Title: Energy-efficient virtual network embedding in networks for cloud computing

Authors: Xiang-Wei Zheng; Bin Hu; Dian-Jie Lu; Zhen-Hua Chen; Hong Liu

Addresses: School of Information Science and Engineering, Shandong Normal University, China; Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, China ' School of Information Science and Engineering, Shandong Normal University, China; School of Information Science and Engineering, Lanzhou University, China ' School of Information Science and Engineering, Shandong Normal University, China; Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, China ' School of Information Science and Engineering, Shandong Normal University, China; Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, China ' School of Information Science and Engineering, Shandong Normal University, China; Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, China

Abstract: Cloud computing is based on several service models such as Network as a Service (NaaS), Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). NaaS is a business model that aims to provide virtual network (VN) services over the internet from shared cloud data centres. Virtual network embedding (VNE) is one of the core technologies in NaaS resource allocation. In view of the enormous energy consumption by numerous cloud data centres, energy-efficient VNE becomes a new research focus. In this paper, we propose an energy efficient virtual network embedding (EEVNE) approach for cloud computing networks, in which power savings are introduced by consolidating resources in the network and data centres. EEVNE is based on the evaluation of the energy consumption by hosts, the allocation of virtual resources both in nodes and links, as well as the cost and revenue in network virtualisation. We also formulated a heuristic embedding algorithm based on Group Search Optimiser (GSOVNE) to solve the NP-hard problem. Furthermore, a reconfiguration algorithm is developed to improve the utility of fragmented resources. Simulation experiments show that the proposed algorithm can effectively increase VN acceptance ratio and reduce energy consumption when large quantities of virtual networks arrive and depart over time.

Keywords: cloud data centres; network as a service; NaaS resource allocation; virtual networks; embedded systems; energy consumption; group search optimisation; energy efficiency; cloud computing; cloud services; virtual resources; network virtualisation; simulation.

DOI: 10.1504/IJWGS.2017.082058

International Journal of Web and Grid Services, 2017 Vol.13 No.1, pp.75 - 93

Available online: 27 Jan 2017 *

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