Lilly Tech SystemsEvent 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
| Dimension | Method | Example |
|---|---|---|
| Temporal | Group events occurring within a time window | 50 interface-down alerts within 30 seconds = one event |
| Topological | Correlate events based on network topology | Downstream device alerts caused by upstream link failure |
| Causal | ML-learned cause-effect relationships | Config change at 14:00 caused BGP instability at 14:02 |
| Service | Map events to business services | Database server alert + app latency = service degradation |
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
Change Correlation: Up to 80% of network incidents are caused by recent changes. Always correlate alerts with the change management system to identify change-related issues quickly.
Next Step
Learn how to reduce alert noise so operators can focus on real issues.
Next: Noise Reduction →