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The ant colony optimization meta-heuristic
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A Population Based Approach for ACO
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Proceedings of the 9th annual conference on Genetic and evolutionary computation
A virtual network mapping algorithm based on subgraph isomorphism detection
Proceedings of the 1st ACM workshop on Virtualized infrastructure systems and architectures
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 2
Virtual network embedding through topology awareness and optimization
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ViNEYard: virtual network embedding algorithms with coordinated node and link mapping
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Embedding Virtual Infrastructure Based on Genetic Algorithm
PDCAT '12 Proceedings of the 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies
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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.