Microservice deployment strategy based on interaction weight in edge environment

Abstract

This paper proposes a microservice deployment strategy based on interactive weights to optimize the deployment of microservices in edge environments. In an edge computing environment, this strategy is particularly important to reduce data transmission latency and improve processing efficiency as data is processed and stored closer to where the data is generated. The proposed deployment strategy evaluates the dependencies between microservices based on their interaction weights, and models computation and transport latency to minimize the overall latency of applications in the edge network. By introducing an interactive weight factor and constructing an end-to-end delay model of microservices, this strategy can effectively deal with the variability of microservice requests and the diversification of network transmission in edge computing environments. The experimental results show that this strategy is effective in reducing the overall latency of microservice deployment, and can deal with the interactions and dependencies between microservices more effectively than the traditional greedy algorithm (GA), random algorithm (RA) and other interactively aware deployment strategies (such as IntMA). In addition, experimental evaluations on open source microservices applications such as BookInfo validate the effectiveness of the proposed strategy.