Unsupervised learning approach for web application auto-decomposition into microservices
Author | Abdullah M. |
Author | Iqbal W. |
Author | Erradi A. |
Available date | 2020-04-02T11:08:04Z |
Publication Date | 2019 |
Publication Name | Journal of Systems and Software |
Resource | Scopus |
ISSN | 1641212 |
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. |
Sponsor | This work was made possible by NPRP grant # 7-481-1-088 from the [Qatar National Research Fund] a member of Qatar Foundation). |
Language | en |
Publisher | Elsevier Inc. |
Subject | Application decomposition Cloud computing Microservices Scalability Web applications |
Type | Article |
Pagination | 243-257 |
Volume Number | 151 |
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