Context-Aware Runtime Type Prediction for Heterogeneous Microservices

Serverless function is becoming increasingly popular as a new runtime type for application execution. However, it is not suitable for arbitrary microservices. Different components in microservice applications are often suitable to be deployed with different runtime types according to their own attributes and workload characteristics. However, the complex topology of microservice applications often leads to difficulty in determining the optimal runtime types of microservices, and the existing container-based microservice systems only support a single runtime type. Therefore, we propose a targeted heterogeneous runtime unified orchestration solution to address the above problems. First, we propose an execution need characterization model for microservice applications and introduce a microservice resource sensitivity type analysis method. Second, we propose a graph neural network-based approach for context-aware accurate prediction of heterogeneous microservice runtime types, which synthesizes the characteristics of each component and the correlation relationships between components to determine the optimal runtime type specific to each microservice. Third, we design and implement a unified orchestration system for heterogeneous microservice applications to support user-independent automated orchestration of serverful and serverless microservices. Finally, we validate the advantages of the system in terms of service performance and cost efficiency through experiments on real clusters.