Adaptive burst routing in optical burst switching networks via graph-derived structural features and reinforcement learning.

Abstract

Optical Burst Switching (OBS) networks require adaptive and interpretable routing mechanisms to handle dynamic traffic variation and structural complexity. In this work, they aim to design a benchmark-oriented framework, called OBS-GraphSyn-2025, that combines graph-derived structural modeling, traffic temporal representation, and reinforcement learning based on PPO to evaluate the adaptivity of burst routing. It converts 3.57 M real traffic flows into 199,870 routing flows (with topology information), 79,133 burst-level routing states, and 15,000 PPO-compatible samples. A Traffic Burst Complexity Index-Graph (TBCI-G) is proposed, which combines the routing state complexity metrics of hop count, path complexity, edge load, and route uniqueness to quantify routing complexity. The PPO policy is based on fused states across time and space, and is able to adaptively route under controlled benchmark conditions using reward signals. The results of experimental evaluation over 250 episodes show stable learning performance, with a mean reward of 0.5610 ± 0.0068, throughput of 0.6717 ± 0.0709, and stability score of 0.5856 ± 0.0627. The scalability analysis showed that the runtime increases by a controlled amount (0.32-1.56 s) and the reward retention remains fairly unchanged (0.97-1.00). The consistency of the policy behavior was assessed by reproducibility, and resulted in a coefficient of variation of less than 1.20% in 30 independent runs. The findings show that OBS-GraphSyn-2025 offers a scalable, interpretable, and reproducible complexity-aware metric for evaluating routing-state in OBS-inspired networks while abstracting from the constraints of the optical-layer.