The question is not just a teaser. There is a fundamental reason for introducing this blog in this manner. Because any SD-WAN implementation is a vendor specific mix of standard and proprietary building blocks, breaking the symbiotic relationship that network engineers had with network technology in the past. Previously, network engineers were building an entire network stack that included connecting links to the router, configuring interface IPs, configuring IGP, BGP peers, dampening, timers, BFD, queues, MPLS LSPs with RSVP FRR or LDP, MPLS VPN route distinguishers, route targets, and many more.
Recently two high profile AWS outages have disrupted service to many organizations using their services. These outages often have direct and in-direct downstream effects to services which may not be hosted directly in AWS but rely on 3rd party services (e.g., Slack) that use AWS. A few months back Augtera released a new service , real-time multi-cloud observability that provides actionable insights. In this blog we will explain how this service detected and notified operations teams about the recent AWS outage (30 minutes before AWS posted it) and how it provides valuable outage context for organizations that are using multiple regions and public cloud providers.
When we first presented at SD-WAN Summit in 2019 about the applicability of AI/ML on SD-WAN networks, I have to admit that the essence of our pitch was coming from a strong intuition rather than practical experience in that space. This is because most of our production deployments at that time were in WAN and Datacenter segments. However, when I look back at those old slides today when the new edition SD-WAN and SASE 2021 is starting, I am really surprised by how well they resonate with our last two years of deployment in enterprise and MSP SD-WAN infrastructures.
This blog will describe a real example of how an organization uses Augtera machine learning to proactively detect environmental issues before they adversely impact service. Machine learning and AI are not technologies that typically come to mind when you think of monitoring environmental conditions in a facility, however, they should be and this blog will highlight why. Augtera is reinventing the way organizations operate their networks. Augtera machine learning and AI enable organizations to proactively identify conditions where failure may soon follow. In this blog, we examine a real-world example of how Augtera machine learning prevented a facility outage, and describe the shortcomings with traditional monitoring systems.
NetOne Systems is participating in the ONUG fall 2021 proof of concept session tomorrow. We are showcasing a practical illustration of how machine learning can enhance NetSecOps workflows to better protect cloud infrastructure. For this proof of concept we have integrated Cloudflare, Augtera Network AI and NetOne Cloud Controller.
When we started Augtera we had a mission to leverage Machine Learning and deep analytics to transform the networking industry. Today we are launching our company and our industry-first Network AI platform – a major milestone towards that goal.