Cisco Technology, Inc.

Jean-Philippe Vasseur

Grégory Mermoud

Jonathan W Hui

Pascal Thubert

Patrick Wetterwald

Pierre-André Savalle

Vinay Kumar Kolar

Sukrit Dasgupta

Wei Hong

Eric Michel Levy-Abegnoli

Stefano Benedetto Previdi

Andrea Di Pietro

Javier Cruz Mota

Shmuel Shaffer

David Delano Ward

Sandeep Shetty

Clarence Filsfils

David Tedaldi

Santosh Ghanshyam Pandey

Michal Wladyslaw Garcarz

Laurent Sartran

Eduard Schornig

Mukund Yelahanka Raghuprasad

Muthurajah Sivabalan

Jim Guichard

Sateesh Kumar Addepalli

Raghuram S Sudhaakar

Anna Charny

Vincent Jean Ribiere

Carol Etta Iturralde

Robert Raszuk

Stefano Novello

Sébastien Gay

Jerome Henry

Vikram Kumaran

Fabien Flacher

James Neil Guichard

George Swallow

Alton Lo

Gabriele Maria Galimberti

Navneet Agarwal

Pooya Monajemi

Flavio Giovanni Bonomi

Atul Mahamuni

Robert Goguen

Alexandre Honoré

Diane Bouchacourt

Jürg Nicolaus Diemand

Sharon Shoshana Wulff

Francois Le Faucheur

Thomas David Nadeau

Sambarta Dasgupta

Abhishek Kumar

Giovanni Martinelli

Charles Okwudiafor

Jeff Apcar

Kevin C Lee

Sami Boutros

David R Oran

Zafar Ali

Ornan Alexander Gerstel

Richard W Bradford

Erwan Barry Tarik Zerhouni

Gilman Tolle

Danny Prairie

Sajjit Thampy

Ijsbrand Wijnands

Jan Medved

Hugo Latapie

Enzo Fenoglio

John Galen Scudder

Abhay Kumar Roy

Reshad Rahman

Mohammed Azhar Sayeed

Walid Wakim

Peter De Vriendt

Steve Y Chiu

Anca Zamfir

Jeongyeup Paek

Robert Marcin Raszuk

Norman William Finn

Stewart Frederick Bryant

Alpesh S Patel

Domenico Ficara

Ian Michael Charles Shand

Ornan A Gerstel

Rakesh Gandhi

Amine Choukir

Peter Psenak

Eric Osborne

Warren Scott Wainner

Ajay Kumar

Benoit Claise

Daniel C Tappan

Christopher Metz

Robert H Thomas

Kevin Gagnon

Gad Miller

Sumit Rangwala

In one implementation, a device receives an input request for a large language model-based network troubleshooting agent regarding an issue in a network. The large language model-based network troubleshooting agent performs a lookup of a recipe based on the input request, wherein the recipe comprises contextual information for the issue. The device generates, by the large language model-based network troubleshooting agent, a prompt for a large language model based on the input request and on the recipe. The device provides, by the large language model-based network troubleshooting agent, the prompt to the large language model to troubleshoot the issue in the network.

LLM-based network troubleshooting using expert-curated recipes

In one embodiment, a device discovers one or more network interfaces that an endpoint in a local network could use to access an online application. The device identifies a plurality of connectivity options available to the endpoint to access the online application via an external network. The device makes a prediction that a path that comprises a particular connectivity option from among the plurality of connectivity options and a particular network interface from among the one or more network interfaces will provide an optimal quality of experience metric associated with the online application. The device causes the endpoint to use the path to connect to the online application.

Predictive application-aware routing for remote work

In one embodiment, a device uses a multi-armed bandit model to select different network paths over time via which traffic associated with an online application is routed. The device obtains, from a provider of the online application, application experience metrics associated with the different network paths and indicative of user satisfaction with the online application. The device learns, by the multi-armed bandit model, which of the different network paths will provide satisfactory application experience metrics, based on the application experience metrics associated with the different network paths. The device causes the traffic associated with the online application to be routed via a set of one or more paths expected by the multi-armed bandit model to provide satisfactory application experience metrics for the online application.

Avoiding user experience disruptions using contextual multi-armed bandits

In one embodiment, a device obtains perception results generated by one or more perception models that use media data as input that is transmitted between endpoints of an online application via a network. The device computes performance measures for the one or more perception models, based in part on the perception results and on the media data. The device quantifies, based on the performance measures, quality of experience for the online application. The device causes a configuration change to be made with respect to the online application, based on the quality of experience.

Inferring the user experience for voice and video applications using perception models

In one embodiment, a device obtains media data captured by an endpoint of an online application that results from conducting a test in a network that subjects traffic of the online application to a particular network path degradation pattern. The device receives user feedback regarding the media data from one or more user interfaces. The device computes, based on the user feedback, a quality of experience metric for the online application associated with the particular network path degradation pattern. The device causes a configuration change to be made with respect to the online application, based on the quality of experience metric.

