Generative AI-based Adaptation in Microservices Architectures: A Systematic Mapping Study
Résumé
Microservices have seen widespread adoption in academia and industry. Despite their benefits, challenges persist in resilience, performance, scalability, and adaptation to dynamic contexts. Generative AI (GenAI) has emerged as a promising approach to address these issues, though concerns remain about the suitability of various models and potential drawbacks. To assess the state of the art, we conducted a systematic mapping study analyzing 22 primary studies. Results reveal significant potential of GenAI in enhancing microservice adaptation, with emphasis on Large Language Models and optimization techniques. Applications primarily target maintenance and monitoring, especially anomaly management. This study also highlights research gaps and outlines future directions to advance GenAI integration for more resilient and autonomous microservices architectures.
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