Yi W Wang

20160062815, Mar 3, 2016

Sep 2, 2014

14/475550

- Redmond WA, US
Smita Ojha - Seattle WA, US
Alexandre de Baynast - Aachen, DE
Markus Cozowicz - Salzburg, AT
Ulrich Pinsdorf - Aachen, DE
Yi Wang - Redmond WA, US
Philipp Kranen - Aachen, DE
Venkat Narayanan - Redmond WA, US

G06F 11/07
G06N 99/00
G06F 11/30

Anomalies detection in error signals of a cloud based service is provided. An application such as an analysis application identifies a machine learning algorithm that matches error signals of components of a cloud based service. A periodic pattern from the error signals is removed with the machine learning algorithm to filter the periodic pattern from an error count in the error signals. The error signals are processed with the machine learning algorithm to detect one or more anomalies with the components. The machine learning algorithm is updated while processing new data to detect new patterns.

20160062816, Mar 3, 2016

Sep 2, 2014

14/475543

- Redmond WA, US
Venkat Narayanan - Redmond WA, US
Smita Ojha - Seattle WA, US
Lei He - Redmond WA, US
Art Sadovsky - Bellevue WA, US
Yi Wang - Redmond WA, US
Ashish Premaraj - Bellevue WA, US

G06F 11/07
G06F 21/62

Outage detection in a cloud based service is provided using usage data based error signals. Usage data is collected from component of the cloud based service or client devices of the cloud based service based on customer actions on the cloud based service. The usage data is aggregated and normalized to generate an error signal from errors generated from a component of the cloud based service. An outage is detected from the error signal. An alert that includes information associated with the outage and one or more customers impacted by the outage is generated.