On the Robustness of Controlled Deep Reinforcement Learning for Slice Placement

The evaluation of the impact of using Machine Learning in the management of softwarized networks is considered in multiple research works. In this paper, we propose to evaluate the robustness of online learning for optimal network slice placement. A major assumption in this study is to consider that slice request arrivals are non-stationary. We precisely simulate unpredictable network load variations and compare two Deep Reinforcement Learning (DRL) algorithms: a pure DRL-based algorithm and a heuristically controlled DRL as a hybrid DRL-heuristic algorithm, in order to assess the impact of these unpredictable changes of traffic load on the algorithms performance. We conduct extensive simulations of a large-scale operator infrastructure. The evaluation results show that the proposed hybrid DRL-heuristic approach is more robust and reliable than pure DRL in real network scenarios.

Automated summary

This paper evaluates the impact of using Machine Learning in the management of softwarized networks. The study focuses on the robustness of online learning for optimal network slice placement and compares different Deep Reinforcement Learning algorithms. The results demonstrate the superiority of the proposed hybrid algorithm.