Microsoft Technology Licensing, LLC

Vincent Joseph Blas

Sean Michael James

Nicholas Andrew Keehn

Michael Rees Helsel

Brandon Middaugh

Zhangwei Xu

Oren Rosenbloom

Murali Mohan Chintalapati

Nicholas Anthony Swanson

Anupama Vedapuri

Bing Hu

Peiling Wu

Chaturanan Damodar Mishra

Igor Figlin

Robert Gerard Boyce

Systems, methods, and devices are described for intelligent outage evaluation and insight management for monitoring and incident management systems. A set of incident logs associated with a cloud computing service are received by a cloud computing service provider system. Each incident log includes impact data corresponding to a respective incident that occurred during an evaluation period. The impact data includes an interruption count representative of a number of interruptions on resources caused by the respective incident. A total active resource time representative of resource activity during the evaluation period is determined. An interruption rate is determined for the evaluation period by summing interruption counts to generate a total interruption count and dividing the total interruption count by the total active resource time. An actionable insight applicable to the cloud computing service is generated based on the interruption rate and the incident logs.

Intelligent outage evaluation and insight management for monitoring and incident management systems

Improving the operations of a computer system that is located within a power grid and that is associated with its own power sources. Past operational characteristics of the power grid are analyzed to derive learned characteristics for the power grid. Future operational characteristics of the power grid are also monitored. A prediction regarding a future load event associated with the power grid is then generated using the learned characteristics and the monitored characteristics. In response to this prediction, one or more operations are performed to balance the computer system with the power grid during the future load event, and to ensure a determined availability of services associated with the computer system during the future load event.

Learning power grid characteristics to anticipate load

Improving the operations of a data center that is located within a power grid and that includes its own power sources. Past operational characteristics of the power grid are analyzed to derive learned characteristics for the power grid. Current and/or future operational characteristics of the power grid are also monitored. A prediction regarding an upcoming, anticipated load for the power grid is then generated using the learned characteristics and the monitored characteristics. In response to this prediction, one or more mitigation operations are selected and then performed at the data center to ensure that the data center is adequately available. Some of these mitigation operations include, but are not limited to, causing the data center to consume more power, causing the data center's power sources to store more power, or causing the data center to migrate services and/or data to a different data center.

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Edmonds, WA, United States of America

Vincent Joseph Blas