Particle Swarm–Grey Wolf Cooperation Algorithm Based on Microservice Container Scheduling Problem

In recent years, microservices have been very widely used as a new application development technology in edge computing, IoT, and cloud computing. Application containerization is one of its core technologies, which allows multiple containers to be deployed within the same physical node. Then single node could provide different services user. How rationally deploy on cluster nodes main research directions nowadays. Although number researchers modeled microservice container scheduling problem proposed effective solutions, there are still shortcomings, for example, slow speed finding optimal solution tendency algorithm fall into local optimality. This paper propose Particle Swarm - Grey Wolf Cooperation Algorithm based Microservice Container Scheduling Problem (PS-GWCA) by using particle swarm optimization (PSO) grey wolf (GWO) multi-core parallel way, enables two algorithms complement each other whole search process through information exchange between populations. early stage, GWO can use global capability guide PSO jump out optimum avoid premature convergence, late enhance pareto frontier. The experimental results show that compared with three algorithms, optimizes 18.07% network transmission cost, 14.67% load balancing, 20.66% 7.5% speed, 5.69% service reliability.