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Anritsu eoMind: Streaming analytics for the virtualized network

 

As service providers look to virtualization and cloud services to provide new flexibility and revenue streams, the evolution of the network presents new challenges for monitoring and troubleshooting.

“Everybody is looking at cloud services and also virtualization, so there’s huge opportunities there for lower total cost of ownership and improved flexibility with the whole idea of service chaining micro-services,” said Neil McKinlay, head of project management for Service Assurance Solutions at Anritsu. However, he went on to add that despite the opportunities that virtualization presents, “it brings a major set of issues, because the old fundamentals of location, network device and everything like that, changes – because data could be coming from two different sides of the country into one single point. So how do you then say, okay, this piece of the network needs more capacity?”

A new approach to real-time, streaming analytics is needed, McKinlay said: one that puts the customer experience at the center of automated analysis, rather than the network itself or the services that run on the network.

“It’s really a chance from thinking of the network to being completely customer-focused, and actually dealing with the customer as the fundamental keystone of your analysis,” McKinlay said.

[embedyt] https://www.youtube.com/watch?v=M_6hhdU26u8[/embedyt]

Bringing this vision to fruition is difficult, however, in a landscape where self-healing networks and real-time automation have not yet been fully realized, he added.

“The reality is that even with that functionality, if it becomes available, the real challenge there becomes the ability to do it fast enough – to do it in actual, real-time,” McKinlay said — meaning in seconds or even milliseconds, not with a delay of minutes.

To address these challenges, Anritsu’s Service Assurance Solutions unit has launched eoMind, the industry’s first self-operating machine-learning platform that automates understanding of the customer experience by highlighting “clouds of issues” as they happen – dropped calls or network congestion, for example. Without human intervention, eoMind puts the issue, root cause analysis and all the customers impacted in front of users. This information can even be delivered to a smartphone, McKinlay added – and some Anritsu Solutions customers, he said, run their networks from their smartphones via eoMind.

See a demonstration of eoMind below and learn more about eoMind here.

[embedyt] https://www.youtube.com/watch?v=JMNygmOXqVM[/embedyt]

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