Classification of anomalies in microservices using an XGboost-based approach with data balancing and hyperparameter tuning

Barata, Luís; Lopes, Eurico; Inácio, Pedro; Freire, Mário

Publication

Classification of anomalies in microservices using an XGboost-based approach with data balancing and hyperparameter tuning

2025Preprint

dc.contributor.author	Barata, Luís
dc.contributor.author	Lopes, Eurico
dc.contributor.author	Inácio, Pedro
dc.contributor.author	Freire, Mário
dc.date.accessioned	2025-10-27T17:14:57Z
dc.date.available	2025-10-27T17:14:57Z
dc.date.issued	2025
dc.description.abstract	Microservice architecture has emerged as a leading paradigm for decomposing large monolithic applications into smaller, autonomous services. Although this approach offers many advantages, its complexity, distributed nature, and substantial scale create significant challenges for monitoring and anomaly detection. The vast volume of generated data further exacerbates computational load and detection latency, complicating the identification of anomalies. This study analyses the impact of data balancing and hyperparameter tuning on anomaly classification in microservices and introduces ADMXGB - Anomaly Detection in Microservices using XGBoost, a XGBoost-based framework tailored for anomaly detection in microservices that seamlessly integrates data balancing with hyperparameter tuning. We propose guidelines to determine appropriate threshold values that balance sensitivity with false positives, and show that the framework is model-agnostic, enabling integration with different machine learning algorithms beyond XGBoost. Validation was performed using a four-stage process encompassing preprocessing, training, validation, and testing. ADMXGB demonstrated improvements in both Accuracy and F1-Score, reaching 99.96% in both metrics on the TraceRCA dataset, outperforming the baseline XGBoost method by a margin of 1.46% in Accuracy and 45.62% in F1-Score. Moreover, ADMXGB achieves reductions in execution time (-86.3%) and memory usage (-21.7%), while maintaining an acceptable CPU overhead. These findings highlight the robustness of ADMXGB in delivering high-accuracy classification in a microservice environment.	por
dc.identifier.citation	BARATA, L.M. [et al.] (2025) - Classification of anomalies in microservices using an XGBoost-based approach with data balancing and hyperparameter tuning. IEEE Open Journal of the Computer Society. DOI: 10.1109/OJCS.2025.3622532
dc.identifier.doi	10.1109/OJCS.2025.3622532
dc.identifier.uri	http://hdl.handle.net/10400.11/10342
dc.language.iso	eng
dc.peerreviewed	yes
dc.publisher	IEEE
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.subject	Anomaly Detection
dc.subject	Data Balancing
dc.subject	Oversampling
dc.subject	Undersampling
dc.subject	Hybridsampling
dc.subject	Microservices
dc.subject	Hyperparameter Optimization
dc.title	Classification of anomalies in microservices using an XGboost-based approach with data balancing and hyperparameter tuning	eng
dc.type	preprint
dspace.entity.type	Publication
oaire.citation.title	IEEE Open Journal of the Computer Society
oaire.version	http://purl.org/coar/version/c_ab4af688f83e57aa
person.familyName	Lopes
person.givenName	Eurico
person.identifier.ciencia-id	281C-B830-CF30
person.identifier.ciencia-id	C117-F943-3B96
person.identifier.orcid	0000-0002-6471-4681
person.identifier.orcid	0000-0002-1818-8203
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