Virtual network embedding with pre‐transformation and incentive convergence mechanism. (27th August 2016)

• Record Type: Journal Article
• Authors: Wang, Cong; Liu, Guohua; Peng, Sancheng; Yuan, Ying; Li, Guorui; Wan, Cong
• Other Names: Hassan Houcine guestEditor.; Yang Laurence T. guestEditor.; Qiu Meikang guestEditor.
• Is Part Of: Concurrency and computation. Volume 29:Number 14(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-08-27
• Subjects: cloud computing -- network virtualization -- virtual network embedding -- virtual topological transformation -- discrete particle swarm optimization; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.3947
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Efficient and fair resource allocation for multitudinous virtual networks running cloud‐based applications is crucial to archive dynamic resources multi‐tenancy in cloud computing. In order to solve the problem, we propose a novel virtual network embedding (VNE) algorithm to increase revenue and utilization of substrate network as well as to improve acceptance fairness of virtual networks. First, we present a virtual topology pre‐transformation mechanism leveraging reusable technology to reduce topology difference and achieve acceptance fairness. Then, because of the Non‐deterministic polynomial‐time (NP)‐hard characteristics of VNE, we model the problem as an integer linear programming problem and solve the VNE problem with a discrete particle swarm optimization‐based algorithm. The operations and parameters of particles are well redefined according to the VNE context. Finally, an incentive convergence mechanism is proposed to reduce mapping complexity, which can be used to accelerate convergence and to save more bandwidth by exploiting individual candidate nodes' lists. Simulation results prove that our proposed method is superior to the existing similar algorithms in terms of physical resource utilization, acceptance fairness, revenue/cost ratio, and searching efficiency. Copyright © 2016 John Wiley & Sons, Ltd.