Cisco Technology, Inc.

Jean-Philippe Vasseur

Grégory Mermoud

Jonathan W Hui

Pascal Thubert

Patrick Wetterwald

Vinay Kumar Kolar

Pierre-André Savalle

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

Pierre-Andre Savalle

Laurent Sartran

Michal Wladyslaw Garcarz

Muthurajah Sivabalan

Jim Guichard

Sateesh Kumar Addepalli

Raghuram S Sudhaakar

Mukund Yelahanka Raghuprasad

Anna Charny

Vincent Jean Ribiere

Carol Etta Iturralde

Robert Raszuk

Eduard Schornig

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

Abhay Kumar Roy

John Galen Scudder

Reshad Rahman

Mohammed Azhar Sayeed

Walid Wakim

Peter De Vriendt

Steve Y Chiu

Anca Zamfir

Vanson Lim

Jeongyeup Paek

Norman William Finn

Stewart Frederick Bryant

Alpesh S Patel

Ian Michael Charles Shand

Domenico Ficara

Ornan A Gerstel

Rakesh Gandhi

Peter Psenak

Eric Osborne

Amine Choukir

Ajay Kumar

Warren Scott Wainner

Benoit Claise

Daniel C Tappan

Christopher Metz

Robert H Thomas

Kevin Gagnon

Gad Miller

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

Techniques to determine location-related data for a plurality of access points located in an area in communication with a network by determining that a plurality of access points in a network are associated with a same geographical area including identifying, from among the plurality of access points, a first access point associated with the geographical area, determining first location-related data for the first access point, determining second location-related data for a second access point of the plurality of access points, the second access point being interior to the first access point within the network based at least in part on the second access point having at least a threshold number of the neighbor access points, and exchanging ranging data indicative of a first relative distance between the first access point and the second access point, the ranging data based at least in part on ranging message exchange measurements.

Systems and methods for determining locations of access points

In one embodiment, a supervisory device in a network forms a virtual access point (VAP) for a node in the network. A set of access points (APs) in the network are mapped to the VAP as part of a VAP mapping and the node treats the APs in the VAP mapping as a single AP for purposes of communicating with the network. The supervisory device receives measurements from the APs in the VAP mapping regarding communications associated with the node. The supervisory device identifies a movement of the node based on the received measurements from the APs in the VAP mapping. The supervisory device adjusts the set of APs in the VAP mapping based on the identified movement of the node.

Transparent roaming in virtual access point (VAP) enabled networks

In one embodiment, a device identifies peaks of a timeseries of a path metric used to predict performance of a path in a network. The device determines one or more characteristics of the peaks of the timeseries. The device computes, based on the one or more characteristics of the peaks, a measurement frequency for the path metric. The device causes the path metric to be measured in the network according to the measurement frequency.

Dynamic input granularity estimation for network path forecasting using timeseries features

In one embodiment, a device identifies a plurality of online applications whose traffic is conveyed via an interface of a networking entity of a network. The device computes a relationship between a quality of experience metric for a particular online application in the plurality of online applications and a traffic volume associated with the interface for the plurality of online applications. The device makes, based on the relationship, a determination that degradation of the quality of experience metric for the particular online application is due to the traffic volume associated with the interface for the plurality of online applications. The device reconfigures, based on the determination, the networking entity to prioritize traffic for the particular online application.

Optimizing application experience in hybrid work environments

In one embodiment, a device causes, in accordance with a probing strategy, performance of a probing test by one or more agents in a network and with respect to an online application. The device obtains quality of experience measurements for the online application. The device adjusts, using reinforcement learning, the probing strategy based on how well a predictive model was able to predict the quality of experience measurements given results of the probing test. The device repeats the causing, obtaining, and adjusting steps using the probing strategy adjusted by the device, to find a minimally disruptive probing strategy that provides acceptable performance by the predictive model.

Learning probing strategies for QoE assessment

In one embodiment, a device obtains data indicative of quality of experience for an online application. The device predicts, based on the data, path performances of network paths between an endpoint and the online application for different traffic loads. The device selects traffic loads for the network paths between the endpoint and the online application, based on the path performances predicted by the device. The device causes application traffic to be load balanced across the network paths between the endpoint and the online application, in accordance with those traffic loads selected by the device.

Load balancing application traffic with predictive goodput adaptation

In one embodiment, a supervisory service for a network obtains quality of experience metrics for application sessions of an online application. The supervisory service maps the application sessions to paths that traverse a plurality of autonomous systems. The supervisory service identifies, based in part on the quality of experience metrics, a particular autonomous system from the plurality of autonomous systems associated with a decreased quality of experience for the online application. The supervisory service causes application traffic for the online application to avoid the particular autonomous system.

Autonomous system bottleneck detection

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Saint Martin d'Uriage, France

Jean-Philippe Vasseur