A Comparative Study of Monolithic and Microservices Architectures in Machine Learning Scenarios

Abstract

Choosing the most suitable architecture for applications is not an easy decision. While the software giants have almost all put in place the microservices architecture, on smaller platforms such decision it is not so obvious. In the healthcare domain and specifically when accomplishing Machine Learning (ML) tasks in this domain, considering its special characteristics, the decision should be made based on specific metrics. In the context of the beHEALTHIER platform, a platform that is able to handle heterogeneous healthcare data towards their successful management and analysis by applying various ML tasks, such research gap was fully investigated. There has been conducted an experiment by installing the platform in three (3) different architectural ways, referring to the monolithic architecture, the clustered microservices architecture exploiting docker compose, and the microservices architecture exploiting Kubernetes cluster. For these three (3) environments, time-based measurements were made for each Application Programming Interface (API) of the diverse platform’s functionalities (i.e., components) and useful conclusions were drawn towards the adoption of the most suitable software architecture.