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Monday, June 11, 2018

SDN, machine learning could lead to intelligent networks | SDN applications - TechTarget

Now, as machine learning and AI begin to evolve, the next step is for organizations to integrate networking with tools that will further automate the network, said Mathieu Lemay, founder and CEO of Montreal-based Inocybe Technologies.

To find out more about what this shift may mean for enterprises and service providers, Lemay and John Zannos, Inocybe's chief revenue officer, discussed the evolution of open networking, the necessity to trust machines and the progression toward automated intelligent networks.

Editor's note: This interview has been lightly edited for length and clarity.

Jennifer English, Associate Site Editor for SearchSDN in TechTarget’s Networking media group notes, "The next step for SDN is to integrate network analytics and machine learning to result in automated, intelligent networks. But first, humans need to trust the technology."

Photo: TechTarget

The industry has varying definitions of 'software-defined networking.' How do you define it?

Mathieu Lemay: Basically, I'll lump it all under flexible and programmable networks. For Inocybe, it's more about open networking than it is about SDN, per se, even though we come from an SDN background. It's about the programmability of the network and the dynamicity of the fabric.

In the past, people were operating networks. Now, it needs to be machines. As we get more connected devices, we'll need to have more advanced and intelligent networks. That will have a more machine-to-machine approach to it. So, for us, SDN actually takes the underlying flexible programmable network approach. I'll make it all-encompassing by saying it starts with programmable data planes and finishes with intelligence and AI.

John Zannos: You have to know how the network behaves to have the ability to manage it in an automated way. That ultimately leads to an automated, intelligent network, where you layer in machine learning that consumes collected analytics and then directs the controller.

How far in the future are intelligent networks with well-integrated machine learning?

Lemay: The challenge with machine learning in networks is most of the network challenges we face today -- network outages from human error -- can't be learned by the machine. We have a little bit of a Catch-22. In order to have proper intelligent networks, we need to stop touching them. We need to stop -- or minimize -- human intervention in networking before we can start making the network intelligent. But the problem with operators is they don't trust the intelligence. So, there's this little circle where the network admins don't trust the software, and the software doesn't trust the network admins.

Source: TechTarget

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