An In-Depth Study of Microservice Call Graph and Runtime Performance

Abstract

Loosely-coupled and light-weight microservices running in containers are replacing monolithic applications gradually. Understanding the characteristics of microservices is critical to make good use of microservice architectures. However, there is no comprehensive study about microservice and its related systems in production environments so far. In this paper, we present a solid analysis of large-scale deployments of microservices at Alibaba clusters. Our study focuses on the characterization of microservice dependency as well as its runtime performance. We conduct an in-depth anatomy of microservice call graphs to quantify the difference between them and traditional DAGs of data-parallel jobs. In particular, we observe that microservice call graphs are heavy-tail distributed and their topology is similar to a tree and moreover, many microservices are hot-spots. We also discover that the structure of call graphs for long-term developed applications is much simpler so as to provide better performance. Our investigation on microservice runtime performance indicates most microservices are much more sensitive to CPU interference than memory interference. Moreover, we design resource management policies to efficiently tune memory resources.

Huanle Xu

2021.01 - Current

I am currently an assistant professor from the Department of Computer and Information Scicence, Univeristy of Macau.

Chengzhi Lu

2025 - Current

My research interests include resource management and task scheduling in the large scale cluster.