Unsupervised learning approach for web application auto-decomposition into microservices

Abstract

Nowadays, large monolithic web applications are manually decomposed into microservices for many reasons including adopting a modern architecture to ease maintenance and increase reusability. However, the existing approaches to refactor a monolithic application do not inherently consider the application scalability and performance. We devise a novel method to automatically decompose a monolithic application into microservices to improve the application scalability and performance. Our proposed decomposition method is based on a black-box approach that uses the application access logs and an unsupervised machine-learning method to auto-decompose the application into microservices mapped to URL partitions having similar performance and resource requirements. In particular, we propose a complete automated system to decompose an application into microservices, deploy the microservices using appropriate resources, and auto-scale the microservices to maintain the desired response time. We evaluate the proposed system using real web applications on a public cloud infrastructure. The experimental evaluation shows an improved performance of the auto-created microservices compared with the monolithic version of the application and the manually created microservices.