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Silver Peak Applies Machine Learning to SD-WANs

Mike Vizard

In the near future, software-defined wide area networks (SD-WANs) will employ machine learning algorithms to automatically route network branch traffic across the most appropriate connection available at any given moment in time.

Silver Peak CEO David Hughes says IT organizations are already seeing the benefits of applying machine learning algorithms to wide area network traffic via the Silver Peak Unity EdgeConnect SD-WAN that was recently upgraded to include the ability to identify the first packet being transferred to determine where best to route that traffic using machine learning algorithms.

In all, Hughes says Silver Peak has three machine learning projects underway that promise to significantly simplify the management of SD-WANs by being able to dynamically shift traffic between multiple types of WAN traffic based on costs, security requirements, and the attributes of the application.

Hughes says there’s already a major shift to SD-WANs underway. Silver Peak today announced it now has 400 paying customers for its Unity EdgeConnect SD-WAN, which Hughes says represents 4X growth in the last year. While Silver Peak has been selling a wide area network (WAN) optimization platform for years, the shift to SD-WANs is significantly expanding the company’s base of customers.

“Of our SD-WAN customers, only 25 percent were WAN op customers,” says Hughes. “The other 75 percent are net new customers.”


As thin branch computing at the edge of the network functions such as WAN optimization, routing and firewalls are all being consolidated onto a single SD-WAN platform, it may take some time for that transition to play out. But the days when IT organizations needed to integrate a disparate set of networking and security services within every branch office are finally coming to an end.

 


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