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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| Vydáno v: | Neurocomputing (Amsterdam) Ročník 284; s. 1 - 9 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
05.04.2018
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| Témata: | |
| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Li, Maozhen Zhang, Peiying Chen, Xu Yao, Haipeng Wang, Luyao |
| Author_xml | – sequence: 1 givenname: Haipeng orcidid: 0000-0003-1391-7363 surname: Yao fullname: Yao, Haipeng email: yaohaipeng@bupt.edu.cn organization: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecom, Beijing, P.R. China – sequence: 2 givenname: Xu surname: Chen fullname: Chen, Xu organization: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecom, Beijing, P.R. China – sequence: 3 givenname: Maozhen surname: Li fullname: Li, Maozhen organization: Department of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UK – sequence: 4 givenname: Peiying surname: Zhang fullname: Zhang, Peiying organization: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecom, Beijing, P.R. China – sequence: 5 givenname: Luyao surname: Wang fullname: Wang, Luyao organization: Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, P.R. China |
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| SubjectTerms | Policy gradient Policy network Reinforcement learning Virtual network embedding |
| Title | A novel reinforcement learning algorithm for virtual network embedding |
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