The Need for Modern NetOps Tools
NetOps tools for data centers face new challenges. The shift to fixed-form factor switches, dense topologies, underlays and overlays, hybrid cloud, and SD-WAN, has created new complexity, inside, outside, and between data centers. Legacy point products cannot support the number of interfaces, number of layers, amount of noise, and critical need to understand the impact of the network on applications. Or the need to quickly determine network innocence if that may be the case.
Read Augtera Networks Data Center Solution
Alert fatigue is bad and getting worse. Increasing number of tools, increasing number of data sources, increasing number of monitored objects, and more. Detecting operationally relevant and high priority anomalies by looking at dashboards and monitors is becoming harder and harder. Whether automating trouble ticket creation or creating manually, noise needs to be drastically reduced, and optimally, eliminated.
Read more about noise elimination.
AI/ML is realizing game changing improvements to NetOps workflows and automation. Reducing the mundane work of constantly adjusting thresholds, detecting anomalies, detecting gray failures, manually correlating data from different tools, identifying incident roots, and dramatically improving KPIs. NetOps teams adopting AIOps.
One good example of Network AIOps is replacing thresholds with machine learning models. Thresholds are noisy, creating false positives or false negatives. Network-specific machine learning models learn patterns, adapt to changing patterns, detect anomalies, and detect gray failures.
Read more about Machine Learning model advantages over Thresholds.
Another example of Network AIOps is the ability to connect the dots across multiple data sources, events, and alerts through correlation. In the case of Augtera’s Network AI platform, multilayer, topology-aware auto-correlation.
Read more about AI in Networking
Network Operations Outcomes
Network operations teams adopting Network AIOps based NetOps tools are realizing dramatic improvements in KPIs:
- 90%+ reduction in mean time to detect (MTTD)
- 50%+ reduction in mean time to mitigation (MTTM)
- 40%+ reduction in mean time to repair (MTTR)
- 4x increased in mean time between incidents (MTBI)
These improvements roll up to one overarching NetOps transformation, the ability to automate anomaly detection to trouble ticket creation, and mitigation and repair for some anomaly types. Mundane, time-consuming work is automated, enabling NetOps teams to focus their skills on remediation, incident prevention, and overall network reliability.
The simplification of network equipment has resulted in new levels of network operations complexity. This new complexity requires a new generation of NetOps tools. The new generation leverages AI/ML technology to eliminate noise, group related events into a common incident, dramatically improve KPIs, and automate the identification of incident root and operationally relevant incidents for further action.