Microservices architectural design has become increasingly popular due to the enhanced scalability, flexibility, and maintainability of large and complex applications. Machine learning (ML) has emerged as a powerful tool in microservices deployment and management. Although ML techniques have been useful for building microservices-based system architectures, the current literature does not provide clear guidance on which ML techniques developers of these systems might use. This research describes the design and results of a systematic mapping study to identify the ML techniques used in the building of microservices-based systems. The review yielded 193 articles, of which 34 primary studies were selected. Key findings are: (i) Monitoring, diagnostics and observability (MDO) and Resource orchestration and management (ROM) are the most used domains; (ii) Deep Learning (DL) and Unsupervised Learning (UL) are the most used techniques; (iii) Proposed solutions validated through evaluative research dominate the field; (iv) Case studies and experiments are the main empirical strategies; and (v) Public data sets are limited. This effort will enable developers to effectively address the refinement and improvement of software designs using ML methods.

This research will be presented in International Conference of the Chilean Computer Science Society (SCCC)