Multi-timescale online optimization of network function virtualization for service chaining

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

作者机构：Dept. of Communication Science and Engineering Shanghai Institute for Advanced Communication and Data Science Fudan University 220 Handan Road Shanghai China School of Engineering Macquarie University SydneyNSW2109 Australia  SydneyNSW2122 Australia Dept. of Electrical and Computer Engineering and Digital Technology Center University of Minnesota MinneapolisMN55455 United States School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2018年

核心收录：

主　　题：Virtual machine 

摘      要：Network Function Virtualization (NFV) can costefficiently provide network services by running different virtual network functions (VNFs) at different virtual machines (VMs) in a correct order. This can result in strong couplings between the decisions of the VMs on the placement and operations of VNFs. This paper presents a new fully decentralized online approach for optimal placement and operations of VNFs. Building on a new stochastic dual gradient method, our approach decouples the realtime decisions of VMs, asymptotically minimizes the time-average cost of NFV, and stabilizes the backlogs of network services with a cost-backlog tradeoff of [o, 1/o], for any o 0. Our approach can be relaxed into multiple timescales to have VNFs (re)placed at a larger timescale and hence alleviate service interruptions. While proved to preserve the asymptotic optimality, the larger timescale can slow down the optimal placement of VNFs. A learnand- A dapt strategy is further designed to speed the placement up with an improved tradeoff [o, log2(o)/po]. 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 benchmarks. Copyright © 2018, The Authors. All rights reserved.