Microsoft Technology Licensing, LLC

Smita Ojha

Venkat Narayanan

Art Sadovsky

Olga Ivanova

Yi Wang

Ashish Premaraj

Timur Sadykov

Lei He

Jon Barry Avner

Alexandre De Baynast

Markus Cozowicz

Ulrich Pinsdorf

Philipp Kranen

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.

Detection of outage in cloud based service using usage data based error signals

A failure analysis of a cloud based service is provided using synthetic measurements of the cloud based service. The synthetic measurements associated with a customer experience is executed on the cloud based service to determine a health of the cloud based service. The synthetic measurements simulate the customer experience which includes a use scenario of a customer of the cloud based service. Failures associated with the health of the cloud based service are aggregated, where the failures are detected from the synthetic measurements. A distribution is generated from the failures. The distribution is presented to a stakeholder.

Failure analysis in cloud based service using synthetic measurements

Visual tools are provided for failure analysis in distributed systems. Errors from synthetic measurements and usage data associated with a cloud based service are aggregated by a management application. The errors are processed to create a distribution that segments the errors based on components of the cloud based service. A failed component that generates a subset of the errors associated with a failure is highlighted. The failed component is one of the components of the cloud based service. The distribution is provided in a visualization to identify the failure by emphasizing the failed component with a failure information in proximity to the failed component.

Visual tools for failure analysis in distributed systems

Outage detection in a cloud based service is provided using synthetic measurements and anonymized usage data of the cloud based service. Synthetic measurements and usage data are processed through a shared aggregator to generate aggregated data. The synthetic measurements and the usage data are analyzed through a decision tree to correlate an outage based on the synthetic measurements and the usage data. A confidence value is assigned to the outage. An alert is generated that includes information associated with the outage and the confidence value.

Detection of outage in cloud based service using synthetic measurements and anonymized usage data

Usability of a cloud based service is recovered from a system failure. A customer transaction associated with the customer experience is executed to simulate the customer experience in the cloud based service. A failure associated with a subsystem the cloud based service is detected from an output of the customer transaction. A recovery action is determined to be associated with the failure. The recovery action is executed on the subsystem and monitored to determine a success status.

Recovering usability of cloud based service from system failure

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.

Growing community of inventors

Seattle, WA, United States of America

Smita Ojha