Optimizing Network Slicing in Distributed Large Scale Infrastructures: From Heuristics to Controlled Deep Reinforcement Learning.

This paper summarizes the PhD thesis and the 10 associated publications on the optimization of network slice placement in large-scale distributed infrastructures by focusing on online heuristics and approaches based on Deep Reinforcement Learning (DRL). First, we rely on Integer Linear Programming (ILP) to propose a data model for on-Edge and on-network slice placement. Second, we leverage an approach called Power of Two Choices (P2C) to propose an online heuristic adapted to support placement on large-scale distributed infrastructures while incorporating Edge-specific constraints like latency. Finally, we investigate the use of Machine Learning (ML) methods, specifically DRL, to increase the scalability and automation of network slice placement by considering a multi-objective optimization approach to the problem. We will go through the extensive evaluation work that provide encouraging results about the advantages of the proposed approaches when used in realistic network scenarios.