Near Optimal Learning-Driven Mechanisms for Stable NFV Markets in Multitier Cloud Networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Zichuan Xu
  • Haozhe Ren
  • Qiufen Xia
  • Wanlei Zhou
  • Pan Zhou
  • Wenzheng Xu
  • Guowei Wu
  • Mingchu Li

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)2601-2615
Journal / PublicationIEEE/ACM Transactions on Networking
Volume30
Issue number6
Online published8 Jun 2022
Publication statusPublished - Dec 2022

Abstract

More and more 5G and AI applications demand flexible and low-cost processing of their traffic through diverse virtualized network functions (VNFs) to meet their security and privacy requirements. As such, the Network Function Virtualization (NFV) market has been emerged as a major service market that allows network service providers to trade their network services among customers. Since each service market usually involves complex interplays among players with different roles, efficient mechanisms that guarantee stable and efficient operations of the NFV market are urgently needed. One fundamental problem in the NFV market is how to maximize the social welfare of all players so that all players have incentives to participate in the activities of the market. In this paper, we first formulate a novel social welfare maximization problem in an NFV market of a multi-tier edge cloud network, with the aim to maximize the total revenue collected from all players, and we implement VNF services on Virtual Machines (VMs) leased by service providers to fulfill customers with service requests, where the edge cloud network consists of both cloudlets in edge networks and remote data centers in the core network. We then design an efficient incentive-compatible mechanism for the problem, and analyze the existence of a Nash equilibrium of the mechanism. Also, we consider an online social welfare maximization problem with uncertain values of customers and without the knowledge of future request arrivals, for which we devise an online learning algorithm by adopting the Multi-Armed Bandits (MAB) method with a bounded regret. We finally evaluate the performance of the proposed mechanisms through simulations and a testbed. Results show that the proposed mechanisms deliver up to 27% higher social welfare than those of existing studies.

Research Area(s)

  • 5G mobile communication, Cloud computing, Multi-tier cloud networks, near optimal incentive-compatible mechanisms, network function virtualization, online learning, Optimization, price of anarchy, Resource management, Software, Stochastic processes, Urban areas

Citation Format(s)

Near Optimal Learning-Driven Mechanisms for Stable NFV Markets in Multitier Cloud Networks. / Xu, Zichuan; Ren, Haozhe; Liang, Weifa et al.

In: IEEE/ACM Transactions on Networking, Vol. 30, No. 6, 12.2022, p. 2601-2615.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review