Scientific journal paper Q2
Enhancing infrastructure observability: Machine learning for proactive monitoring and anomaly detection
Darlan Noetzold (Noetzold, D.); Anubis Graciela de Moraes Rossetto (Rossetto, A. G. D. M.); Valderi Leithardt (Leithardt, V. R. Q.); Humberto Jorge De Moura Costa (Costa, H. J. de M.);
Journal Title
Journal of Internet Services and Applications
Year (definitive publication)
2024
Language
English
Country
Brazil
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Abstract
This study addresses the critical challenge of proactive anomaly detection and efficient resource man-agement in infrastructure observability. Introducing an innovative approach to infrastructure monitoring, this workintegrates machine learning models into observability platforms to enhance real-time monitoring precision. Employ-ing a microservices architecture, the proposed system facilitates swift and proactive anomaly detection, addressingthe limitations of traditional monitoring methods that often fail to predict potential issues before they escalate. Thecore of this system lies in its predictive models that utilize Random Forest, Gradient Boosting, and Support VectorMachine algorithms to forecast crucial metric behaviors, such as CPU usage and memory allocation. The empiri-cal results underscore the system’s efficacy, with the GradientBoostingRegressor model achieving an R² score of0.86 for predicting request rates, and the RandomForestRegressor model significantly reducing the Mean SquaredError by 2.06% for memory usage predictions compared to traditional monitoring methods. These findings not onlydemonstrate the potential of machine learning in enhancing observability but also pave the way for more resilientand adaptive infrastructure management.
Acknowledgements
A realização desta investigação foi parcialmente financiada por fundos nacionais através da FCT - Fundação para a Ciência e Tecnologia, I.P. no âmbito dos projetos UIDB/04466/2020 e UIDP/04466/2020.
Keywords
Machine learning,Infrastructure monitoring,Anomaly detection,Proactive maintenance
  • Computer and Information Sciences - Natural Sciences
  • Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology
Funding Records
Funding Reference Funding Entity
UIDB/04466/2020 Fundação para a Ciência e a Tecnologia
UIDP/04466/2020 Fundação para a Ciência e a Tecnologia
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