Zscaler, Inc.

Javier Rodriguez Gonzalez

Chakkaravarthy Periyasamy Balaiah

Prasannakumar Jobigenahally Malleshaiah

Sandeep Kamath

Pankaj Chhabra

Satish Kalipatnapu

Sanjit Ganguli

Alexander Frazier

Jay Makwana

Ashok Kolachina

The present systems and methods provide a user performance monitoring solution that enables the monitoring of application and device performance from the end user's point of view. The present systems and methods help Information Technology (IT) personnel to ensure the quality of digital experience across the enterprise. The present system is adapted to collect telemetry data from devices relative to the performance of all tiers of Internet Service Providers (ISPs), create a baseline of the performance of the ISPs based on a plurality of metrics and the collected telemetry data, train a Machine Learning (ML) model to assess blackout and brownout prediction accuracy at different performance values for the metrics, and identify a blackout or brownout, wherein a blackout or brownout is identified when real time performance is worse than the performance values identified by the model.

Advanced machine learning techniques for internet outage detection

Systems and methods include monitoring user experience of one or more users accessing any of the Internet, cloud applications, and private applications; determining a user experience score for the one or more users; responsive to detecting a low user experience score for a user, performing one or more analyses on the user experience of the user; and determining a root cause of the low user experience score based on the one or more analyses. The systems and methods can include determining a remedial action for the user based on the root cause.

Automatic analysis of user experience issues to reduce resolution time

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San Jose, CA, United States of America

Javier Rodriguez Gonzalez