Juniper Networks, Inc.

Kaushik Adesh Agrawal

Ruchit Rajkumar Mehta

Jisheng Wang

Patrick John MeLampy

Patrick Timmons

Michael Baj

Prashant Kumar

Greg Schrock

Abilash Menon

Scott A McCulley

Sarah V Jaffer

Michael John Adams

Anna Yungelson

Keh-Ming Luoh

Jie C Jiang

Ryan Koss

Juei Cheng Lo

Gorakhanath Kathare

Yogesh B G

Monimoy Deb Purkayastha

Dennis Gordon Montgomery

Patrick John Melampy

Ajit Krishna Patankar

Kihwan Han

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 disclosed for reporting diagnostics data by a first network device to a cloud-based Wide Area Network (WAN) assurance system, responsive to the first network device detecting a communication issue with the cloud-based WAN assurance system. For example, the first network device detects an issue with sending telemetry data to the cloud-based WAN assurance system via a first communication path. In response, the first network device determines a second network device that has connectivity to the WAN assurance system. The first network device sends diagnostics data to the second network device along a second communication path for forwarding to the cloud-based WAN assurance system. The cloud-based WAN assurance system receives the diagnostics data from the second network device. The cloud-based WAN assurance system controls the second network device to remediate the first network device based on the diagnostics data.

Diagnostics reporting for wide area network assurance system

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

A device may receive network traffic data that includes network traffic packet sizes, and may transform the network traffic data into transformed data. The device may process the transformed data, with a machine learning model, to generate an embedding, and may obtain a similarity metric for the embedding. The device may create a graph with nodes and edges based on the embedding and the similarity metric, and may process the graph, with a community detection model, to identify network traffic categories for the network traffic data. The device may perform one or more actions based on the network traffic categories.

Utilizing machine learning models for network traffic categorization

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

Techniques are described for monitoring application performance in a computer network. For example, a network management system (NMS) includes a memory storing path data received from a plurality of network devices, the path data reported by each network device of the plurality of network devices for one or more logical paths of a physical interface from the given network device over a wide area network (WAN). Additionally, the NMS may include processing circuitry in communication with the memory and configured to: determine, based on the path data, one or more application health assessments for one or more applications, wherein the one or more application health assessments are associated with one or more application time periods for a site, and in response to determining at least one failure state, output a notification including identification of a root cause of the at least one failure state.

Predicting network issues based on historical data

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.

Wireless signal strength-based detection of poor network link performance

Application service level expectation health and performance

A first router generates session establishment metrics for use in network path selection. For example, a plurality of routers connect a client device to a network service instance hosted by a server. A first router is connected to the network service instance via first and second paths. The first router receives session performance requirements for a session between the client device and the network service instance. The first router forwards, along the first path, network traffic for the session by modifying a first packet of the session to include a session identifier for the session. The first router determines that session establishment metrics for the session do not satisfy the session performance requirements. In response, the first router forwards, along the second path, the network traffic for the session by modifying a second packet of the session to include the session identifier for the session.

Session monitoring using metrics of session establishment

Techniques are disclosed for generating session-specific packet capture records. In one example, a first network device receives a first packet of a session between first and second client devices, the session comprising forward and reverse packet flows. The first network device modifies the first packet to include metadata comprising a packet capture indicator that indicates whether packet capture is to be performed for the session. The first network device stores at least a portion of the first packet and each subsequent packet of the session and forwards the modified first packet. A second network device receives the modified first packet and, based on the packet capture indicator, stores at least a portion of the first packet and each subsequent packet of the session in a session-specific packet capture record. The first and second network devices may generate, from the stored packet data, a packet capture record for the session.

Growing community of inventors

Chelmsford, MA, United States of America

Kaushik Adesh Agrawal