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Security-Aware Virtual Network Embedding Algorithm Based on Reinforcement Learning
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DOI：10.1109/TNSE.2020.2995863.png)
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摘要
En 中文
Virtual network embedding (VNE) algorithm is always the key problem in network virtualization (NV) technology. At present, the research in this field still has the following problems. The traditional way to solve VNE problem is to use heuristic algorithm. However, this method relies on manual embedding rules, which does not accord with the actual situation of VNE. In addition, as the use of intelligent learning algorithm to solve the problem of VNE has become a trend, this method is gradually outdated. At the same time, there are some security problems in VNE. However, there is no intelligent algorithm to solve the security problem of VNE. For this reason, this paper proposes a security-aware VNE algorithm based on reinforcement learning (RL). In the training phase, we use a policy network as a learning agent and take the extracted attributes of the substrate nodes to form a feature matrix as input. The learning agent is trained in this environment to get the mapping probability of each substrate node. In the test phase, we map nodes according to the mapping probability and use the breadth-first strategy (BFS) to map links. For the security problem, we add security requirements level constraint for each virtual node and security level constraint for each substrate node. Virtual nodes can only be embedded on substrate nodes that are not lower than the level of security requirements. Experimental results show that the proposed algorithm is superior to other typical algorithms in terms of long-term average return, long-term revenue consumption ratio and virtual network request (VNR) acceptance rate.
Keyword:
Security
Substrates
Heuristic algorithms
Feature extraction
Bandwidth
Training
Classification algorithms
Virtual network embedding
Network virtualization
Intelligent learning algorithm
Network security
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期刊
I
IF:
7.9
论文数:
2.5K
被引数:
10.0K
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CHINA COMMUNICATIONS
CHINA COMMUNICATIONSIF3.1
PREAI
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