DATA CENTRE MONITORING MODEL UTILIZING ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND ANOMALY DETECTION ALGORITHMS

In this article the review is created of architectures of popular data centre monitoring tools and corresponding information processing techniques are summarised. Pros and cons analysis of the monitoring tools is done and novel approach is offered by utilizing Artificial Intelligence (AI), Machine Learning (ML) and Anomaly Detection (AD) algorithms to achieve research goals and prove hypothesis that data centre level monitoring model could be built using combined AI, ML and AD techniques. Oracle performance metric data are collected to perform the information analysis from such angles the most modern enterprise monitoring tools do not provide yet.

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Publication Details

Journal
Environment Technology Resources Proceedings of the International Scientific and Practical Conference
Published
2025-06-08
DOI
https://doi.org/10.17770/etr2025vol2.8565
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.00

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article

DATA CENTRE MONITORING MODEL UTILIZING ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND ANOMALY DETECTION ALGORITHMS

Roberts Volkovičs
Environment Technology Resources Proceedings of the International Scientific and Practical Conference
Network Security and Intrusion Detection
article

DATA CENTRE MONITORING MODEL UTILIZING ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND ANOMALY DETECTION ALGORITHMS

Roberts Volkovičs
article en

Abstract

In this article the review is created of architectures of popular data centre monitoring tools and corresponding information processing techniques are summarised. Pros and cons analysis of the monitoring tools is done and novel approach is offered by utilizing Artificial Intelligence (AI), Machine Learning (ML) and Anomaly Detection (AD) algorithms to achieve research goals and prove hypothesis that data centre level monitoring model could be built using combined AI, ML and AD techniques. Oracle performance metric data are collected to perform the information analysis from such angles the most modern enterprise monitoring tools do not provide yet.

Environment Technology Resources Proceedings of the International Scientific and Practical ConferenceVol. 2
Vidzeme University of Applied Sciences (LV)
European Commission, Research Executive Agency
Openalex Percentile: Top 86%
Network Security and Intrusion Detection
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DATA CENTRE MONITORING MODEL UTILIZING ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND ANOMALY DETECTION ALGORITHMS — Roberts Volkovičs · Environment Technology Resources Proceedings of the International Scientific and Practical Conference (2025) | TGRS Research Map | TGRS