Is your network AI as smart as you think?

The network operation type tells us that AI will manage the network in the future. They also tell me that their vendor told them exactly the same thing. The good news is that it’s a kind of truth. The bad news is the same. The emphasis is on the modifier “sort-of”. To get the most out of AI network management, you need to get out of that hazy “kind of” zone, thinking about ants and farmers.

Ants can interconnect tunnels and levels of all kinds to build wonderfully complex anthills. Do worker ants have strong engineer ants to direct this process? No. Each of them simply performs their own simple tasks, and their instincts program them. There are actually ant engineers, but it is their own DNA that organized their work to achieve their goals. This is a bit like the behavior of most network AIs.

Each network consists of a series of technology “collections” like anthills. There is a collection based on vendor, device type, physical location, and connectivity. Looking at today’s network AI, it works primarily in collections. Maybe it manages edge elements like Wi-Fi or maybe SD-WAN or SASE. AI applications that manage collections have management goals built into DNA, design. If we are a Wi-Fi vendor, we know how Wi-Fi works and we are incorporating that knowledge into AI management.

Challenges arise when you stop thinking of collections as independent elements and start thinking of networks as collections of collections. The network is not an anthill, but an entire ecosystem with anthills inside, including many things such as trees and cows. Trees know how to become trees, and cows understand the essence of cowness, but what does they understand the ecosystem? The farm is a farm, not any combination of trees, cows and anthills. It is the farmer who knows what the farm is, not the elements of the farm or the suppliers of those elements. In your network, dear network operation type, that farmer is you.

In the early days, AI developers clearly allowed the separation between the knowledge engineers who built the AI ​​framework and the experts in the areas where knowledge formed the framework. In software, especially DevOps, management tools aim to reach the target state. This shows where cows, trees and ants fit in the farm analogy. If the current state is not the target state, they do something or move. Around to converge on the goal. This is a great concept, but for it to work, you need to know what your goals are. At the enterprise network level, it requires knowledge that Wi-Fi professionals have subliminally introduced into Wi-Fi AI management tools. If AI vendors don’t know how that knowledge is acquired, their AI won’t help.

Keep in mind before deciding that your hopes for AI will be shattered forever! Many network operation types are completely satisfied with the AI ​​that manages the collection of technologies that make up the network. After all, why worry about coordinating Wi-Fi and SD-WAN management when what happened on one side can’t be improved by shaking the other? This Collection-If your AI model fits your needs, you don’t have to be at home.

Copyright © 2021 IDG Communications, Inc.

Is your network AI as smart as you think?

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