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A Case Study on Monolith to Microservices Decomposition with Variational Autoencoder-Based Graph Neural Network

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  • Rokin Maharjan

    (Department of Computer Science, Baylor University, Waco, TX 76798-7141, USA)

  • Korn Sooksatra

    (Department of Computer Science, Baylor University, Waco, TX 76798-7141, USA)

  • Tomas Cerny

    (Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ 85721-0020, USA)

  • Yudeep Rajbhandari

    (Department of Computer Science, Baylor University, Waco, TX 76798-7141, USA)

  • Sakshi Shrestha

    (Department of Computing, East Tennessee State University, Johnson City, TN 37614-1700, USA)

Abstract

Microservice is a popular architecture for developing cloud-native applications. However, decomposing a monolithic application into microservices remains a challenging task. This complexity arises from the need to account for factors such as component dependencies, cohesive clusters, and bounded contexts. To address this challenge, we present an automated approach to decomposing monolithic applications into microservices. Our approach uses static code analysis to generate a dependency graph of the monolithic application. Then, a variational autoencoder (VAE) is used to extract features from the components of a monolithic application. Finally, the C-means algorithm is used to cluster the components into possible microservices. We evaluate our approach using a third-party benchmark comprising both monolithic and microservice implementations. Additionally, we compare its performance against two existing decomposition techniques. The results demonstrate the potential of our method as a practical tool for guiding the transition from monolithic to microservice architectures.

Suggested Citation

  • Rokin Maharjan & Korn Sooksatra & Tomas Cerny & Yudeep Rajbhandari & Sakshi Shrestha, 2025. "A Case Study on Monolith to Microservices Decomposition with Variational Autoencoder-Based Graph Neural Network," Future Internet, MDPI, vol. 17(7), pages 1-19, July.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:7:p:303-:d:1700679
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