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Proactively detect underlay congestion based on ML on the ratio of dropped packets to the traffic. Immediately look at impacted flows to determine and mitigate application impact.
Detection of rare logs via NLP plus actionable events already learned across production deployments of Augtera. These point to likely root cause of simmering failures and fixing the root cause can prevent future outages.
High fidelity early detection of temperature issues, optical degradation or asic errors prevents hardware failures which lead to outages. This use case is best experienced in production as seen in this demo video.
Seasonality aware ML based detection of abnormal traffic. Prevents prolonged issues due to unexpected routing changes or DDoS attacks.
Automatic creation of tickets that are relevant and actionable dramatically reduces time to detect and remediate problems as seen in this ServiceNow integration.
Automatic correlation of operationally relevant events and ML anomalies dramatically reduces the time that operators spend on manual correlation. It also provides contextual playback on topology for quick root cause analysis.
Augtera synthetic probes continuously ensure that underlay and overlay are performing as expected. Heatmaps provide visual hot spots while ML anomalies enable prevention of app performance degradation due to persistent loss or latency.
Map the actual path taken by a specific or several application flows on the DC topology. Overlay events and anomalies based on a time machine to isolate the root cause of app performance degradation.
Syslog and SNMP Trap events (learned via collective learning across customers) and Augtera ML anomalies overlaid via a time-machine on auto-discovered topology.
Real-time view of key events, metric based ML anomalies, log based ML anomalies, top talkers by errors, traffic and application flows across the DC infrastructure and network.
Real-time detection of the first occurrence of rare logs via NLP These point to likely root cause of simmering failures and fixing the root cause can prevent future outages.
Prevent failures by detecting actionable events already learned via collective learning across production deployments of Augtera.
