Cisco will use AI/ML to boost intent-based networking

Cisco explains how artificial intelligence and machine learning fit into a feedback loop that implements and maintain desired network conditions to optimize network performance for workloads using real-time data.

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Artificial Intelligence and machine learning are expected to be some of the big topics at next week’s Cisco Live event and the company is already talking about how those technologies will help drive the next generation of Intent-Based Networking.

“Artificial intelligence will change how we manage networks, and it’s a change we need,” wrote John Apostolopoulos Cisco CTO and vice president of Enterprise Networking in a blog about how Cisco says these technologies impact the network. 

AI is the next major step for networking capabilities, and while researchers have talked in the past about how great AI would be, now the compute power and algorithms exist to make it possible, Apostolopoulos told Network World. 

To understand how AI and ML can boost IBN, Cisco says it's necessary to  understand four key factors an IBN environment needs: infrastructure, translation, activation and assurance.

Infrastructure can be virtual or physical and include wireless access points, switches, routers, compute and storage. “To make the infrastructure do what we want, we use the translation function to convert the intent, or what we are trying to make the network accomplish, from a person or computer into the correct network and security policies. These policies then must be activated on the network,” Apostolopoulos said.

The activation step takes the network and security polices and couples them with a deep understanding of the network infrastructure that includes both real-time and historic data about its behavior. It then activates or automates the policies across all of the network infrastructure elements, ideally optimizing for performance, reliability and security,  Apostolopoulos wrote.

Finally assurance maintains a continuous validation-and-verification loop.  IBN improves on translation and assurance to form a valuable feedback loop about what’s going on in the network that wasn’t available before.   

Apostolopoulos used the example of an international company that wanted to set up a world-wide video all-hands meeting.  Everyone on the call had to have high-quality, low-latency video, and also needed the capability to send high-quality video into the call when it was time for Q&A.

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