Green virtual network embedding with supervised self-organizing map

Abstract Virtualization in the data center network is used to overcome the resistance of Internet ossification. Node mapping in virtual network embedding (VNE) process is more important than link mapping because each node mapping has a lot of choices with high probability. Historical data always have valuable information which displays how to be mapped virtual networks with special features. Previous works use physical network historical information, and they do not pay attention to the characteristics of virtual networks during the embedding process. To address this problem, this paper proposes SoGVNE (Self-organized Green Virtual Network Embedding) which uses previous successful virtual node mapping information to train a self-organizing map (SOM) neural network. Then, it will respond to future requests quickly and with fewer resources. SoGVNE uses virtual networks historical data and is able to embed more than one virtual network concurrently. Using supervised SOM makes the SoGVNE aware of resources with renewable energy and uses them to manage energy consumption. This paper evaluates SoGVNE with reinforcement-learning, Monte-Carlo and Presto methods. The results indicate the high efficiency of the proposed method in learning the node mapping process and achieving high profit and acceptance ratio with low cost.