ML AND STREAMING ANALYTICS
Machine Learning (ML) techniques provide an expeditious approach to discerning baseline traffic patterns through advanced analysis. Legacy methodologies contain holes and inefficiencies that can cause patterns to be overlooked.
Using correlation between metrics across different points throughout the network, such as packet data, flow data, SNMP utilization metrics, and syslog, produces a complete view of the network. DART takes a ML-driven approach to reliably detect anomalies in real-time, thereby minimizing the time to resolution and avoiding disruptions that degrade QoE for end users.