μP: A Development Framework for Predicting Performance of Microservices by Design

Microservice (MS) architecture has become a popular paradigm in software engineering and has been embraced in the industry (e.g., Amazon, Netflix) for cloud-based applications with crucial performance requirements. Surprisingly, assessing how the MS designs affect performance is still a challenging issue, which is generally tackled by extensive and expensive profiling. In this paper, we propose μP , a novel development framework for MS applications where performance can be predicted by design. μP offers an API that automatically generates a per-formance model based on Layered Queuing Networks (LQNs) without requiring any development effort beyond writing the actual system code. The model can then be queried to predict performance metrics such as response time and utilization of individual microservices. We validate μP on four benchmarks taken from the literature. The results show the effectiveness of μP in accurately predicting performance due to increasing user load, vertical and horizontal scaling. We report prediction errors for response times consistently lower than 10% across a wide range of operating conditions.