Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/61932

Performance evaluation of artificial intelligence algorithms for virtual network embedding

Authors Chang, Xiao Lin
Mi, Xiu Ming
Muppala, Jogesh Kumaraswamy Ramachandra View this author's profile
Issue Date 2013
Source Engineering Applications of Artificial Intelligence , v. 26, (10), November 2013, p. 2540-2550
Summary Network virtualization is not only regarded as a promising technology to create an ecosystem for cloud computing applications, but also considered a promising technology for the future Internet. One of the most important issues in network virtualization is the virtual network embedding (VNE) problem, which deals with the embedding of virtual network (VN) requests in an underlying physical (substrate network) infrastructure. When both the node and link constraints are considered, the VN embedding problem is NP-hard, even in an offline situation. Some Artificial Intelligence (AI) techniques have been applied to the VNE algorithm design and displayed their abilities. This paper aims to compare the computational effectiveness and efficiency of different AI techniques for handling the cost-aware VNE problem. We first propose two kinds of VNE algorithms, based on Ant Colony Optimization and genetic algorithm. Then we carry out extensive simulations to compare the proposed VNE algorithms with the existing AI-based VNE algorithms in terms of the VN Acceptance Ratio, the long-term revenue of the service provider, and the VN embedding cost. (C) 2013 Published by Elsevier Ltd.
Subjects
ISSN 0952-1976
Language English
Format Article
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