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Written by Chris Bloom, LiveAction Senior Product Sales Specialist

In my 27 years as a Software Engineer for network performance monitoring (NPM) products, evolving into my current role as a Senior Product Sales Specialist, I work with customers to use the best NPM tools, along with experience and creative problem solving, to identify and solve challenging and elusive network issues.

With good tools, identifying the problems is usually the easy part.
The most difficult part of my job is bridging the gap from identifying network issues to exactly what devices on the network need to be configured, what that correct configuration is, and how to do it.

Part of this is the lack of information that I, and the customers, may have about the architecture, specific devices, configuration, and other aspects of the physical network. But more so, at least in my opinion, is the giant gap between the virtual world of the network data which is packets of energy traveling through the wires and cables, and the physical world of the network devices connecting the wires together.

Network engineers can’t be expected to know everything.
Another factor widening the gap is that although network engineers like myself have extensive expertise in using NPM tools to network troubleshoot, we can’t know everything about every network product available, and even if we did, most enterprise network and network organizations are so large, they are segregated into different teams with different skill sets, and often times different and conflicting goals.

From the network engineers’ point of view the activity of the network is present as 1’s and 0’s, which is decoded into network protocols, which is analyzed to detect issues. To bridge the gap between the analysis of the protocols, I find myself using ChatGPT more and more. It is not perfect, but it helps a lot. Not only does it help me move further down the path of solving problems faster than ever before, but I also remember some of the details along the way, making me a smarter network engineer.

NPM tools, such as protocol analyzers that capture packets and provide in-depth analysis, have long been the stalwarts of network troubleshooting. These tools perform all kinds of advanced analysis on the traffic and can provide many insights into what the problems are. However, as technology advances and networks become larger, they are also becoming more complex, abstract, dynamic, and even software defined. This can make the solutions to these problems harder to figure out, even with many years of experience. Because of this, there is a growing need for innovative solutions to bridge the gap between identifying network problems and fixing the hardware devices causing them.

Enter ChatGPT, the machine learning powerhouse that’s revolutionizing AI network monitoring. Or at least it can if you know how to use it!

Addressing the challenge: connecting network analysis to hardware issues.
Experienced network engineers are no strangers to the challenges posed by complex networks. When a network has performance issues, it often falls on the shoulders of these experts to diagnose and rectify the problem. This task can be time-consuming, requiring a deep understanding of network protocols and hardware configurations.

Traditional NPM tools excel at capturing and analyzing network traffic, providing valuable insights into bottlenecks, latency, and errors. However, these tools often stop short of providing actionable solutions when it comes to the hardware responsible for the issue. This gap between analysis and resolution can lead to prolonged network downtime, frustrated users, and increased operational costs.

The solution: ChatGPT and AI network performance monitoring
ChatGPT, a groundbreaking AI model developed by OpenAI has the potential to bridge the gap between NPM analysis and hardware problem resolution, offering a seamless and efficient approach to network troubleshooting.

Be on the look out for this 10 part blog series

Explaining how AI can bridge that gap and can help network engineers cover better manage their networks with the help of AI:

  1. AI Network Monitoring: Bridging the NPM with ChatGPT: Introduction
  2. Expert Advice for Network Monitoring
  3. Interpretation and Insights for Complex Networks
  4. Recommendations for Network Issue Resolution
  5. Examples of Maximizing Your NPM with ChatGPT
  6. The Future of AI based Network Monitoring (coming soon)
  7. Integration into NPM Tools (coming soon)
  8. Next Steps for AI Network Monitoring (coming soon)
  9. How to Become an Expert Prompt Engineer for Network Engineering (coming soon)
  10. Conclusion (coming soon)