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Unsupervised learning approach for web application auto-decomposition into microservices
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DOI：10.1016/j.jss.2019.02.031.png)
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Abstract
En 中文
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. (C) 2019 Elsevier Inc. All rights reserved.
Keywords:
Application decomposition
Scalability
Microservices
Web applications
Cloud computing
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