Incident Intelligence in Telecom: A Framework for Real-Time Production Defect Triage and P0 Resolution

Abstract

The rising intricacy of the telecom platforms drives the need of the intelligent, automated incident response systems. The current paper describes a real-time incident intelligence platform that was implemented into Charter Communications Mobile 2 ecosystem. Using Kafka-based ingestion of logs, the machine learning logic of chooser responder, and RCA pipelines that are automatic with Splunk and Datadog, this framework will lower the mean time to detect, assign, and resolve P0 incidents considerably. The deployment in the real world illustrates the better levels of keeping to the SLA, automation of the triage and resilience of the systems. The architecture will combine well-organized playbooks and feedback loops to permit continuous learning. The findings indicate that these structures can be the framework to provide a model of scalable, intelligent triage of production defects in a telco-grade application.