Autonomous Network Troubleshooting with AIOps and Machine Learning: A Data-Driven Architecture for Correlation, Prediction, and Remediation
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Abstract
Modern networks carry business-critical traffic across data centers, cloud platforms, branch locations, APIs, telecom circuits, security gateways, and software-defined overlays. In this environment, traditional troubleshooting is no longer limited by the availability of alarms; it is limited by the volume, fragmentation, and operational interpretation of telemetry. This updated paper presents an expanded AIOps and machine learning framework for automating network troubleshooting through telemetry ingestion, data enrichment, anomaly detection, event correlation, root-cause ranking, predictive risk scoring, and governed closed-loop remediation. The paper extends the original architecture by adding data governance, model lifecycle management, explainability, human approval gates, operational KPIs, and security controls. It also introduces a telecommunications implementation scenario in which Remedy tickets, Kafka streams, Kong APIs, topology data, and AIOps model outputs are combined to reduce alert noise, accelerate diagnosis, and improve network resilience. The proposed approach is not intended to replace network engineers; rather, it converts repetitive investigation patterns into repeatable, auditable, and continuously improving operational workflows.
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References
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