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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| Vydáno v: | IEEE/IFIP Network Operations and Management Symposium s. 1 - 6 |
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| Jazyk: | angličtina |
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08.05.2023
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| ISSN: | 2374-9709 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Guillemin, Fabrice Esteves, Jose Jurandir Alves Sens, Pierre Boubendir, Amina |
| Author_xml | – sequence: 1 givenname: Jose Jurandir Alves surname: Esteves fullname: Esteves, Jose Jurandir Alves email: josejurandir.alves@mercadolivre.com organization: Mercado Livre,Brazil – sequence: 2 givenname: Amina surname: Boubendir fullname: Boubendir, Amina email: amina.boubendir@airbus.com organization: Airbus Defence and Space,France – sequence: 3 givenname: Fabrice surname: Guillemin fullname: Guillemin, Fabrice email: fabrice.guillemin@orange.com organization: Orange Labs,France – sequence: 4 givenname: Pierre surname: Sens fullname: Sens, Pierre email: pierre.sens@lip6.fr organization: Sorbonne Université / CNRS / Inria,France,LIP6 |
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| SubjectTerms | Automation Deep learning Deep Reinforcement Learning Focusing Heuristics Integer linear programming Large-scale infrastructures Network Functions Virtualization Network slicing Optimization Placement Reinforcement learning Scalability |
| Title | Optimizing Network Slicing in Distributed Large Scale Infrastructures: From Heuristics to Controlled Deep Reinforcement Learning |
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