Telemetry and Analytics

Arista's telemetry and analytics platform replaces legacy SNMP polling with real-time state streaming from EOS switches, delivering per-port, per-queue, and per-flow data to CloudVision at millisecond granularity without the polling intervals that create gaps in legacy monitoring data. EOS streaming telemetry uses a push-based model where the switch continuously exports state changes as they occur (rather than waiting for a management system to request a snapshot), enabling anomaly detection on the actual traffic-level timescale rather than on a polling-interval timescale that misses sub-second events.

CV-UNO extends this telemetry model upward into compute and application layers, correlating network-level events with host and workload data to enable root cause analysis that spans the full stack without requiring context switches between separate monitoring tools. Tracers built into EOS for virtual machines, containers, and big-data frameworks map individual workload connections to the physical network paths that carry them, enabling an operator to trace a specific application's latency increase through the network path, compute resource, and application layer simultaneously, a capability that legacy monitoring tools which separate network, compute, and application visibility cannot provide.

CloudVision's analytical pipeline applies AI-driven prescriptive analysis to the combined network-compute-application telemetry stream, surfacing recommendations with specific remediation steps rather than raw alert data that requires manual interpretation. For organizations operating at the scale where manual investigation of every alert is not feasible, this automation separates signal from noise in the telemetry stream and prioritizes the investigation queue based on impact and confidence, reducing mean time to resolution for network-affecting events by surfacing the root cause with a recommended action rather than leaving the correlation work to the operator.