Distributed Stochastic Optimization of Network Function Virtualization

Chen, X; Ni, W; Chen, T; Collings, IB; Wang, X; Liu, RP; Giannakis, GB

Distributed Stochastic Optimization of Network Function Virtualization

Chen, X Ni, W

Chen, T Collings, IB Wang, X Liu, RP

Giannakis, GB

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Publication Type:: Conference Proceeding
Citation:: 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings, 2017, 2018-January pp. 1 - 6
Issue Date:: 2017-07-01

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Field	Value	Language
dc.contributor.author	Chen, X	en_US
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X	en_US
dc.contributor.author	Chen, T	en_US
dc.contributor.author	Collings, IB	en_US
dc.contributor.author	Wang, X	en_US
dc.contributor.author	Liu, RP https://orcid.org/0000-0001-7001-6305	en_US
dc.contributor.author	Giannakis, GB	en_US
dc.date.issued	2017-07-01	en_US
dc.identifier.citation	2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings, 2017, 2018-January pp. 1 - 6	en_US
dc.identifier.isbn	9781509050192	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127034
dc.description.abstract	© 2017 IEEE. Decoupling network services from underlying hardware, network function virtualization (NFV) is expected to significantly improve agility and reduce network cost. However, network services, sequences of network functions, need to be processed in specific orders at specific types of virtual machines (VMs), which couples decisions of VMs on processing or routing network services. Built on a new stochastic dual gradient method, our approach suppresses the couplings, minimizes the time-average cost of NFV, stabilizes queues at VMs, and reduces the backlogs of unprocessed services through online learning and adaptation. Asymptotically optimal decisions are instantly generated at individual VMs, with a cost-delay tradeoff [(ϵ)/√ϵ]. Numerical results show that the proposed method is able to reduce the time-average cost of NFV by 30% and reduce the queue length (or delay) by 83%, as compared to existing non-stochastic approaches.	en_US
dc.relation.ispartof	2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings	en_US
dc.relation.isbasedon	10.1109/GLOCOM.2017.8254728	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Distributed Stochastic Optimization of Network Function Virtualization	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-January	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-January	en_US

Abstract:

© 2017 IEEE. Decoupling network services from underlying hardware, network function virtualization (NFV) is expected to significantly improve agility and reduce network cost. However, network services, sequences of network functions, need to be processed in specific orders at specific types of virtual machines (VMs), which couples decisions of VMs on processing or routing network services. Built on a new stochastic dual gradient method, our approach suppresses the couplings, minimizes the time-average cost of NFV, stabilizes queues at VMs, and reduces the backlogs of unprocessed services through online learning and adaptation. Asymptotically optimal decisions are instantly generated at individual VMs, with a cost-delay tradeoff [(ϵ)/√ϵ]. Numerical results show that the proposed method is able to reduce the time-average cost of NFV by 30% and reduce the queue length (or delay) by 83%, as compared to existing non-stochastic approaches.

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http://hdl.handle.net/10453/127034