Network Slice Resource Mapping Method Based on Discrete Binary Particle Swarm Optimization Algorithm

Abstract

Network slice can meet the needs of network speed, delay, and reliability in different application scenarios, and build a virtual logical network based on business requirements for network functions and security. This technology is one of the key technologies of 5G. Aiming at the needs of network slice diversification, a network slice resource mapping method based on discrete binary particle swarm optimization algorithm was proposed. This method establishes a data model for base station resources and network slice requests, defines the cost function of resource mapping, and converts the resource mapping problem into a mapping matrix optimization problem. The discrete binary particle swarm optimization algorithm is used to solve the optimal mapping scheme at the cost of resource mapping. In order to solve the problem that the traditional discrete binary particle algorithm is easy to fall into the local optimum, a linear reduction of the inertia weight factor is adopted to balance the local and global relationships and improve the optimization accuracy. Simulation results show that the proposed resource mapping method can meet the resource requirements of the network slice, while making the resource mapping cost and mapping failure rate significantly smaller than the current resource mapping strategy.