Open AccessService restoration in multi-modal optical transport networks with reinforcement learning
Author(s) -
Zhao Zhen,
Yongli Zhao,
Yajie Li,
Rui Wang,
Xinghua Li,
Dahai Han,
Zhang Jie
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.417440
Subject(s) - computer science , reinforcement learning , benchmark (surveying) , service (business) , image restoration , computer network , distributed computing , artificial intelligence , image processing , image (mathematics) , economy , geodesy , economics , geography
Optical transport networks (OTNs), while increasingly popular, can be affected in ways that are challenging to restore efficiently. This study investigates the problem of service restoration under OTNs with an optical channel data unit (ODU)-k switching capability (OTN-OSC) environment. An advantage actor-critic-based service restoration (A2CSR) algorithm is presented with the objective of increasing the service restoration rate. In our experimental setup, A2CSR uses the advanced image recognition model MobileNetV2 and an advantage actor-critic reinforcement learning algorithm. Simulation results show that the proposed A2CSR algorithm can achieve better blocking probability and resource utilisation than the benchmark algorithm (first fit (FF)), and the restoration time is within the acceptable range.