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 proceeding contributionpeer-review

6 Citations (Scopus)


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
Title of host publication2017 IEEE Global Communications Conference (GLOBECOM)
Subtitle of host publicationproceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781509050192, 9781509050185
Publication statusPublished - 2017
Event2017 IEEE Global Communications Conference, GLOBECOM 2017 - Singapore, Singapore
Duration: 4 Dec 20178 Dec 2017


Conference2017 IEEE Global Communications Conference, GLOBECOM 2017


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