AI-Based Network Telemetry and Observability: A Review of Intelligent Monitoring, Log Analysis, and Performance Prediction
Abstract
Network observability has become increasingly important as modern infrastructures span cloud platforms, data centers, edge environments, wireless networks, and distributed services. The enormous volume of telemetry, logs, traces, and performance measurements generated by these systems makes conventional monitoring approaches increasingly difficult to scale. Artificial Intelligence (AI) provides new capabilities for transforming raw operational data into actionable insights through automated pattern recognition, anomaly detection, forecasting, and root-cause analysis. This review investigates AI-driven network observability techniques, covering machine learning, deep learning, natural language processing, time-series modeling, graph neural networks, and large language models. Applications in log classification, event correlation, performance prediction, anomaly detection, root-cause analysis, capacity planning, and incident management are systematically examined. The paper analyzes the characteristics of network telemetry data and discusses challenges involving high dimensionality, heterogeneous data sources, temporal dependencies, missing observations, and noisy measurements. The emerging role of LLMs and AI agents in converting network telemetry into natural-language explanations and automated operational actions is also explored. Future research directions toward intelligent, predictive, and autonomous network observability are identified.
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