From Monolith to Microservices: A Comparative Evaluation of Decomposition Frameworks

TL;DR

This paper provides a comprehensive comparison of various microservice decomposition frameworks using a unified evaluation pipeline, highlighting the effectiveness of hierarchical clustering methods like HDBScan in producing balanced, modular microservice architectures.

Contribution

It introduces a standardized evaluation approach for microservice decomposition techniques and compares static, dynamic, and hybrid methods across multiple benchmarks.

Findings

Hierarchical clustering methods, especially HDBScan, outperform others in balanced decompositions.

HDBScan achieves high modularity with minimal communication and interface overhead.

The unified pipeline enables consistent and reproducible evaluation of decomposition approaches.

Abstract

Software modernisation through the migration from monolithic architectures to microservices has become increasingly critical, yet identifying effective service boundaries remains a complex and unresolved challenge. Although numerous automated microservice decomposition frameworks have been proposed, their evaluation is often fragmented due to inconsistent benchmark systems, incompatible metrics, and limited reproducibility, thus hindering objective comparison. This work presents a unified comparative evaluation of state-of-the-art microservice decomposition approaches spanning static, dynamic, and hybrid techniques. Using a consistent metric computation pipeline, we assess the decomposition quality across widely used benchmark systems (JPetStore, AcmeAir, DayTrader, and Plants) using Structural Modularity (SM), Interface Number(IFN), Inter-partition Communication (ICP), Non-Extreme Distribution (NED), and related indicators. Our analysis combines results reported in prior studies with experimentally reproduced outputs from available replication packages. Findings indicate that the hierarchical clustering-based methods, particularly HDBScan, produce the most consistently balanced decompositions across benchmarks, achieving strong modularity while minimizing communication and interface overhead.