Group search optimization-assisted deep reinforcement learning intelligence decision for virtual network mapping

Abstract

Virtual network mapping (VNM), as a key technology in network virtualization, has received widespread attention due to its ability to instantiate network services on infrastructure. However, existing VNM technologies have drawbacks, such as poor dynamic mapping processes, single search strategies, and low resource utilization. In this end, we propose a novel group search optimization-assisted deep reinforcement learning (DRL) intelligence decision for virtual network mapping, GSRL-VNM. In this algorithm, we first formalize the deep reinforcement learning model of VNM and describe the dynamic characteristics of VNM process. Then, in order to effectively reduce resource fragmentation and improve the mapping success rate in VNM process, group search optimization (GSO), a swarm intelligent optimization algorithm with excellent global search ability, is utilized to assist deep reinforcement learning intelligent decision-making by improving convergence speed and optimal value. The simulation results show that the proposed GSRL-VNM algorithm outperforms the existing baseline algorithms in terms of acceptance rate, link pressure, long-term average cost, and average revenue.