Poor Application & Customer Visibility
It is no secret, that in today’s Hybrid IT environment, among the most significant challenges for Network Operations teams is mitigating application impacting network issues before Application teams and Customers start reporting incidents. Even just knowing where to start triage when an incident is raised, is a significant challenge.
Augtera’s Network AI understands all layers of the network, from the physical layer to the TCP layer. In addition, scalable, high-frequency capable synthetic probes to both agents and URL end points, ensures Network Operations teams understand application latency and packet loss at a low time granularity / periodicity. Other synthetic solutions do not scale when high-frequency probing is configured, and they also become expensive, charging per-probe. Augtera charges per device, regardless of the probe interval.
AI/ML anomaly detection is performed on all metric data sources, including synthetic probes, and multi-layer, topology-based auto-correlation is performed across all data sources / network layers.
The scalability of Augtera’s synthetic solution, the effectiveness of noise reduction and incident root identification across all layers, and the simple per-device license model offers Network Operations teams a capability they have not previously enjoyed.
Mean-time-to-innocence (MTTI) is lowered and when there is an application impacting network issue, action can be quickly taken. In addition, Network AI’s ability to detect gray failures means preventative action can be taken before incidents even occur.
Alert & Ticket Fatigue
Humans cannot process or monitor millions to billions of data points per day. Multiple tools are generating alerts. There are many false and redundant alerts / tickets. Network Operations teams are overwhelmed, not really knowing what is relevant or not. Too often, the first meaningful action is taken is after a customer or application team raises a trouble ticket. Augtera’s Network AI eliminates alert / ticket fatigue by:
- More accurate AI/ML-based anomaly detection with less false positives
- Multi-layer topology-based auto-correlation that generates a single incident record with all relevant context as child data
- Multi-layer topology-based auto-correlation that more accurately identifies incident root
- Operations team definable policy that defines which resources notifications are raised for
- Suppression of maintenance-related alerts / tickets at an interface granularity
Read how one Fortune 500 eCommerce company reduced trouble tickets by 90%, and mitigation times by 50%+.
Reactive Anomaly Detection with Gaps & Inaccuracies
Threshold-based monitoring systems:
- Generate false positives and false negatives
- Do not detect gray failures, which can be used for preventative action
- Do not detect rare / new log messages that often precede new types of outages, enabling preventative action
Augtera’s Network AI generates anomaly alerts / trouble tickets based on patterns that are difficult for humans to detect via monitoring / visualization. These alerts have less false positives / false negatives. Patterns are constantly being assessed, including the identification of gray failures – anomalies that in the future will become more significant incidents. In addition, Augtera’s Real-Time Natural Language Processing is unique in its ability to not just text match, but understand semantics, and therefore truly detect rare / new log messages that precede new types of incidents that there are no existing rules / known signatures for.
Network AI’s anomaly detection is more accurate, less noisy, and enables preventative action. This enables Network Operations teams to transform from a reactive mode to a preventative mode.
Manual or Poor Correlation
On average, Network Operations teams have 4-10 tools. When incidents occur, manual correlation is required across these tools. This is time-consumer and error-prone unless performed by scarce highly skilled operations / engineers. Some network operations teams have attempted to use early correlation tools or build their own, with mixed results. Often these approaches are not accurate and ultimately do not meet the needs of operations teams.
Augtera’s Network AI is production proven, at scale, to dramatically reduce the number of alerts / trouble-tickets and accurately identify incident root, with multi-layer, topology-based auto-correlation.
Augtera’s engineers have refined correlation techniques across many different verticals including Fortune 500 eCommerce, Finance, Transportation, and Retail as well as Communication Service Providers and Managed Service Providers.
Do not reinvent the wheel or continue to stagger from one tool to another doing manual correlation. Take the leap to accurate, automated, scalable auto-correlation today.
Scalability
On-prem only, monolithic tools are no longer capable of addressing the elastic scalability requirements of today’s dynamic Network Operations. Nor are SaaS-based solutions with data usage-based licensing models.
Augtera’s Network AI is a cloud-first, cloud agnostic architecture that supports elastic, micro-services based, horizontal scalability, agility, and continuous integration / deployment whether in a public cloud or a private cloud. The device-based licensing model means that costs are predictable, regardless of data volume.
Cost-effective scalability, with a cloud-agnostic design focus, is a leap forward from many existing tools. This aligns Network Operations teams with broader IT trends / preferences in addition to enabling a completely different experience for Operations teams.
Augtera’s Network AI is production proven in the largest networks, with a million+ interfaces, generating hundreds of millions of data points per hour. When other monitoring and synthetic testing tools fall over, Augtera’s Network AI is just getting warmed up.
All Topologies, Including VRF / Customer Topologies
Understanding and visualizing topologies is critical to Network Operations teams, including mapping anomalies. However, there is more than one topology in a network. There are physical, optical, Ethernet, IP underlay, IP overlay, and SD-WAN topologies. Network Operations teams segmenting the network with VRFs / VPNs also need to understand those topologies. Most network tools have limited understanding of all the topologies in a network, which limits the effectiveness of auto-correlation and incident root identification.
Some tools with limited topology capabilities, require the Network Operations team to manually create topology, which is both time-consuming and quickly outdated in today’s dynamic network environments. Augtera’s Network AI auto-discovers all relevant topologies. Alternatively, Network Operations / Engineering teams can provide topology information through an API.
Conclusion
Augtera’s Network AI supports existing monitoring / observability paradigms for customer-controlled migration to a post-monitoring, automated Network Operations model. To enable that shift, support is provided for:
- Automated ticket reduction
- Consolidated View of Network / Data
- Self-maintaining anomaly detection
- Auto-discovery of multi-layer network model
- Auto-correlation & incident root identification
- ML-based Synthetic probes
- Shift to SaaS
- Tool consolidation
The above Network Operations pain points are just a few of the pain points addressed by Augtera’s Network AI.
To learn more about Next Generation Network AIOps and Augtera’s Network AI, request a free trial with the button above or contact us and request a consultation with an Augtera Engineer.
