부산대학교 도서관

In the wake of advancements in big data, cloud computing, and the Internet of things, software functionalities are constantly evolving to cater to a diverse and growing set of user needs. This rapid pace of data updates and the introduction of new modules can destabilize and imbalance traditional monolithic architectures. Consequently, microservices architecture (MSA), with its independent deployment service capabilities, has been proposed as a solution. MSA offers significant advantages in scalability and maintainability. However, a standard specific definition of MSA remains elusive due to its composition being contingent on specific business logic and varying business scenario requirements. These differing requirements inevitably lead to unique MSA patterns. This study aims to presents the cost-effective and effort-based prediction model for the most influential challenges of migration from monolithic to MSA using a nature-inspired optimization algorithm, i.e., genetic algorithm (GA). Moreover, future research directions are suggested in the realm of microservices architecture.