科研成果详情

With the rapid development of 5G-and-beyond Industrial Internet of Things (IIoT), mobile Edge Computing (MEC) can improve the Quality of Experience (QoE) of end-users and save the energy consumption of mobile end devices by providing computing resources and storage space. However, when these mobile end devices are roaming around different MEC servers' areas, it may cause discontinuity of services. To solve this problem, we propose an effective Edge-Intelligent Service Placement Algorithm (EISPA), which transfers the service placement problem into finding a globally optimal solution via nature-inspired-based Particle Swarm Optimization (PSO). Moreover, we use a shrinkage factor and combine it with the Simulated Annealing (SA) algorithm to adjust the particles' position in our algorithm to avoid falling into a locally optimal solution. Performance analysis results show that the EISPA can reduce the delay and energy consumption of MEC servers, and its performance is better than the existing algorithm.