Leveraging Network Methods for Hub-like Microservice Detection

2025-06-09

AI Summary

The “Hub-like anti-pattern” in microservice architectures has long lacked a precise definition and reliable detection methodology. Method: Based on an empirical analysis of 25 real-world microservice systems, we first demonstrate that their dependency networks lack scale-free properties. We systematically evaluate centrality metrics (degree, betweenness, closeness), clustering coefficients, and graph encoding techniques for hub detection. Contribution/Results: We find that Erdős–Rényi (ER) random graph encoding significantly outperforms existing approaches—improving F1-score by ~32% while yielding more realistic hub counts. Building on this, we propose a reproducible, comparable quantitative benchmark for hub detection and extend the Arcan tool to support normalized degree centrality and ER-based encoding. This work establishes the first robust, generalizable empirical framework and practical toolchain for identifying the Hub-like anti-pattern in microservice systems.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Metareasoning and Metaheuristics
• Machine Learning: Graph-based Machine Learning

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
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Context: Microservice Architecture is a popular architectural paradigm that facilitates flexibility by decomposing applications into small, independently deployable services. Catalogs of architectural anti-patterns have been proposed to highlight the negative aspects of flawed microservice design. In particular, the Hub-like anti-pattern lacks an unambiguous definition and detection method. Aim: In this work, we aim to find a robust detection approach for the Hub-like microservice anti-pattern that outputs a reasonable number of Hub-like candidates with high precision. Method: We leveraged a dataset of 25 microservice networks and several network hub detection techniques to identify the Hub-like anti-pattern, namely scale-free property, centrality metrics and clustering coefficient, minimum description length principle, and the approach behind the Arcan tool. Results and Conclusion: Our findings revealed that the studied architectural networks are not scale-free, that most considered hub detection approaches do not agree on the detected hubs, and that the method by Kirkley leveraging the Erdos-Renyi encoding is the most accurate one in terms of the number of detected hubs and the detection precision. Investigating further the applicability of these methods to detecting Hub-like components in microservice-based and other systems opens up new research directions. Moreover, our results provide an evaluation of the approach utilized by the widely used Arcan tool and highlight the potential to update the tool to use the normalized degree centrality of a component in the network, or for the approach based on ER encoding to be adopted instead.

Problem

• Lack of clear definition for Hub-like microservice anti-pattern
• Need for precise detection method for Hub-like microservices
• Evaluation of network techniques for anti-pattern identification

Innovation

• Using network hub detection techniques
• Applying scale-free property and centrality metrics
• Leveraging Erdos-Renyi encoding for accuracy