Microsoft Technology Licensing, LLC

Vinod Mukundan Menon

Rahul Nigam

Mark Raymond Gilbert

Mangalam Rathinasabapathy

Esha Sharma

Andrei Nicolae

Srigopal Chitrapu

In non-limiting examples of the present disclosure, systems, methods and devices for detecting and classifying service issues associated with a cloud-based service are presented. Operational event data for a plurality of operations associated with the cloud-based application service may be monitored. A statistical-based unsupervised machine learning model may be applied to the operational event data. A subset of the operational event data may be tagged as potentially being associated with a code regression, wherein the subset comprises a time series of operational event data. A neural network may be applied to the time series of operational event data, and the time series of operational event data may be flagged for follow-up if the neural network classifies the time series as relating to a positive code regression category.

Automated detection of code regressions from time-series data

A method of and system for identifying one or more outlier anomalies in a computer environment is carried out by collecting data from a computing environment, identifying a plurality of anomalies in the computing environment based in part on the collected data, grouping the plurality of anomalies into one or more clusters, and classifying each of the one or more clusters based on a plurality of dimensions. The method may also include assigning a weight to each dimension of the plurality of dimensions for each of the one or more clusters, aggregating the weights assigned to each dimension to calculate a score for each of the one or more clusters, and generating a ranking for each of the one or more clusters base in part on the calculated score. After the rankings are generated, one of the clusters may be identified as an outlier anomaly based on the rankings. The plurality of dimensions and the weights assigned to each dimension may be selected by employing machine-learning models.

Identifying service issues by analyzing anomalies

In non-limiting examples of the present disclosure, systems, methods and devices for monitoring telemetry data from a cloud-based application service are presented. Telemetry data for a plurality of operations for the cloud-based application service may be analyzed, wherein the analysis comprises comparing a first time series with a second time series, and where data from the second time series relates to operations that were executed prior in time compared with execution of operations related to the first time series. One or more operational changes in the cloud-based application service may be identified based on the analysis, and at least one telemetry monitor may be dynamically configured based on the one or more operational changes that were identified.

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Bothell, WA, United States of America

Vinod Mukundan Menon