@inproceedings{66cfd38d4398434ca5227a3dcdeaf656,
title = "Improved monte carlo tree search for virtual network embedding",
abstract = "In this paper, we consider the Virtual Network Embedding (VNE) problem for 5G networks slicing. This consists in optimally allocating multiple Virtual Networks (VN) on a substrate virtualized physical network while maximizing among others, resource utilization, maximum number of placed VNs and network operator's benefit. We solve the online version of the problem where slices arrive over time. We propose the use of the Nested Rollout Policy Adaptation (NRPA) algorithm, a variant of the well known Monte Carlo Tree Search (MCTS). Both algorithms learn by randomly simulating the embedding, but NRPA also learns how to perform better simulations over time. Performance analysis with different scenarios, show that NRPA improves acceptance and reward ratios (by up to 69\% and 65\%). We also show how a smart initialization of the learning process can help improve the results furthermore (up to a 12.5\% increase of acceptance ratio).",
keywords = "5G slicing, AI-enabled networking, Cloud Computing and Networking, Quality of Service, Reinforcement Learning, Virtual Network Embedding",
author = "Maxime Elkael and Hind Castel-Taleb and Badii Jouaber and Andrea Araldo and Aba, \{Massinissa Ait\}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 46th IEEE Conference on Local Computer Networks, LCN 2021 ; Conference date: 04-10-2021 Through 07-10-2021",
year = "2021",
month = oct,
day = "4",
doi = "10.1109/LCN52139.2021.9524975",
language = "English",
series = "Proceedings - Conference on Local Computer Networks, LCN",
publisher = "IEEE Computer Society",
pages = "605--612",
editor = "Lyes Khoukhi and Sharief Oteafy and Eyuphan Bulut",
booktitle = "Proceedings of the IEEE 46th Conference on Local Computer Networks, LCN 2021",
}