Event Correlation Beginner

When a network issue occurs, it generates a cascade of alerts across multiple devices and monitoring systems. Event correlation uses AI to connect these disparate signals into a single, coherent incident with an identified root cause.

Correlation Dimensions

Dependency Mapping

Accurate event correlation requires a dependency map that shows how network components relate to each other and to business services. Build this using:

• Topology data - Physical and logical connectivity from LLDP, routing protocols, SDN controllers
• Flow analysis - Communication patterns reveal application dependencies
• Service models - CMDB or manually defined service-to-infrastructure mappings
• ML-discovered dependencies - Statistical correlation of metrics and events across devices

Root Cause Analysis

AI-driven RCA goes beyond simple "first event is the root cause" logic. It uses:

• Bayesian networks - Probabilistic reasoning about cause-effect chains
• Graph analysis - Walk the dependency graph to find the originating failure
• Historical pattern matching - Compare current event pattern against known incident patterns
• Change correlation - Cross-reference events with recent configuration or infrastructure changes