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 (...

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Veröffentlicht in:IEEE/IFIP Network Operations and Management Symposium S. 1 - 6
Hauptverfasser: Esteves, Jose Jurandir Alves, Boubendir, Amina, Guillemin, Fabrice, Sens, Pierre
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 08.05.2023
Schlagworte:
Automation
Deep learning
Deep Reinforcement Learning
Focusing
Heuristics
Integer linear programming
Large-scale infrastructures
Network Functions Virtualization
Network slicing
Optimization
Placement
Reinforcement learning
Scalability
ISSN:2374-9709
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Zusammenfassung: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.
ISSN:2374-9709
DOI:10.1109/NOMS56928.2023.10154353