Analysis of modern approaches to monitoring and load management in IT infrastructures

Abstract

The article analyzes modern approaches to monitoring and load management in IT infrastructures. Traditional resource monitoring methods, Observability, load balancing, autoscaling, containerization, and machine learning methods for workload forecasting and anomaly detection are considered. The features of reactive, proactive, and intelligent resource management are analyzed. A conceptual model of intelligent load management is proposed, integrating telemetry data collection, preprocessing, workload forecasting, anomaly detection, and automated resource scaling. A comparative analysis of the main approaches is performed according to the level of automation, adaptability, forecasting capabilities, and data requirements. The feasibility of transitioning from reactive to proactive IT infrastructure management is substantiated, and the prospects of using machine learning and AIOps technologies are identified.

It has been established that the application of machine learning methods enables a transition from reactive to proactive management by forecasting future load and detecting anomalous behavior.

A conceptual sequence for intelligent management is proposed: telemetry collection → preprocessing → analysis → forecasting → anomaly detection → decision-making → scaling → outcome monitoring.

A promising avenue for future research involves the development and experimental validation of an intelligent load management system utilizing time-series forecasting models, anomaly detection algorithms, and automated scaling mechanisms. Particular attention should be paid to evaluating forecast accuracy, reducing false positives, ensuring the explainability of decisions, and optimizing the balance between performance and resource costs.