Augtera demo lab comprises a multi-cloud network with two data centers, a backbone and multiple AWS, Azure and GCP VPCs. It ingests real-time syslog, SNMP Trap, telemetry streaming and SNMP metrics, SNMP topology, sFlow, Augtera synthetic probe and AWS VPC flow log data into the Augtera platform.
View live real-time demos of some of our widely deployed use cases below.
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Underlay Congestion & Impacted Flows
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.
Prevent Grey Failures from Becoming Outages
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.
Prevent Hardware Failures
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.
Abnormal Traffic Patterns
Seasonality aware ML based detection of abnormal traffic. Prevents prolonged issues due to unexpected routing changes or DDoS attacks.
Proactive / Preventive Noiseless Tickets
Automatic creation of tickets that are relevant and actionable dramatically reduces time to detect and remediate problems as seen in this ServiceNow integration.
Topology based Auto-Correlation
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.
Synthetics – Underlay and Overlay Continuity
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 Anomalies on Actual Flow Path
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.
Actionable Events & Anomalies on Topology
Syslog and SNMP Trap events (learned via collective learning across customers) and Augtera ML anomalies overlaid via a time-machine on auto-discovered topology.
Network and Infra Health
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.
Rare Logs for Unknown Unknowns
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.
Collective Learning for Known Unknowns
Prevent failures by detecting actionable events already learned via collective learning across production deployments of Augtera.
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