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Responding to Readers: Automated Design?

Deepak responded to my video on network commodization with a question:

What’s your thoughts on how Network Design itself can be Automated and validated. Also from Intent based Networking at some stage Network should re-look into itself and adjust to meet design goals or best practices or alternatively suggest the design itself in green field situation for example. APSTRA seems to be moving into this direction.

The answer to this question, as always, is—how many balloons fit in a bag? 🙂 I think it depends on what you mean when you use the term design. If we are talking about the overlay, or traffic engineering, or even quality of service, I think we will see a rising trend towards using machine learning in network environments to help solve those problems. I am not convinced machine learning can solve these problems, in the sense of leaving humans out of the loop, but humans could set the parameters up, let the neural network learn the flows, and then let the machine adjust things over time. I tend to think this kind of work will be pretty narrow for a long time to come.

There will be stumbling blocks here that need to be solved. For instance, if you introduce a new application into the network, do you need to re-teach the machine learning network? Or can you somehow make some adjustments? Or are you willing to let the new application underperform while the neural network adjusts? There are no clear answers to these questions, and yet we are going to need clear answers to them before we can really start counting on machine learning in this way.

If, on the other hand, you think of design as figuring out what the network topology should look like in the first place, or what kind of bandwidth you might need to build into the physical topology and where, I think machine learning can provide hints, but it is not going to be able to “design” a network in this way. There is too much intent involved here. For instance, in your original question, you noted the network can “look into itself” and “make adjustments” to better “meet the original design goals.” I’m not certain those “original design goals” are ever going to come from machine learning.

If this sounds like a wishy-washy answer, that’s because it is, in the end… It is always hard to make predictions of this kind—I’m just working off of what I know of machine learning today, compared to what I understand of the multi-variable problem of network designed, which is then mushed into the almost infinite possibilities of business requirements.

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