Streaming Telemetry

Unlike traditional SNMP polling, which requires a management system to periodically request data, streaming telemetry uses a publish/subscribe model. Network devices continuously push state and performance metrics to a collector as soon as they change. This eliminates the N+1 polling problem and drastically reduces the time between an event occurring and its detection.

Telemetry data is structured using standardized, model-driven schemas, primarily YANG. This provides a well-defined, machine-readable contract for the data's structure and semantics. Key benefits include:

• Schema validation: Guarantees data integrity from source to collector.
• Self-describing data: Eliminates manual parsing of unstructured text outputs.
• Vendor-neutrality: Enables a single collector to ingest data from multi-vendor environments.

Streaming telemetry captures metrics at sub-second intervals, providing a high-fidelity view of network state. This high-resolution data is critical for detecting microbursts, transient congestion events, and other short-lived anomalies that are invisible to 5-minute polling cycles. It provides the precise data foundation required for predictive AI/ML models.

To handle high-frequency data, streaming telemetry uses modern, efficient transport protocols instead of legacy UDP-based SNMP. The most common is gRPC, which leverages HTTP/2 for multiplexed, bidirectional streaming and Protocol Buffers (protobuf) for compact, high-performance binary serialization. This significantly reduces bandwidth overhead and CPU utilization on both the network device and the collector.

Streaming telemetry supports two operational modes:

• Dial-out: The network device initiates a session to a pre-configured collector. This is firewall-friendly and ideal for monitoring a few devices.
• Dial-in: The collector initiates a session to the network device and subscribes to specific data streams. This is more scalable for large, dynamic networks as the collector manages all subscriptions centrally.

Streaming telemetry is the Monitor phase of the MAPE-K (Monitor-Analyze-Plan-Execute over a shared Knowledge base) autonomic control loop. The continuous, real-time data stream feeds analytics engines that detect state deviations, triggering automated policy-driven remediation. This forms the sensory nervous system for self-healing and intent-based networks.

The application of statistical and machine learning techniques to high-volume streaming telemetry data to identify deviations from normal behavior. This is the analytical engine that transforms raw metrics into actionable alerts.

• Detects micro-bursts, silent packet loss, and precursor failure signals.
• Uses time-series decomposition to separate trends, seasonality, and residuals.
• Feeds detected anomalies into closed-loop automation systems for immediate remediation.