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Eliminate Alert Fatigue: 3 Must-Haves to Cut Through the Noise 

As digital transformation takes hold across industries, networks become more complex. Modern technology has led to a proliferation of network operations tools to help manage that complexity, but the wide array of tools—combined with the ever-increasing demand for high network performance and near-instant availability of data and applications—has only served to complicate things even further.  

A major challenge for network operations teams today is keeping networks running optimally while also dealing with the “alert storms” generated by all the management tools designed to simplify NetOps. For instance, if a network switch fails, that failure could result in a ripple effect of network alerts that floods NetOps teams with often dozens or hundreds of notifications. Even powering down a device for scheduled maintenance could cause a flurry of alerts.  

Alert Fatigue Can Compromise Service 

These storms lead to alert fatigue for network operators. They are bombarded with a never-ending stream of alerts every day and only have the time and manpower to address a mere fraction of them. Knowing which alerts are operationally relevant isn’t always clear. And when a NetOps team is flooded with alerts, it’s too easy to start ignoring them. Even worse, it’s too easy to possibly miss a very relevant alert about an issue that doesn’t become obvious until it’s impacted the network badly enough that a customer or application owner has to request assistance.  

What network operations teams need most is a solution that can help them cut through the noise of alert storms and identify quickly and efficiently which alerts need their skilled attention. Most of today’s NetOps management and alerting systems simply don’t have the features that can help teams do that.  

When looking for a solution to cut through the noise and eliminate alert fatigue, here are three must-have capabilities. 

1) Machine Learning-Based Anomaly Detection That Goes Beyond Thresholds 

Many NetOps tools rely heavily on thresholds set by admins to detect when issues arise. But in modern networks, there are many metrics that don’t align with easily-defined, networkwide thresholds—optimal latency, for instance, which can vary across links. Thresholds for these metrics are notoriously noisy. They’re either set too low and cause too many false positive alerts, or too high and lead to false negatives because relevant anomalies aren’t detected.  

Machine learning models work much more effectively, especially those with production-tested algorithms specialized for network operations. These self-learning algorithms can detect anomalies in hundreds of metrics on millions of objects. They do this by learning “normal” patterns for all of the metrics on interfaces, devices, queues, CPU and all constructs across the network. The algorithms go even deeper, too, using Natural Language Processing to learn to identify anomalies in text data from logs.  

With anomaly detection based on machine learning, there’s no need to rely solely on thresholds. Now teams can be more proactive than reactive by improving visibility into the network and any changes that are operationally relevant.   

2) Auto-Correlation to Eliminate Redundant Alerts 

An effective NetOps management and alerting system should be able to reduce noise by eliminating the many redundant and duplicated alerts that can occur from a single event. This type of system uses auto-correlation capabilities to automatically recognize the relationship between multiple alerts related to that one issue.  

A system that offers real-time, multilayer topology-aware auto-correlation is ideal. It analyzes and “understands” the network topology well enough to create a detailed model that takes into account network object hierarchy and relationships. That way, when a single switch fails, for example, the system can create just one alert or ticket with all the alerts that are related to the parent or incident root.  

With this kind of advanced auto-correlation, NetOps teams aren’t buried under dozens or hundreds of alerts or trouble tickets for a single network incident. And with the quick identification of the incident root, teams know exactly where to focus their attention and can typically jump into mitigation and repair efforts much sooner than they could with traditional alerting systems.  

3) Maintenance Suppression to Calm the Storm 

Scheduled maintenance can cause alerts and trouble tickets that clog up NetOps teams’ workloads and could cause them to overlook the real issues that need their attention. A more effective NetOps system can automatically suppress these maintenance events, on both the devices and interfaces in maintenance as well as on devices and interfaces related to them, to keep them out of the alerting system altogether.  

Today’s advanced NetOps solutions allow NetOps teams to input lifecycle states into the system so it knows when an event is maintenance-based. It should give teams the flexibility to suppress maintenance notifications granularly—for instance, suppress notifications on some interfaces while allowing them on others.  

See how one global Fortune 500 e-commerce giant transformed network operations and enhanced its customers’ experiences with Augtera Network AI. View the case study >> 

Augtera Helps Eliminate Alert Fatigue with AI and Machine Learning 

Augtera Network AI platform supports the above three must-haves at scale to help network operations teams cut through the noise, eliminate alert fatigue, and be confident that the alerts they receive are operationally relevant. The company’s revolutionary AIOps platform, Network AI, includes purpose-built machine learning algorithms that are self-learning and maintenance-free and automate operationally relevant alerts and tickets. Using Network AI, many companies have been able to achieve:  

  • 90% reduction in trouble ticket queues by grouping related alerts under a single incident 
  • 90% reduction in the time it takes to schedule a resource to investigate an incident (mean time to detect, or MTTD) 
  • 50% reduction in time to mitigation (MTTM) 
  • 40% reduction in mean time to repair (MTTR) 

With such improved detection, mitigation, and repair times, organizations are able to offer significantly improved network performance and reliability to application teams and their customers and increase revenue. Customers deal with fewer issues and their overall network experience is better than ever—which goes a long way toward building stronger relationships and loyalty.  

To see for yourself how you can improve network operations with Augtera Network AI platform, please visit our real-time demo lab.