Adaptive Selecting Algorithm for Runtime Types of Microservices

Li Kang, Binbin Feng, Zhijun Ding

Abstract: In recent years, serverless computing has become increasingly popular in the domain of microservices. Compared to serverful computing, serverless computing significantly reduces developers’ expenses due to its resource elasticity and on-demand allocation features. However, serverless computing suffers from long cold start time and high function invocation latency, leading to suboptimal service performance. Due to the dynamic workload, microservices exhibit varying demands for different runtime types over time, which is overlooked by existing approaches. Therefore, we propose the Adaptive Selecting Algorithm for Runtime Types of microservices, which optimizes resource usage in cloud service providers (CSPs) and ensures efficient execution of developers’ applications. Specifically, the algorithm dynamically switches microservices to the optimal runtime type by analyzing the workload patterns, resource requirements, and execution efficiency of microservices. We conducted experiments on real clusters, demonstrating that algorithm enhances the service quality of microservices while improving their cost efficiency.