Application Deployment in Mobile Edge Computing Environment Based on Microservice Chain

Mobile edge computing (MEC) has become an extremely hot topic in recent years. Mobile edge cloud relies on storage and computing resources on network edge to provide users with delay-sensitive services. However, the transmission delay among microservices and the load of the servers tend to increase due to improper service placement and unreasonable resource allocation under MEC. In this paper, an edge service placement strategy based on an improved fast non-domination sorted genetic algorithm is proposed. First, a microservice placement optimization model is built with the goal of minimizing the average transmission delay and the load balance degree. Then, a genetic algorithm-based microservice placement approach called GA-MSP using improved NSGA-II is studied under the premise that a single service instance is deployed only on one container. The experiments show that the proposed GA-MSP approach is able to achieve low delay and load balance effectively, and ultimately deploy services based on the resulting sets after convergence, which outperforms several other existing representative methods.