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Sunday, April 15, 2018

Juniper: Machine Learning Isn't All We Need for Smart Networks

#Softwaredefined networks ( #SDN )give you flexibility, but to make them really effective at scale we need to take humans out of the loop and use automation to respond more quickly – like taking an optical link down for maintenance and moving the traffic over to another line automatically as the latency rises. Plus, we need to do that before the speed drops enough to cause problems for the workloads relying on that connection. That kind of automation will create something more like a " #selfdriving " network, @Juniper platform systems CTO @KireetiKompella told @DataCenterKnowledge; but just as with self-driving cars, the prospect is exciting but also raises some long term concerns.  SPONSORED CONTENT Cloud Native Cybersecurity APR 02, 2018 This is about creating adaptive, self-customizing services built on the flexibility of SDNs and Network Function Virtualization which means that instead of being a monolithic device, network hardware exposes APIs and functions. But even though we have what Kompella calls “power sharing between equipment makers and the people who deploy networks, who want more of a say in how systems are being built,” the problem is that it can also end up like parents fighting, forgetting about the children caught in the middle. “The kids are the users, the applications, the IoT devices sitting on the edge of the network trying to use the network, and we ignore them. We give them a portal and APIs. We say ‘fill out this profile so I'll know how to deal with you,' but that’s good enough. We need to think about how to build systems in a different way, and makers and deployers of equipment need to work together to give users a better quality of life.”

http://www.datacenterknowledge.com/networks/juniper-machine-learning-isnt-all-we-need-smart-networks

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