Optimization of routing and wavelength optimization algorithm for optical transport network based on reinforcement learning

Aiming at the routing and wavelength problems of dynamic services in optical transport network, a deep routing wavelength assignment algorithm based on reinforcement learning is proposed. The algorithm is based on a software defined network architecture, flexibly adjusts and controls the optical transport network through reinforcement learning, and realizes the optimization of the optical network routing wavelength assignment strategy. For the problem of routing selection, combined with the wavelength usage on the link, the A3C algorithm is used to select the appropriate route to minimize the blocking rate; for the problem of wavelength assignment, the first fit algorithm is used to select the wavelength. Considering multiple indicators such as blocking rate, resource utilization, policy entropy, value loss, execution time, and speed of algorithm convergence, the 14-node NSFNET network topology simulation experiment is implemented. The results show that when the channel contains 18 wavelengths, compared with the traditional KSP-FF algorithm, the blocking rate of this routing wavelength assignment algorithm is reduced by 0.06, and the resource utilization rate is increased by 0.02, but the execution time is increased. When the number of wavelengths exceeds 45, compared with KSP-FF, the proposed algorithm maintains the blocking rate and resource utilization, while the execution time begins to decrease. When the number of wavelengths is 58, compared with KSP-FF, the proposed algorithm's execution time is reduced by 0.07 ms. It can be seen that the proposed algorithm optimizes the routing and wavelength assignment.