Quantifying application quality of experience under different path performance motifs

In one embodiment, a device forms a plurality of clusters of network paths used to convey traffic for an online application by applying clustering to telemetry data for those network paths. The device determines a predictability metric for a particular cluster in the plurality of clusters. The device provides an indication of the predictability metric for the particular cluster for display. The device enables, based in part on the predictability metric, predictive routing for the network paths in the particular cluster.

Selecting paths for high predictability using clustering

In one embodiment, a recommendation service of a device provides a recommended action to a client of an online application predicted to improve a quality of experience metric for the online application. The device receives feedback from the client indicative of the recommended action not being implemented by a user of the client. The device determines, based on the feedback, a reason for the recommended action not being implemented. The device updates the recommendation service based on the reason for the recommended action not being implemented.

End user recommendation service to ensure satisfactory application QoE

In one embodiment, a device makes a prediction regarding service level agreement violations by a network transport available between a site and an online application. The device associates a risk metric with the prediction, based in part on a type of the network transport. The device computes a longevity metric for the prediction that indicates an expected validity period for the prediction. The device cause traffic to be routed between the site and the online application using the network transport, based on the prediction and its associated risk metric and its longevity metric.

Predictive routing using risk and longevity metrics

In one implementation, a troubleshooting agent executed by a device receives an indication of an anomaly detected in a network by an anomaly detection agent. The troubleshooting agent uses a language model to determine a root cause of the anomaly. The troubleshooting agent determines whether automated remediation for the root cause is allowed by a policy. The troubleshooting agent provides a remediation request to a remediation agent in the network to remediate the root cause of the anomaly, when the policy allows automated remediation for the root cause.

Multi-agent coordination for network anomaly detection, troubleshooting, and remediation using language models

In one embodiment, a device predicts, for each of a plurality of applications accessible via a network, quality metrics for different network paths where traffic for that application be routed via one or more paths among the different network paths. The device generates a congestion risk prediction model that predicts a risk of traffic congestion for a particular combination of: applications from among the plurality of applications, traffic flows associated with those applications, and paths among the network paths via which those traffic flows may be routed. The device performs a constrained optimization based on the predicted quality metrics and on the risk of traffic congestion predicted by the model, to assign traffic flows for the applications to a selected subset of the different paths. The device causes the traffic flows to be routed in the network via the selected subset of the different paths to which they are assigned.

Cross-application predictive routing

In one embodiment, a device uses a quality of experience model to make predictions comprising probabilities of a quality of experience metric for an online application accessed via a network being acceptable. The device determines, for each of a subset of the predictions having lowest probabilities of acceptable user experience from amongst the predictions, whether that prediction corresponds to a negative experience rating by a user of the online application. The device computes a negative retrieval accuracy for the quality of experience model based on a count of negative experience ratings associated with the subset and a size of the subset. The device uses the negative retrieval accuracy to detect an issue in the network.

Measuring performance in cognitive networks

In one embodiment, a method is disclosed comprising monitoring dynamic locations of a plurality of mobile communication devices within a physical area covered by a wireless communication network, wherein keys are distributed to the mobile communication devices at association time; determining that a particular mobile communication device should have a relay for communication with the network based on a first location of the particular mobile communication device and inadequate wireless communication characteristics at the first location; selecting an opportunistic relay device from the mobile communication devices based on a second location of the opportunistic relay device and adequate wireless communication characteristics of the opportunistic relay device within the network and to the first location from the second location; and directing the opportunistic relay device to relay communications for the particular mobile communication device at the first location, wherein the communications are encrypted based on the keys.

Opportunistic relay among mobile communication devices

In one embodiment, a device forms clusters of network paths via which traffic for an online application is conveyed by applying clustering to time series of quality of experience metrics for the online application. The device calculates, for a particular cluster of network paths among the clusters of network paths, correlation metrics between path features of those network paths of that cluster. The device selects, based on the correlation metrics, a particular path feature from among the path features as a root cause of poor quality of experience metrics for the online application along the network paths of the particular cluster of network paths. The device provides, to a user interface, an indication of the particular path feature as being the root cause of poor quality of experience metrics for the online application.

Application degradation root causing and rerouting using time series clustering

In one embodiment, a device obtains data regarding routing decisions made by a machine learning-based predictive routing engine for a network. The device determines, based on the data regarding the routing decisions, a behavior of the machine learning-based predictive routing engine. The device compares the behavior of the machine learning-based predictive routing engine to a behavioral policy for the machine learning-based predictive routing engine. The device adjusts operation of the machine learning-based predictive routing engine, when the behavior of the machine learning-based predictive routing engine violates the behavioral policy.

Safety net engine for machine learning-based network automation

In one embodiment, a device deploys short timescale prediction model and a long timescale prediction model to one or more hosts in a network, whereby the short timescale prediction model predicts failure conditions for an online application that are attributable to the network on a timescale that is shorter than that of the long timescale prediction model. The device configures a trigger that causes the long timescale prediction model to activate predictions by the short timescale prediction model. The device evaluates performance of the short timescale prediction model. The device adjusts the trigger, when the performance of the short timescale prediction model is unacceptable.

Capturing network dynamics and forecasting across multiple timescales

Growing community of inventors

Saint Martin d'Uriage, France

Jean-Philippe Vasseur