Distributed Stochastic Optimization of Network Function Virtualization

Xiaojing Chen, Wei Ni, Tianyi Chen, Iain B. Collings, Xin Wang, Ren Ping Liu, Georgios B Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781509050192
DOIs
StatePublished - Jul 1 2017
Event2017 IEEE Global Communications Conference, GLOBECOM 2017 - Singapore, Singapore
Duration: Dec 4 2017Dec 8 2017

Publication series

Name2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
Volume2018-January

Other

Other2017 IEEE Global Communications Conference, GLOBECOM 2017
Country/TerritorySingapore
CitySingapore
Period12/4/1712/8/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

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