The rapid development of Cloud Computing and Content Delivery Networks (CDNs) brings a significant increase in data transfers that leads to new optimization challenges in inter-data center networks. In this article, we focus on the cross-stratum optimization of an inter-data center Elastic Optical Network (EON). We develop an optimization approach that employs machine learning Monte Carlo Tree Search (MCTS) algorithm for the simulation of future traffic to improve the performance of the network regarding the request blocking and the operational cost. The key novelty of our approach is using various selection strategies applied to the phase of building a search tree under different network scenarios. We evaluate the performance of these selection strategies using representative topologies and real-data provided by Amazon Web Services. The main conclusion is that the approach based on the policy of Last-Good-Reply with Forgetting enables more efficient cloud resource allocation, which results in lower request blocking, thus, reduces the operational cost of the network.

中文翻译：

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用于云就绪光网络认知优化的具有最后良好回复策略的蒙特卡洛树搜索

云计算和内容交付网络 (CDN) 的快速发展带来了数据传输的显着增加，这给数据中心间网络带来了新的优化挑战。在本文中，我们重点介绍数据中心间弹性光网络 (EON) 的跨层优化。我们开发了一种优化方法，该方法采用机器学习蒙特卡罗树搜索 (MCTS) 算法来模拟未来流量，以提高网络在请求阻塞和运营成本方面的性能。我们的方法的关键新颖之处在于在不同网络场景下使用应用于构建搜索树阶段的各种选择策略。我们使用 Amazon Web Services 提供的代表性拓扑和真实数据来评估这些选择策略的性能。