(CLOUD 2020) Capestor: a Service Mesh-based Capacity Estimation Framework for Cloud Applications

Abstract: Due to complex deployment configurations and application logics in cloud computing, microservice architectures are widely used to divide cloud appli-cations into multiple independent microservices communicating with each other through APIs that are not associated with languages and platforms. However, a large amount of various microservices make it difficult for opera-tors to predict the performance of microservices and then estimate cloud ap-plications' capacity. This paper proposes a capacity estimation framework called as Capestor for cloud applications based on a service mesh. Capestor employs a service mesh to place target microservices in isolated containers, simulates workloads, and collect monitoring data related with performance and resources. Then, Capestor employs a lasso regression model to correlate resources and performance, and estimates the capacity of each microservice to plan fine-grained flexible expansion. Finally, we evaluate Capestor with a typical microservice based application. The experimental results show that Capestor can estimate the capacity of microservices, and provide perfor-mance guarantee for applications with low prediction error.

Authors: Yao Sun (Nanjing Institute of Big Date, Jinling Institute of Technology, China); Lun Meng (Hohai University, China); Shudong Zhang (Capital Normal University, China)

Email: myresearch2020@126.com, studyscholar@foxmail.com, zsd@mail.cnu.edu.cn