Publication Date
Spring 2025
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Genya Ishigaki
Second Advisor
Navrati Saxena
Third Advisor
Katerina Potika
Keywords
GNPy, Reinforcement Learning, Optical Networks, OSNR.
Abstract
This paper presents a novel approach for optimizing network monitoring in optical communication systems using Reinforcement Learning (RL). Assuming a multi-domain architecture with limited domain visibility, we simulate multiple optical connections using an optical communications simulation software, GNPy, obtaining key network metrics to model the system. We developed two RL agents: the first agent selects near-optimal monitoring paths based on network states, and the second agent dynamically adapts its selected paths in response to state changes, such as fiber failures or issues with ROADMs. This adaptive approach allows for continuous improvement of network monitoring, ensuring resilience and efficient fault detection. Experimental results demonstrate the RL agents’ ability to select a set of monitoring paths performing within single-digit percentages of the optimal set.
Recommended Citation
Choudhury, Soham, "Reinforcement Learning-based End-to-End Monitoring Path Selection in Multi-domain Optical Networks" (2025). Master's Projects. 1561.
DOI: https://doi.org/10.31979/etd.98aq-s4bu
https://scholarworks.sjsu.edu/etd_projects/1561
