Adopting microservice architecture: A decision support model based on genetically evolved multi-layer FCM

Abstract

Microservice architectures foster the development of applications as suites of small, autonomous and conversational services, which are then easy to understand, deploy and scale. However, one of the problems nowadays is that microservices introduce new complexities to the system and, despite the hype, many factors should be considered when deciding whether to use them or not. This paper introduces a novel decision and analysis model with enhanced interpretative and explanatory capabilities. The model is conceived by identifying the key concepts and factors in deciding whether to adopt microservice architectures, or not, through literature review and experts’ feedback from the industry and academia. These concepts are organized as a Multi-Layer Fuzzy Cognitive Map (MLFCM), a graph-based computational intelligent model. A new formulation is proposed, along with a novel genetically evolved algorithm, both aiming at improving the model in terms of performance, bias resilience and explainability. The model is evaluated and calibrated through a series of executions over real and synthetic scenarios. The application of static and dynamic analyses, in conjunction with the incorporation of the evolutionary approach, guide the identification of the prevailing factors that regulate the adoption of a microservice architecture and allow the interpretation of the importance of each concept. Finally, an industrial scenario leverages the assessment of the model's applicability and efficacy, highlighting some interesting results.