A Multi-Objective Virtual Network Migration Algorithm Based on Reinforcement Learning

Virtual network migration (VNM) helps improve network performance by remapping a subset of virtual nodes or links to physical infrastructure, aligning the resource allocation to the virtual network’s changing conditions. However, existing VNM methods neglect integrating multiple objectives that affect network performance, such as energy, communication, migration, and service level agreement violation (SLAV). It is challenging to make VNM decisions to optimize the overall objective in a large-scale cloud environment. This article establishes a multi-objective optimization model and proposes a multi-objective VNM algorithm called MiOvnm. The MiOvnm employs the double deep Q-learning approach to cope with ample state space. It also applies an action selection method called actfilter to deal with large-scale action space. The MiOvnm finds the migration action with optimal potential reward from the candidate action set. Simulation results demonstrate the superiority of our MiOvnm to the state-of-the-art methods. More specifically, MiOvnm reduces average SLAV, communication cost, and total cost by 24.32%, 4.95%, and 12.45%, respectively. Furthermore, evaluation results in a real-world OpenStack platform reveal that making full use of computation and network resources, the MiOvnm reduces the completion time of computation- and network-intensive benchmarks by 11.35% and 10.31%, respectively, with a total cost reduction of 26.02%.