From Monolith to Microservices: A Comparative Evaluation of Decomposition Frameworks

2026-01-30

AI Summary

This work addresses the lack of unified and reproducible evaluation criteria for service boundary identification in the migration from monolithic systems to microservices. It presents the first systematic comparison of mainstream microservice decomposition approaches—static, dynamic, and hybrid—within a consistent experimental framework. Using standardized metric computation procedures and multiple benchmark systems (JPetStore, AcmeAir, DayTrader, Plants), the study evaluates key dimensions including structural modularity (SM), interface count (IFN), inter-component communication (ICP), and non-extreme distribution (NED). Experimental results demonstrate that HDBScan with hierarchical clustering consistently yields highly cohesive and loosely coupled microservice partitions across benchmarks, achieving an optimal trade-off between modularity strength and communication overhead, thereby significantly enhancing the objectivity and reproducibility of microservice decomposition evaluation.

Technology Category

• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Machine Learning: Scalability of ML Systems
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

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.

Problem

• microservice decomposition
• service boundaries
• monolithic architecture
• software modernisation
• benchmark evaluation

Innovation

• microservice decomposition
• comparative evaluation
• modularity metrics
• HDBScan
• software modernization