Augtera founder and CEO Rahul Aggarwal recently spoke about “AI Driven Automation of Network Operations” at MPLSSDAINETWORLD23. In this blog we will cover some of the key points from his presentation. Digital transformation has been a major focus in driving innovation and great change across entire enterprises and their IT infrastructure. One area of IT, however, that remains largely unaltered is network operations. While NetOps teams certainly have access to a number of network monitoring tools and solutions today, most of those tools still rely on highly reactive and manual processes.
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.
As more enterprises move data and applications out of the data center into hybrid or multicloud platforms, networks—and network operations—have become increasingly complicated. On-premises architecture was never exactly simple, but today IT must manage the data center as well as many other connected environments, including public and private clouds and do so in real-time. The technology that was developed to improve IT and aid digital transformation may have enabled enterprises to increase agility, improve scalability, and enhance application development, but it has also added much more complexity from an operational point of view.
A technology that can process billions of logs per hour with natural language processing (NLP) and will automatically find exceedingly rare messages that are operationally relevant and without noise. Sounds too good to be true, right? That is exactly what I thought when our engineering team was describing the new “Zero Day Log” ML capability that they were building.
Introduction Fans of science fiction books or movies have all experienced a sequence when someone who has not yet committed a crime is arrested because AI predicts they will in the coming hours or days. Can this impressive outcome be experienced today for networks?
I met Rahul Aggarwal in June of 2022 and took an immediate liking to the team. Early in a joint customer engagement, it became very clear that there was something special here – not just special but BIG. The customer knew it too. Little did I know it would lead to a dramatic personal change as well.
AIOps that is purpose-built for Networking, is a generational step forward, going well beyond monitoring and observability to achieve new outcomes: dramatic reduction in alert / trouble ticket fatigue, multi-layer incident root identification, cloud-native geo-diverse scaling & HA, automation playbooks, and more.
Every few years, the industry creates a new set of terms that end up creating endless debate on what they mean. One top of mind term is “observability.” Recently Enterprise Management Associates (EMA) surveyed 400 IT/Networking professionals, providing data that helps us dive into the subject. In this blog we look just at the responses from Network Operations professionals.
Responses from Network Practitioners on network tool complaints focused on Data Quality and Scalability. Responses from IT leaders focused on Scope and Expense. This blog looks at the survey results and discusses the issues.
Network Operations Tools must dramatically increase productivity, eliminating redundant / irrelevant tickets, reducing the complexity of identifying incident root, and efficiently scaling