Simplifying microservices migration with advanced genetic algorithms

Aljawawdeh, Hamzeh; Abuezhayeh, Sami; Qaddoumi, Eman; Maghrabi, Louai

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.

• Online Publication Date: Nov 9, 2023
• Publication Date: Nov 9, 2023
• Deposit Date: Nov 25, 2023
• Publisher: Springer Verlag
• Pages: 441-452
• Series Title: Studies in Computational Intelligence
• Book Title: Artificial Intelligence, Internet of Things, and Society 5.0
• ISBN: 9783031432996
• DOI: https://doi.org/10.1007/978-3-031-43300-9_36
• Public URL: https://uwe-repository.worktribe.com/output/11462673
• Publisher URL: https://doi.org/10.1007/978-3-031-43300-9_36