Closed-Loop Assurance

The foundational input layer that collects high-frequency, real-time network state data. Unlike traditional SNMP polling, modern assurance systems use gRPC Network Management Interface (gNMI) and NETCONF to subscribe to streaming telemetry, pushing granular metrics—such as interface counters, queue depths, and CPU utilization—at sub-second intervals. This high-resolution data stream enables the system to detect micro-bursts and transient anomalies that batch processing would miss.

The analytical core that continuously compares observed operational state against the declared Service-Level Objective (SLO). Drift detection algorithms ingest streaming telemetry and apply statistical process control, threshold-based rules, or machine learning models to identify when the network has diverged from its intended state. Key detection methods include:

• Absolute threshold violation: Latency exceeds 10ms SLO
• Predictive anomaly scoring: ML model forecasts imminent SLO breach
• Baseline deviation: Behavior diverges from learned seasonal patterns

Pre-defined, executable sequences of corrective actions triggered upon drift detection. Workflows are designed to restore the desired network state without opening a ticket or paging an operator. Common remediation actions include:

• Traffic rerouting: Shifting flows away from congested or degraded paths
• Resource scaling: Dynamically allocating additional bandwidth or compute to a network slice
• Configuration rollback: Reverting to a last-known-good device configuration
• Policy re-injection: Re-applying QoS or security policies that have been overwritten

Workflows are typically orchestrated through Ansible, custom operators in Kubernetes, or SDN controller APIs.

The mechanism that closes the loop by verifying that the executed remediation workflow actually resolved the drift. After a corrective action is applied, the system enters a post-remediation validation phase, monitoring telemetry for a configurable soak period to confirm SLO compliance has been restored. If the drift persists, the system escalates through a hierarchy of progressively more aggressive workflows. This continuous feedback cycle ensures the network is self-stabilizing and prevents oscillating misconfigurations.

The architectural binding that links abstract business intent directly to concrete telemetry streams. The assurance engine maintains a directed acyclic graph (DAG) mapping each declared SLO to the specific device sensors, interface counters, and flow metrics that measure its compliance. This correlation model enables:

• Root cause isolation: Pinpointing which device or path caused an SLO violation
• Impact analysis: Determining which business services are affected by a specific hardware degradation
• Audit trails: Generating compliance reports that prove SLO adherence over time

A formal model governing the valid states and transitions of an assurance loop. Each network intent progresses through a defined lifecycle: Drafted → Validated → Fulfilled → Monitored → Drifted → Remediating → Compliant. The state machine enforces strict transition rules—for example, preventing a drifted intent from being marked compliant without passing through the remediation and validation phases. This formalism prevents race conditions and ensures deterministic behavior in multi-tenant environments where hundreds of intents are simultaneously active.