US 12,244,469 B2
System and method for supervised event monitoring
Hagit Grushka, Beer-Sheva (IL); Rachel Lemberg, Herzliya (IL); and Yaniv Lavi, Tel Aviv (IL)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Dec. 8, 2022, as Appl. No. 18/077,568.
Prior Publication US 2024/0195702 A1, Jun. 13, 2024
Int. Cl. H04L 41/16 (2022.01); G06F 11/07 (2006.01); G06F 11/30 (2006.01); G06F 11/34 (2006.01); G06N 20/00 (2019.01); H04L 41/5009 (2022.01)
CPC H04L 41/16 (2013.01) [G06F 11/3006 (2013.01); G06F 11/3447 (2013.01); G06N 20/00 (2019.01); H04L 41/5009 (2013.01); G06F 11/0709 (2013.01); G06F 11/3409 (2013.01); G06F 2201/81 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A computer-implemented method, executed on a computing device, comprising:
processing event data associated with a plurality of known operational impact events on a cloud computing service and operational data associated with the cloud computing service using a supervised machine learning model conditioned on an operational impact parameter associated with the cloud computing service, wherein the operational impact parameter is a parameter used for determining a lowest detection threshold to achieve a particular performance metric of the cloud computing service;
generating a detection threshold using the supervised machine learning model;
identifying a gap in coverage of the plurality of known operational impact events by:
identifying uncovered operational data and a plurality of uncovered known operational impact events, wherein the uncovered operational data and the plurality of uncovered known operational impact events are unreported when using the detection threshold;
updating a monitoring configuration associated with a business service based upon, at least in part, the gap in the coverage of the plurality of known operational impact events;
generating an updated detection threshold based upon, at least in part, the gap in the coverage of the plurality of known operational impact events; and
updating the supervised machine learning model with the operational data indicative of an operational impact event associated with the business service on a periodic basis to continually generate the updated detection threshold, wherein updating the supervised machine learning model continually generates the updated detection threshold by modifying the updated detection threshold to filter out noisy operational data associated with the cloud computing service that is not indicative of an operational impact event from detection of future operational impact events.