MPCSM: Microservice Placement for Edge-Cloud Collaborative Smart Manufacturing

Latency-aware service placement is promising in reducing the overall service response latency of proliferating edge-cloud collaborative smart manufacturing systems. However, intuitive latency estimators used by existing service placement approaches cannot accurately depict the nonlinear end-to-end (E2E) latency of multihop microservices with complex dependencies, which is severely hindering the effectiveness of latency-aware service placement. To address this issue, in this article, we present a microservice placement mechanism for edge-cloud collaborative smart manufacturing (MPCSM), where a microservice placement algorithm latency-aware edge-cloud collaborative placement supported by an accurate data-driven E2E latency estimation method is proposed. We build a real-world collaborative prototype, and conduct a case study on semiconductor manufacturing to elaborate the construction of our latency estimator. Results of extensive experiments demonstrate that the error of our E2E latency estimator is up to 10x less than that of existing ones, and the overall service latency with MPCSM is up to 10x less than that with existing service placement approaches.