Explainable artificial intelligence-based framework for efficient content placement in elastic optical networks

Abstract

The rapid development of telecommunication networks brings new optimization problems and the urgent need for dedicated and highly efficient solution methods. Recently, the idea of aiding network optimization with machine learning (ml) algorithms has gained more and more attention in the research society. Despite numerous successful applications of these methods, their adaption in real networks and systems is hindered due to the lack of a full explainability of their decisions and, in turn — the lack of trust. Hopefully, these aspects may be addressed by explainable artificial intelligence methods (xai). In this paper, we study an essential problem of the anycast content placement. Having a set of physical data centers (dcs) located in selected network nodes and a set of different contents (services), the task is to decide in which dcs place each of the contents in order to improve the optical network performance (measured as a bandwidth blocking probability (bbp)). To this end, we propose a dedicated ml-based framework, which approaches the placement problem as a supervised learning task of predicting network’s bbp for a content placement configuration. We perform extensive numerical experiments to tune the framework, considering five supervised learning algorithms and three comparison metrics. We also use explainable artificial intelligence methods to interpret the models’ decisions and draw general conclusions regarding beneficial content placement in a real network. Lastly, we compare the performance of the proposed ml-based placement framework with three reference methods. The results prove our approach’s extremely high efficiency, which reduced the bbp significantly compared to the best reference approach. Depending on the network settings and the offered traffic volume, the framework allowed to serve up to 47% of the traffic that would be rejected by the best reference method (corresponding to 3.76 Tbps of data).