In the intricate world of network operations, the ability to swiftly identify and address issues is paramount. Traditionally, auto-correlation has been a staple tool, aiding in the detection of correlated alarms on devices or interfaces. However, the landscape is evolving, and Augtera Network AI platform is leading the charge with groundbreaking advancements.
At its core, auto-correlation in today’s tools focuses on identifying deduplicated alarms that occur simultaneously on a given device, often resulting in a commendable 50% reduction in alarm volume. This methodology has been the cornerstone of network operations for years, offering valuable insights and efficiencies.
Enter Augtera Network AI platform, a trailblazer in the realm of auto-correlation. What sets Augtera apart is its pioneering approach: the integration of auto-discovered network topology knowledge into the auto-correlation process that leverages ML algorithms and is not rule based. This paradigm shift enables a holistic understanding of network events within the context of a multi-layer topology model.
Network Model
The multi-layer topology model is a sophisticated framework comprising a multi-dimensional graph that delineates objects and their interrelatedness within the network. We refer to this as the “Network Model”. These objects span a spectrum of network constructs, from devices and interfaces to queues and components. These objects and the relationships between them are auto-discovered using a number of techniques. Each object is characterized by a myriad of properties, metrics, and metadata, encompassing everything from traffic counters and error packets to temperature readings and CPU performance. Moreover, metadata injects operational semantics into the model, enriching the contextual understanding of network events.
Auto-Correlation Using Machine Learning
In addition to the Network Model the second innovation pillar that makes Augtera auto-correlation groundbreaking is the use of ML algorithms to automatically correlate events and anomalies instead of relying on rules. These algorithms leverage the Network Model. This allows the auto-correlation engine to keep up with evolving network topologies and correlate unknown patterns. Crucially, the topology is multi-layered, accommodating diverse relationship levels such as L2 connectivity links, BGP peerings, overlay tunnels, and metadata. Augtera auto-correlation ML algorithms seamlessly navigate these layers, automatically correlating events and anomalies based on network relationships and the nature of events or anomalies.
Production Outcomes
Augtera’s auto-correlation engine extends beyond raw alarms, encompassing events generated by Augtera AI/ML anomaly detection engines. Rare syslogs, collective learning insights, flapping detections, and ML-detected metric anomalies are all integrated into the correlation process, enhancing the platform’s predictive capabilities.
Real-world implementation underscores the transformative impact of network topology and ML based auto-correlation. Results from production networks reveal an additional staggering 70% reduction in noise—an unprecedented leap in efficiency. In addition to reducing alarms and tickets Augtera auto-correlation results have also allowed to reduce root-cause-analysis and remediation times in large scale production environments by 60%. This is because of the topology based context that is provided along with the output of Augtera auto-correlation.
Ultimately, modern network operations are poised to slash alarm noise by over 90%. while delivering automated, insightful tickets for swifter issue resolution—a game-changing leap towards operational efficiency.
In conclusion, Augtera Network AI platform heralds a paradigm shift in auto-correlation, leveraging network topology knowledge to revolutionize network operations. With its pioneering approach and transformative impact, Augtera paves the way for enhanced efficiency, reliability, and agility in the ever-evolving landscape of network operations.
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