Leveraging double-agent-based deep reinforcement learning to global optimization of elastic optical networks with enhanced survivability

As the services in elastic optical networks (EONs) are bandwidth-intensive, unpredictable and dynamic, increasing network factors are emerging to affect the performance of network survivable planning and operation from network capacity to efficiency. Most of the traditional protection and restoration approaches may become before long inefficient due to the improvement of a particular network performance metric always at the expense of others. We argue that it would be more beneficial for comprehensive optimization of network performance to consider main network metrics jointly. Moreover, the highly dynamic features of EONs call for the new generation of machine learning-based solutions that are flexible and adaptable to cope with the dynamic nature of services to perform analytics. In this paper, we investigate the problem of global optimization of network performance under survivable EON environment. Specifically, a criterion, named the whole network cost-effectiveness value with survivability (WCES), is defined to measure the overall network performance by balancing the interaction among main network metrics. Then we propose a deep reinforcement learning (DRL) -based heuristic with the objective of improving overall network performance, in which two agents are utilized to provide working and protection schemes converging toward better survivable routing, modulation level and spectrum assignment (S-RMLSA) policies. Numerical results show that the proposed criterion can efficiently measure the overall network performance, and the double-agent DRL-based heuristic can greatly improve WCES while ensuring the network survivability and paying the acceptable extra consumption of request blocking probability.