A novel reinforcement learning algorithm for virtual network embedding

Network virtualization enables the share of a physical network among multiple virtual networks. Virtual network embedding determines the effectiveness of utilization of network resources. Traditional heuristic mapping algorithms follow static procedures, thus cannot be optimized automatically, leadi...

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Vydané v:Neurocomputing (Amsterdam) Ročník 284; s. 1 - 9
Hlavní autori: Yao, Haipeng, Chen, Xu, Li, Maozhen, Zhang, Peiying, Wang, Luyao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 05.04.2018
Predmet:
Policy gradient
Policy network
Reinforcement learning
Virtual network embedding
Policy gradient
Policy network
Reinforcement learning
Virtual network embedding
ISSN:0925-2312, 1872-8286
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Popis
Shrnutí:Network virtualization enables the share of a physical network among multiple virtual networks. Virtual network embedding determines the effectiveness of utilization of network resources. Traditional heuristic mapping algorithms follow static procedures, thus cannot be optimized automatically, leading to sub-optimal ranking and embedding decisions. To solve this problem, we introduce a reinforcement learning method to virtual network embedding. In this paper, we design and implement a policy network based on reinforcement learning to make node mapping decisions. We use policy gradient to achieve optimization automatically by training the policy network with the historical data based on virtual network requests. To the best of our knowledge, this work is the first to utilize historical requests data to optimize network embedding automatically. The performance of the proposed embedding algorithm is evaluated in comparison with two other algorithms which use artificial rules based on node ranking. Simulation results show that our reinforcement learning is able to learn from historical requests and outperforms the other two embedding algorithms.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2018.01.025