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Journal of Electrical and Computer Engineering
Volume 2016, Article ID 4067186, 10 pages
http://dx.doi.org/10.1155/2016/4067186
Research Article

A Game for Energy-Aware Allocation of Virtualized Network Functions

1CNIT-University of Genoa Research Unit, 16145 Genoa, Italy
2DITEN, University of Genoa, 16145 Genoa, Italy

Received 2 October 2015; Revised 22 December 2015; Accepted 11 January 2016

Academic Editor: Xavier Hesselbach

Copyright © 2016 Roberto Bruschi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Network Functions Virtualization (NFV) is a network architecture concept where network functionality is virtualized and separated into multiple building blocks that may connect or be chained together to implement the required services. The main advantages consist of an increase in network flexibility and scalability. Indeed, each part of the service chain can be allocated and reallocated at runtime depending on demand. In this paper, we present and evaluate an energy-aware Game-Theory-based solution for resource allocation of Virtualized Network Functions (VNFs) within NFV environments. We consider each VNF as a player of the problem that competes for the physical network node capacity pool, seeking the minimization of individual cost functions. The physical network nodes dynamically adjust their processing capacity according to the incoming workload, by means of an Adaptive Rate (AR) strategy that aims at minimizing the product of energy consumption and processing delay. On the basis of the result of the nodes’ AR strategy, the VNFs’ resource sharing costs assume a polynomial form in the workflows, which admits a unique Nash Equilibrium (NE). We examine the effect of different (unconstrained and constrained) forms of the nodes’ optimization problem on the equilibrium and compare the power consumption and delay achieved with energy-aware and non-energy-aware strategy profiles.