Learning-Based Cognitive Hitless Spectrum Defragmentation for Dynamic Provisioning in Elastic Optical Networks

Abstract

The rapid evolution of data transmission has posed unprecedented challenges to the dynamic resource provisioning in optical networks. In this letter, we investigate the dynamic spectrum defragmentation problem in elastic optical networks, and propose a novel deep reinforcement learning based solution Deep-DF to achieve self-adaptive spectrum optimization. Compared to the existing pre-fixed heuristics tailored to only immediate optimizations for the current network state, the key advantage of Deep-DF is that it can be trained to identify and perform hitless defragmentation operations that lead to longer-term optimal dynamic request provisioning, and continuously reoptimize and readapt as traffic changes. The simulation results validate the superior performance of the proposed Deep-DF over the benchmark heuristics, and its robustness against heterogeneous and non-stationary traffic distributions and traffic changes.