Kapak Resmi

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

Kaydedildi:
Yayımlandı:Future Internet vol. 17, no. 7 (2025), p. 303-322
Yazar: Maharjan Rokin
Diğer Yazarlar: Korn, Sooksatra, Cerny Tomas, Rajbhandari Yudeep, Shrestha Sakshi
Baskı/Yayın Bilgisi:
MDPI AG
Konular:
Machine learning
Software
Static code analysis
Artificial intelligence
Graphs
Communication
Graph neural networks
Neural networks
Social networks
Decomposition
Architecture
Automation
Case studies
Online Erişim:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiketler: Etiketle
Etiket eklenmemiş, İlk siz ekleyin!
Özet: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.
ISSN:1999-5903
DOI:10.3390/fi17070303
Kaynak:ABI/INFORM Global