Network AI Introduction
Augtera’s Network AI transforms network operations, incident response, and incident management by delivering on three fundamental value propositions: faster resolution of incidents, better insights on incidents, and fewer overall incidents.
Whether Network AI is ingesting syslog only, or ingesting all network operations data, it sees anomalies other tools do not.
“50% of anomalies detected by Augtera were not detected by existing tools.”Arnaud Plouhinec, Head of Automation and Data/IA Program, Orange International
Network AI does not just see anomalies that have already occurred, it sees gray failures such as increasing degradation that will become failures, and also rare/never before see log messages that are often precede a future failure.
Alert fatigue is causing missed anomalies. Operationally irrelevant incidents have skilled staff chasing the wrong problems. Noisy signals make meaningful automation impossible.
While many talk of eliminating noise, one good proof point is a customer allowing the automated creation of trouble tickets. Trouble tickets allocate skilled resources. As a result, there is zero tolerance for noise being injected into ticketing systems.
In the case study “Fortune 500 Enterprise Transforms Data Center Network Operations” we wrote of a Fortune 500 Enterprise that had developed such trust in Augtera’s solution, that in now has Network AI auto-create trouble tickets. Some network operations teams want a slightly better mouse trap. Some operations teams want to transform. The latter automate from ingestion to ticket creation.
Network AI eliminates noisy threshold approaches, suppresses maintenance alarms at an interface granularity, and prevents duplicate incident records. Network AI also gives customers the ability to create policy that defines what they consider operationally relevant, as opposed to what a tool vendor does.
Read more on noise elimination.
Network operations teams can spend considerable time just getting siloed roles, looking at siloed data, to agree on where to start incident triage.
Augtera’s Network AI models the network and correlates data in an industry-leading way. Multi-layer, topology-aware, and all network constructs. When a low-level anomaly is creating higher-layer alarms, Network AI can focus attention on where operationally relevant action is required. Incident records point skilled staff in the right direction.
There will always be incidents that require investigation by skilled resources. However, there are many commonly occurring anomalies for which operations teams routinely, and mundanely, respond to in the same way. These are ripe for automation.
Augtera Networks is actively engaged with customers on incident response: auto-mitigation and where possible, auto-remediation. Use cases auto-mitigated include:
- Optical anomaly
- BGP gray failure
- Border router congestion
When failures occur, they must be dealt with quickly, so customer and application-team experiences are not impacted. Even better though, is if the incidents never occurred.
With increasing data volumes network complexity, resolving incidents quicker is merely running faster on the hamster wheel. What network operations teams ultimately need, is a new hamster wheel.
“We quickly figured out we cannot be in the incident response business, or incident management business. We have to be in the business of incident prevention.”Pranatap Lahiri, VP of Network and Data Center Engineering, EBAY, AI in Networking, ONUG 2021
Augtera Network AI enables network operations teams to act on gray failures, to see rare syslog messages that precede failures, equipment checks, and more.
Prevention is the leading edge of Network AIOps, and essential so network operations teams cannot only respond faster but respond to fewer incidents.
LEARN Collectively With Augtera Network AI
Network AI generates high-fidelity signals in numerous ways including classifiers for known anomaly signatures. Augtera maintains a library of classifiers collected from across the customer base and provided to every Augtera customer.
Network operations teams have limited resources. The rate of anomaly signature learning can be slow, and the implementation of rules to catch anomalies even slower if the operations team must request code changes.
Augtera’s classifier library allows all customers to accelerate the creation of high-fidelity signals by learning from others.
As operation teams learn new anomaly signatures from incident investigation, they can quickly create and activate new classifiers in they own implementation, and the same classifier can then be made available to other network operations teams.
After new classifiers are created and distributed to all customers, Network AI sees even more than it did before, resulting in an increasing number of high-fidelity signals. A virtuous circle that benefits all customers.
Network AI Conclusion
Today’s networks have too many tools, too much noise, too few high-fidelity insights, and too many incidents. Whether making incremental change, or pursuing transformation, network operations teams are transforming by responding FASTER, with BETTER insights, on FEWER incidents. Network Operations teams can no longer simply run faster on the same hamster wheel, they need a new one.