Operations teams will sleep better, knowing they will be aware of new log messages and patterns, as soon as they occur, instead of finding out weeks later, after numerous failures have occurred. 

As a marketer, I’m thrilled for the opportunity to tell great stories about exceptional products & services that are making a significant difference.

Network Operators: Time to get AI/ML out of the labs and into production

A topology-aware network model is a critical building block for Network AIOps

Data center architectures and operational realities have changed dramatically. However, NetOps tools have not kept pace.

“The data center is dead.” It’s certainly a popular opinion. Public cloud revenue is growing at roughly 25% YoY with an estimated $330 billion total revenue for 2021. And Gartner predicted in 2019 that 80% of enterprises would close their traditional data centers by 2025. With stats like those, no one would fault you for thinking that servers are going to be a thing of the past for most enterprises — just like typing pools and interoffice memos.

The use of artificial intelligence and machine learning technologies only makes sense for a company if they profoundly transform the operational experience by improving the company’s KPI performance.  It is therefore important to analyze how a network anomaly found automatically by machine learning impacts operational workflows. 

In today’s telecom market there’s a strong appetite for rich analytics. It’s almost impossible for a SD-WAN vendor or an MSP to sell a solution or service without these features. The question is, how much is it used? Is it really useful?

This week I joined Augtera Networks as Sr. Director of Product and Product Marketing.  I’m not one to jump ship lightly – my tenures at Kentik and Arbor Networks were 7 and 11 years respectively.  So transitions like these create an opportunity for me to stop for a moment and reflect on the industry’s past and future, and my own as well. I’ve spent my entire career in the world of infrastructure.  Much of it relating to networks, but also systems and software.  For as long as I’ve been immersed in it, the state of infrastructure has always been one step ahead of our ability to operate it as effectively as we’d like.  I’m old enough to remember when it seemed impossible just to generate the data we needed to properly troubleshoot networks.  The days when our best tooling was a packet sniffer on a cart in the corner of the data center.

An anomaly is defined by Oxford Dictionary as “a thing, situation, etc., that is different from what is normal or expected”.  When you apply this definition to Networks we need to first determine the networking constructs on which determining what is not normal or expected is of business and operational significance. Then we need to determine the normal behavior of these networking constructs and what is not normal on an ongoing basis.