Cisco Technology, Inc.

Vinay Kumar Kolar

Jean-Philippe Vasseur

Grégory Mermoud

Pierre-André Savalle

Santosh Ghanshyam Pandey

Mukund Yelahanka Raghuprasad

Michal Wladyslaw Garcarz

David Tedaldi

Eduard Schornig

Vikram Kumaran

Sambarta Dasgupta

Andrea Di Pietro

Abhishek Kumar

Jürg Nicolaus Diemand

Sharon Shoshana Wulff

Romain Kakko-Chiloff

Stéphane Bernard Martin

Arnaud Quirin

In one embodiment, a device obtains information regarding temporary routing patches applied to a network. Each temporary routing patch implements a routing change in the network for a specified amount of time to avoid or mitigate against a service level agreement violation. The device evaluates, using the information regarding the temporary routing patches applied to the network, a plurality of replay scenarios for the network. The device determines, based on the plurality of replay scenarios, a long-term configuration change for the network. The device provides an indication of the long-term configuration change for display.

Generating long-term network changes from SLA violations

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 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 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 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

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 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

In one embodiment, a service tracks performance of a machine learning model over time. The machine learning model is used to monitor one or more computer networks based on data collected from the one or more computer networks. The service also tracks performance metrics associated with training of the machine learning model. The service determines that a degradation of the performance of the machine learning model is anomalous, based on the tracked performance of the machine learning model and performance metrics associated with training of the model. The service initiates a corrective measure for the degradation of the performance, in response to determining that the degradation of the performance is anomalous.

Anomaly detection of model performance in an MLOps platform

In one embodiment, a device obtains path metrics for a network path via which traffic for an online application is conveyed. The device models uncertainty of an application experience metric predicted for the online application based on the path metrics. The device identifies, based on the uncertainty of the application experience metric modeled by the device, a breaking point in the path metrics at which the application experience metric predicted for the online application is expected to switch from being acceptable to being degraded. The device provides the breaking point in the path metrics for display.

Detecting application performance breaking points based on uncertainty and active learning

In one embodiment, a device obtains quality of experience metrics for an online application whose traffic traverses a particular interface of a router located at a first site in a network. The device identifies a correlation between throughput of the particular interface and the quality of experience metrics for the online application. The device makes a determination that the correlation is a root cause of degradation of the quality of experience metrics for the online application at least in part by determining whether throughput of an interface of a remote router located at a second site in the network is correlated with the quality of experience metrics. The device configures, based on the determination, a priority queue associated with the particular interface for use by traffic of the online application.

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San Jose, CA, United States of America

Vinay Kumar Kolar