Reinforcement-Learning based routing for packet-optical networks with hybrid telemetry
This work addresses routing optimization for packet-optical networks, but it appears incremental as it applies existing Q-learning methods to a specific scenario with hybrid telemetry.
The paper tackles the problem of finding optimal routes in packet-optical networks by developing a reinforcement learning algorithm that uses hybrid telemetry data, resulting in dynamic adaptation to changing network conditions like link load and degradation.
This article provides a methodology and open-source implementation of Reinforcement Learning algorithms for finding optimal routes in a packet-optical network scenario. The algorithm uses measurements provided by the physical layer (pre-FEC bit error rate and propagation delay) and the link layer (link load) to configure a set of latency-based rewards and penalties based on such measurements. Then, the algorithm executes Q-learning based on this set of rewards for finding the optimal routing strategies. It is further shown that the algorithm dynamically adapts to changing network conditions by re-calculating optimal policies upon either link load changes or link degradation as measured by pre-FEC BER.