Multi-Objective Optimization in Disaster Backup with Reinforcement Learning

Disaster backup, which occurs over long distances and involves large data volumes, often leads to huge energy consumption and the long-term occupation of network resources. However, existing work in this area lacks adequate optimization of the trade-off between energy consumption and latency. We consider the one-to-many characteristic in disaster backup and propose a novel algorithm based on multicast and reinforcement learning to optimize the data transmission process. We aim to jointly reduce network energy consumption and latency while meeting the requirements of network performance and Quality of Service. We leverage hybrid-step Q-Learning, which can more accurately estimate the long-term reward of each path. We enhance the utilization of shared nodes and links by introducing the node sharing degree in the reward value. We perform path selection through three different levels to improve algorithm efficiency and robustness. To simplify weight selection among multiple objectives, we leverage the Chebyshev scalarization function based on roulette to evaluate the action reward. We implement comprehensive performance evaluation with different network settings and demand sets and provide an implementation prototype to verify algorithm applicability in a real-world network structure. The simulation results show that compared with existing representative algorithms, our algorithm can effectively reduce network energy consumption and latency during the data transmission of disaster backup while obtaining good convergence and robustness.