment of VNFs, adapting to the network topology and the temporal and spatial variations of services. Simulations show that the proposed approach is able to reduce the time-average cost of NFV by 71% and reduce the queue length (or delay) by 74%, as compared with existing approaches.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Communications
dc.relation.isbasedon	10.1109/TCOMM.2018.2877336
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0804 Data Format, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.title	Automated Function Placement and Online Optimization of Network Functions Virtualization
dc.type	Journal Article
utslib.citation.volume	67
utslib.for	0804 Data Format
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
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 Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-09-17T02:05:38Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	67
utslib.citation.issue	2

Abstract:

This paper proposes a new fully decentralized approach to online placement and optimization of virtual machines (VMs) for network functions virtualization (NFV). The approach is of practical value, as network services comprising a chain of virtual network functions (VNFs) are proposed to be queued on the basis of leading unexecuted VNFs at every server, rather than on the typical basis of services, reducing queues per server and facilitating queue management and signaling. It is also non-trivial because the VNFs of network services must be executed correctly in order at different VMs, coupling the optimal decisions of VMs on processing or offloading. Exploiting Lyapunov optimization techniques, we decouple the optimal decisions by deriving and minimizing the instantaneous upper bound of the NFV cost in a distributed fashion, and achieve the asymptotically minimum time-average cost. We also reduce the queue length by allowing individual VMs to (un)install VNFs based on local knowledge, achieving stable redeployment of VNFs, adapting to the network topology and the temporal and spatial variations of services. Simulations show that the proposed approach is able to reduce the time-average cost of NFV by 71% and reduce the queue length (or delay) by 74%, as compared with existing approaches.

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http://hdl.handle.net/10453/161866