Intelligent task scheduling for cutover in optical networks

With the rapid expansion of optical networks, the number of network cutover tasks has increased significantly. Manual scheduling of cutover tasks typically takes several days, leading to inefficient resource utilization. To address this issue, we propose systematic intelligent task scheduling methods of cutover tasks in optical networks for what we believe to be the first time. The rules and attributes of cutover tasks are abstracted into graphical representations or mathematical expressions. Then, the scheduling process employs layered topological scheduling methods based on greedy heuristic algorithm, maximal cliques and particle swarm optimization. The scheduling results are verified in the real-time resource library by the conflict verification method. When conflicts arise, the scheduling process is iterated to find a conflict-free schedule, and prior rules are updated. The field trial results of national optical networks demonstrated that our proposed intelligent schedule method realizes second-level scheduling without conflicts. Compared with the layered topological scheduling method based on greedy algorithm, the scheduling methods based on maximal clique and particle swarm optimization can reduce the conflict rate by 20% and 30%, enhance the ability of conflict adjustment by 12% and 43%, and improve scheduling efficiency by 12% and 9%, respectively. The proposed intelligent scheduling methods can also autonomously generate multi-period conflict-free schedules.