Juniper Networks, Inc.

Ruchit Rajkumar Mehta

Kaushik Adesh Agrawal

Patrick Timmons

Jisheng Wang

Patrick John MeLampy

Michael Baj

An example method includes receiving, by a software-defined networking in a wide area network (SD-WAN) system having a first WAN link and a second WAN link for an SD-WAN service, WAN link characterization data for the first WAN link over a time period; determining, by the SD-WAN system based on processing the WAN link characterization data for the first WAN link using a machine learning model trained with historical WAN link characterization data for one or more WAN links, an indicator of a predicted performance metric of the first WAN link at a future time; and reassigning, by the SD-WAN system based on the indicator, an application from the first WAN link to the second WAN link.

AI-assisted WAN link selection for SD-WAN services

Techniques are described for determining one or more outlier logical paths in a computer network. A cloud-based network management system stores path data received from a plurality of network devices operating as network gateways for an enterprise network, the path data collected by each network device of the plurality of network devices for one or more logical paths of a physical interface from the network device over a wide area network (WAN). The network management system compares the path data for the plurality of logical paths to determine one or more outlier logical paths out of the plurality of logical paths. The network management system, in response to determining the one or more outlier logical paths, output a notification indicative of the one or more outlier path data out of the plurality of logical paths.

Peer comparison-based outlier detection for network performance monitoring

An example method includes receiving, by an SD-WAN system, WAN link characterization data for a plurality of WAN links of the SD-WAN system over a time period; and for each site of a plurality of sites of the SD-WAN system, generating, by the SD-WAN system, a local policy for the site, wherein generating the local policy is based on a machine learning model trained with the WAN link characterization data for the plurality of WAN links, and providing the local policy to an SD-WAN edge device of the site.

Adaptive location-based SD-WAN policies

A cloud-based network management system (NMS) stores path data from network devices operating as network gateways for an enterprise network, the path data collected by each network device of the plurality of network devices. The NMS determines, for a logical path within a specified time window, a wireless signal quality and a link quality based at least in part on the path data. The NMS, in response to determining that the logical path is of a poor link quality, determine a correlation between a poor wireless quality and the poor link quality. The NMS may output a notification that indicates the correlation between the poor wireless quality and the poor link quality of the logical path.

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Emeryville, CA, United States of America

Ruchit Rajkumar Mehta