Abstract
The increasing complexity and size of software systems have revealed the limitations of monolithic architectures, leading to the adoption of microservices as a more flexible, scalable, and maintainable alternative. This paper introduces an innovative approach to microservices identification and migration from monolithic architecture using advanced multi-objective genetic algorithms. By formulating the microservices identification problem as a multi-objective Optimization task, we harness the power of genetic algorithms to search for Pareto-optimal solutions, ultimately leading to an efficient decomposition of monolithic systems. Our proposed methodology offers a systematic approach to the migration process, ensuring minimal downtime and maximum efficiency. We present real-world case studies showcasing our approach’s successful application alongside examining its limitations and future research directions. This work encourages further exploration and application of multi-objective genetic algorithms in software engineering and system architecture, ultimately simplifying the transition from monolithic to microservices architectures.
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Aljawawdeh, H., Abuezhayeh, S., Qaddoumi, E., Maghrabi, L. (2023). Simplifying Microservices Migration with Advanced Genetic Algorithms. In: Hannoon, A., Mahmood, A. (eds) Artificial Intelligence, Internet of Things, and Society 5.0. Studies in Computational Intelligence, vol 1113. Springer, Cham. https://doi.org/10.1007/978-3-031-43300-9_36
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