Microsoft Technology Licensing, LLC

Ananth Geethanath

Shankaranand Arunachalam

Rakesh Namineni

Ali Alam

Suneel Suresh

Mohit Verma

The present disclosure relates to methods, systems, and computer readable media for analyzing log files for a wide variety of services (e.g., cloud computing services or microservices) to determine whether the services are operating as designed over some period of time associated with the log file(s). The present disclosure includes features and functionality for training or otherwise generating a model being configured to predict whether portions of an input log file include data reflective of normal operations of a corresponding service used to generate the input log file. The present disclosure provides a domain-agnostic approach to training an outlier detection model to analyze log files for a wide variety of services.

Training and implementing a steady state log analyzer

A system and method for detecting anomalies in a data stream is described. The system receives the data stream that comprises values of metrics derived from observations of operation of a computing entity over a time window. A model comprising variances of the data over the time window is formed. The model identifies operating thresholds for each metric based on the variances of the data for each metric in the data stream. The system computes a steady state distance matrix of the data stream. The system determines that the steady state distance matrix exceeds a steady state threshold. In response to determining that the steady state distance matrix exceeds the steady state threshold, the system computes a pattern distance matrix based on the steady state distance matrix. The anomaly in the data stream is detected based on the pattern distance matrix. The system generates an alert indicating the anomaly.

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Bothell, WA, United States of America

Ananth Geethanath