Microsoft Technology Licensing, LLC

Ya Lin

Zi Ye

Willis Lang

Justin G Moeller

Subramaniam Venkatraman Krishnan

Mathieu Baptiste Demarne

Yiwen Zhu

Joyce Yu Cahoon

Sreraman Narasimhan

Haitao Song

Youquan Su

Shirley F Tan

Ashwin Shrinivas

Wenjing Wang

Yvonne Mckay

Thomas R Michaels, Jr

Neetu Singh

Barton K Duncan

Grant R Culbertson

Raymond Truong

Zhirui Yuan

XingYu Liu

Maria Alexandra Ciortea

Pratyush Rawat

Methods, systems, and computer program products are provided for creating a resource management testing environment. An initial population of databases is established in a database ring, having an in initial count of databases and different types of databases that are determined based on an initial database population model. The initial population model receives ring classification information for the database ring from a ring grouping model. A sequence of database population-change events is generated based on a model, to change the population of the databases over time in the ring. An orchestration framework performs testing of resource manager operations based on the model-defined initial population of databases and the model-defined populations of databases changed over time. Model-defined resource usage metrics for each database are utilized to test the resource manager operations. Resource usage metrics and database add/drop events of a production system are used to train the models.

Database simulation modeling framework

Methods, systems, apparatuses, and computer-readable storage mediums described herein are directed to determining and recommending an optimal compute resource configuration for a cloud-based resource (e.g., a server, a virtual machine, etc.) for migrating a customer to the cloud. The embodiments described herein utilize a statistically robust approach that makes recommendations that are more flexible (elastic) and account for the full distribution of the amount of resource usage. Such an approach is utilized to develop a personalized rank of relevant recommendations to a customer. To determine which compute resource configuration to recommend to the customer, the customer's usage profile is matched to a set of customers that have already migrated to the cloud. The compute resource configuration that reaches the performance most similar to the performance of the configurations utilized by customers in the matched set is recommended to the user.

Cloud-based compute resource configuration recommendation and allocation for database migration

A computing system automatically manages error reports. Each error report specifies an error that occurred within a subsystem of the computing system. A received error report is added into a root cause grouping. Each root cause grouping contains error reports having error types traceable to a same root cause. A deployment time at which the subsystem corresponding to the error report was deployed within the computing system is determined. A severity score for the root cause grouping is generated as a function of the deployment time. The severity score inversely correlates to a time period length between the deployment time and the occurrence time of the error. The root cause grouping is assigned to a ranked error container of a plurality of ranked error containers based on the generated severity score. Each ranked error container contains root cause groupings having severity scores within a specified score range.

Automated error triage scoring

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Bellevue, WA, United States of America

Ya Lin