AI-Oriented Monitoring Agent in Network Infrastructure of Multicloud Data Centers

Globa, Andrii

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Proceedings of International Conference on Applied Innovation in IT · 2026/04/22 · Vol. 14 · Issue 2 · pp. 13–20

AI-Oriented Monitoring Agent in Network Infrastructure of Multicloud Data Centers

Andrii Raichuk and Larysa Globa

📄 Download PDF DOI: Under indexing

Abstract

The growing complexity of multicloud data center infrastructures, together with the rapid increase in telemetry streams, event logs, and protocol notifications, makes timely incident detection and fault localization a critical problem for modern telecommunication systems. Conventional monitoring approaches mainly rely on the analysis of large historical logs, which limits their ability to identify short-term correlated failures and support rapid operational decision-making. The aim of this paper is to develop a software-oriented architecture for intelligent monitoring of data center network infrastructure based on localized telemetry buffering, centralized event correlation, and AI-assisted diagnostics. The proposed method combines local short-term telemetry snapshots formed on network and server nodes, critical-event clustering within a limited time window, root cause analysis using correlation of protocol, resource, and trap data, and risk estimation through a machine learning model. As a result, a five-layer monitoring architecture and an incident-processing algorithm were developed, including local agents, temporary telemetry buffers, a central correlation node, a failure knowledge base, and an AI assistant for troubleshooting support. The results show that the proposed approach reduces the amount of data requiring urgent processing, improves the identification of causally related events, and decreases the time needed to interpret critical incidents. The practical relevance of the study lies in the possibility of deploying the system as a software layer over existing monitoring and logging infrastructure without hardware modification. The theoretical relevance lies in the formalization of localized incident-context modeling, critical-event clustering, and AI-assisted root cause identification for multicloud data center environments.

Keywords

Intelligent Network Monitoring Data Centers Event Log Analysis Log Processing Network Failures Automated Diagnostics AI Assistant.

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