Self-Healing SD-WAN Flow Testing for Anomaly Remediation

The system enables self-service through automated anomaly detection and remediation. The analytics system continuously monitors network metrics, detects anomalies using machine learning models, and automatically executes remediation actions without human intervention. This allows the network to self-diagnose and self-heal, maintaining reliability while increasing automation extent.

The system implements feedback loops where network performance data is continuously collected, analyzed, and used to trigger remediation actions. The closed-loop architecture ensures that anomalies are detected, remediation is executed, and the impact is measured, creating a self-correcting system that maintains network reliability through automated feedback mechanisms.

The system segments the data processing workload by implementing distributed collection at FEs, centralized aggregation at the analytics system, and specialized analysis functions. Flow data is collected from multiple FEs, aggregated to a central analytics system, and then processed through specialized machine learning models, dividing the complex processing task into manageable segments that improve accuracy while controlling complexity.

The analytics system acts as an intermediary between data collection and anomaly detection. It aggregates and pre-processes flow data from multiple FEs before feeding it to machine learning models, simplifying the overall architecture by introducing a dedicated layer that handles data transformation and preparation, thereby reducing direct processing complexity.

The system performs preliminary action by pre-training machine learning models during system deployment before actual anomaly detection begins. Baseline behavior patterns are established in advance, and detection thresholds are configured beforehand, allowing the system to start operating with high reliability while reducing the complexity of ongoing configuration and maintenance.

The system manages complexity by dynamically adjusting detection parameters and model thresholds based on network conditions. Rather than requiring manual reconfiguration, the system automatically adapts its anomaly detection parameters to changing network environments, maintaining high reliability while simplifying operational maintenance through automated parameter optimization.