Microsoft Technology Licensing, LLC

Jeremy Michael Bares

Piyush Gupta

Craig Henry Wittenberg

Weili Zhong McClenahan

Ritchie Nicholas Hughes

David Dominic Anderson

Christopher Grant Ashton

Rajive Kumar

Sushant Sood

Havva Gulay Gurbuz

Kiran Belthur Parthasarathy

Jay Christopher Svoboda

Marc Van Eijk

The present disclosure relates to methods and systems for monitoring release pipelines. The methods and systems calculate health metrics of release pipelines using telemetry data from the release pipelines. The methods and systems determine whether the release pipelines are healthy based on the health metrics. The methods and systems automatically trigger one or more actions in response to determining that the release pipelines are unhealthy and continue to monitor the release pipelines in response to determining that the release pipelines are healthy.

Applying service level objectives to release pipelines

The disclosure is directed to a topology recommendation platform that implements a method that includes receiving an original topology that indicates a first design for an application. The original topology is associated with a first resiliency score. The first design is associated with a set of functionalities for the application. A recommended topology is determined based on a model and the original topology. The recommended topology indicates an alternative design for the application. The recommended topology is associated with a second resiliency score that is greater than the first resiliency score. The alternative design is associated with the set of functionalities for the application. The recommended topology is provided. The original topology and/or the recommended topology is based on and/or encoded via one or more topology templates. The first design indicates a first set of components for the application. The alternative design indicates an alternative set of components for the application.

Topology recommendation platform for application architecture

Techniques are described herein that are capable of performing an aggregation-based determination of resiliency of a cloud computing service to chaos events. Chaos events are executed against a cloud computing service during a time frame that includes multiple time periods. For each chaos event, service health indexes (SHIs) that correspond to the time periods are calculated. Each SHI indicates an extent to which indicators, which represent respective measurements of performance of the cloud computing service during the respective time period, satisfy respective criteria. The SHIs for each chaos event are analyzed to provide a respective resiliency score. At least some of the resiliency scores of the respective chaos events are aggregated to provide an aggregated resiliency score, which represents resiliency of the cloud computing service to the chaos events.

Aggregation-based determination of cloud computing service resiliency to chaos events

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Jeremy Michael Bares