Micro Frontend Based Performance Improvement and Prediction for Microservices Using Machine Learning

Abstract

Microservices has become a buzzword in industry as many large IT giants such as Amazon, Twitter, Uber, etc have started migrating their existing applications to this new style and few of them have started building their new applications with this style. Due to increasing user requirements and the need to add more business functionalities to the existing applications, the web applications designed using the microservices style also face a few performance challenges. Though this style has been successfully adopted in the design of large enterprise applications, still the applications face performance related issues. It is clear from the literature that most of the articles focus only on the backend microservices. To the best of our knowledge, there has been no solution proposed considering micro frontends along with the backend microservices. To improve the performance of the microservices based web applications, in this paper, a new framework for the design of web applications with micro frontends for frontend and microservices in the backend of the application is presented. To assess the proposed framework, an empirical investigation is performed to analyze the performance and it is found that the applications designed with micro frontends with microservices have performed better than the applications with monolithic frontends. Additionally, to predict the performance of microservices based applications, a machine learning model is proposed as machine learning has wide applications in software engineering related activities. The accuracy of the proposed model using different metrics is also presented.