Monte Carlo Tree Search with Last-Good-Reply Policy for Cognitive Optimization of Cloud-Ready Optical Networks

Abstract

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.

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Funding

The work of M. Aibin was supported by the National Science Centre, Poland under Grant No. 2016/21/N/ST7/02147. The work of K. Walkowiak was supported by the National Science Centre, Poland under Grant No. 2017/27/B/ST7/00888.

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Correspondence to Michal Aibin.

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Aibin, M., Walkowiak, K. Monte Carlo Tree Search with Last-Good-Reply Policy for Cognitive Optimization of Cloud-Ready Optical Networks. J Netw Syst Manage 28, 1722–1744 (2020). https://doi.org/10.1007/s10922-020-09555-8

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Keywords

  • Elastic optical networks
  • Dynamic routing
  • Cloud services
  • Traffic prediction
  • Machine learning