DocumentCode :
2977010
Title :
Embedding Virtual Infrastructure Based on Genetic Algorithm
Author :
Xiuming Mi ; Xiaolin Chang ; Jiqiang Liu ; Longmei Sun ; Bin Xing
Author_Institution :
Sch. of Comput. & Inf. Technol., Beijing Jiaotong Univ., Beijing, China
fYear :
2012
fDate :
14-16 Dec. 2012
Firstpage :
239
Lastpage :
244
Abstract :
The virtual network embedding (VNE) problem 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 the offline case. The genetic algorithm (GA) is an excellent approach to solving complex problems in optimization with difficult constraints. This paper explores applying GA to handle the VNE problem. We propose two GA-based VNE algorithms and evaluate them by comparing with the existing state-of-the-art VNE algorithms, including PSO-based VNE approaches. Extensive simulation results validate the capability of the proposed GA-based VNE algorithms in terms of the InP long-term revenue and the VN embedding cost.
Keywords :
computational complexity; computer networks; genetic algorithms; telecommunication links; virtualisation; GA-based VNE algorithms; InP long-term revenue; NP-hard problem; PSO-based VNE approaches; VNE problem; genetic algorithm; link constraints; node constraints; physical infrastructure; state-of-the-art VNE algorithms; substrate network; virtual infrastructure embedding; virtual network embedding problem; virtual network requests; Algorithm design and analysis; Bandwidth; Biological cells; Genetic algorithms; Mathematical model; Sociology; Statistics; Evolutionary algorithm; Network virtualization; Optimization; Virtual network embedding;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2012 13th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-0-7695-4879-1
Type :
conf
DOI :
10.1109/PDCAT.2012.71
Filename :
6589270
Link To Document :
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