Anomaly Detection in Microservice-Based Systems

Currently, distributed software systems have evolved at an unprecedented pace. Modern software-quality requirements are high and require significant staff support and effort. This study investigates the use of a supervised machine learning model, a Multi-Layer Perceptron (MLP), for anomaly detection...

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Bibliographic Details
Published in:Applied sciences 2023-07, Vol.13 (13), p.7891
Main Authors: Nobre, João, Pires, E. J. Solteiro, Reis, Arsénio
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Anomalies
anomaly detection
Automation
Business metrics
Data integrity
Datasets
failure detection
Learning algorithms
Machine learning
microservices
Monitoring
Monitoring systems
multi-service applications
Multilayer perceptrons
Multilayers
Neural networks
Software
Software quality
Supervised learning
Web applications
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Summary:Currently, distributed software systems have evolved at an unprecedented pace. Modern software-quality requirements are high and require significant staff support and effort. This study investigates the use of a supervised machine learning model, a Multi-Layer Perceptron (MLP), for anomaly detection in microservices. The study covers the creation of a microservices infrastructure, the development of a fault injection module that simulates application-level and service-level anomalies, the creation of a system monitoring dataset, and the creation and validation of the MLP model to detect anomalies. The results indicate that the MLP model effectively detects anomalies in both domains with higher accuracy, precision, recovery, and F1 score on the service-level anomaly dataset. The potential for more effective distributed system monitoring and management automation is highlighted in this study by focusing on service-level metrics such as service response times. This study provides valuable information about the effectiveness of supervised machine learning models in detecting anomalies across distributed software systems.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13137891