A Robust Monte-Carlo-Based Deep Learning Strategy for Virtual Network Embedding
2022 IEEE 47th Conference on Local Computer Networks (LCN)(2022)
摘要
Network slicing is one of the building blocks in Zero Touch Networks. It mainly consists in a dynamic deployment of services in a substrate network. However, the Virtual Network Embedding (VNE) algorithms used generally follow a static mechanism, which results in sub-optimal embedding strategies and less robust decisions. Some reinforcement learning algorithms have been conceived for a dynamic decision, while being time-costly. In this paper, we propose a combination of deep Q-Network and a Monte Carlo (MC) approach. The idea is to learn, using DQN, a distribution of the placement solution, on which a MC-based search technique is applied. This improves the solution space exploration, and achieves a faster convergence of the placement decision, and thus a safer learning. The obtained results show that DQN with only 8 MC iterations achieves up to 44% improvement compared with a baseline First-Fit strategy, and up to 15% compared to a MC strategy.
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关键词
Deep Reinforcement Learning,Monte-Carlo,Dynamic service placement,Network slicing
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