The evolution of infrastructure and services places greater demand on monitoring and operations to adapt to a more complex ecosystem.
Approaches used in the past fall short of fulfilling the needs of modern networks with increased bandwidth and more use of encryption. These factors complicate the ability to leverage Deep Packet Inspection and other techniques.
New techniques are required to find efficiencies in understanding traffic patterns and to also gain visibility into cloud infrastructure. Customers expect high Quality of Service (QoS) and Quality of Experience (QoE), and continuous network monitoring and real-time analytics through solutions like DART by Cirries offer a means of ensuring consistent network excellence for your end users.
Machine Learning (ML) techniques can establish a baseline traffic pattern and continuously compare that to real-time traffic to discover anomalies that would otherwise be missed. Legacy methodologies without ML can overlook these patterns,
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.