An Effective Edge-Intelligent Service Placement Technology for 5G-and-Beyond Industrial IoT

With the rapid development of wireless communication, traditional cloud computing cannot fully support low-latency services, especially in sensor networks. Mobile edge computing (MEC) can improve the quality of experience of end users and save the energy consumption of mobile end devices by providing computing resources and storage space. However, it may cause discontinuity of services if these mobile end devices roam around different MEC servers' areas. To solve the aforementioned problem, in this article, we propose an effective edge-intelligent service placement algorithm (EISPA), which transforms the service placement problem into finding a globally optimal solution via nature-inspired particle swarm optimization (PSO). Moreover, we use a shrinkage factor and combine it with the simulated annealing (SA) algorithm to adjust the position of particles in our algorithm, which aims to avoid falling into an optimal local solution to a certain extent. Performance analysis results show that the EISPA is approaching the optimal enumeration collaborative computation offloading algorithm, and system cost under energy constraints is 83.6%, 20.4%, and 20.3% lower than that in Only Local, Finding the Nearest Edge, and the genetic SA-based PSO algorithms, respectively, which proves that the EISPA has better performance.

Automated summary

With the rapid development of wireless communication, traditional cloud computing is not sufficient for low-latency services. Mobile edge computing (MEC) can enhance the user experience and reduce energy consumption. In this article, an edge-intelligent service placement algorithm (EISPA) is proposed, which utilizes nature-inspired particle swarm optimization (PSO) to find the global optimal solution. The algorithm also incorporates a shrinkage factor and simulated annealing (SA) to avoid falling into local optima. Performance analysis results demonstrate that the EISPA outperforms other algorithms in terms of system cost under energy constraints.