Microsoft Technology Licensing, LLC

Narayan Nevrekar

John Morgan Oslake

Subramanian Muralidhar

Jeffrey Robert Currier

Richard Shawn Bice

Scott Rosemund

Justin R Dellamore

Vishal Kumar Seshagirirao Anil

Heriknaz Vermishyan

Francisc Stugren

Sohail Baig Mohammed

Praveen Seshadri

Kevin Joseph Fischer

Kenneth Van Hyning

Atul Bhartia

Colleen Elizabeth Hamilton

Shawn Bice

Matt Hollingsworth

Jin Feng

Kazi Al-Rashid

Ken Van Hyning

Michael Guthrie

Venkat Siva Sai Krishna Khanderao

Techniques are described herein that are capable of performing metadata extraction based on resource attributes and user-defined policy and topology. A hierarchical resource-grouping construct, which is selected from hierarchical resource-grouping constructs in a user-defined hierarchical resource-grouping topology of a cloud computing service account, is registered by enumerating at least a subset of the constructs. The subset includes the selected construct and each other construct encompassed by the selected construct. Metadata is selectively extracted from the constructs that are included in the subset to provide extracted metadata based at least in part on one or more user-defined policies, which are assigned among the constructs that are included in the subset, indicating whether the metadata is to be extracted as a result of resources associated with the constructs included in the subset having specified attributes. A searchable catalog is generated that includes the extracted metadata.

Metadata extraction based on resource attributes and user-defined policy and topology

In some examples, a computing device determines a data manipulation from a job specification. The device determines a corresponding data-processing instruction using data-source metadata, and determines and executes a corresponding query. In some examples, a device receives search keys. The device searches data-source metadata using the search keys. The device weights a first data source based on producer-consumer relationships between data sources, and ranks the first data source using the weight. In some examples, a device determines structural and content information of a data record. The device determines a data-source identifier from the structural information and stores the content information with the data-source identifier in a database. In some examples, via a user interface, a device receives a job specification and annotation data. The device stores the spec and the annotation data in a metadata repository.

Database operation using metadata of data sources

Data assets, such as streams, databases, spreadsheets, or other data sources or types, are identified and representations of the data asset are stored. The representation of a data asset includes a schema used by the data asset, a location of the data asset, and keywords or other descriptive information. The representations of each data asset are indexed, and a search interface is provided that allows users to search for relevant data assets. In addition, dependencies, or other relationship information, among the various data assets is maintained and is used to generate a graph that shows the interrelatedness of the data assets. The graph can be explored by users to select data assets, and used to alert users when a change has been made to a data asset that may affect a data asset that they have used.

Discovery of data assets using metadata

Embodiments are directed to declaratively managing software applications, dynamically applying configuration changes to a specified software application based on a service level objective (SLO) and to efficiently performing an impact analysis for a specified service level agreement SLO.

Service level objective for cloud hosted applications

A method includes displaying a plurality of user modifiable utilization views of a monitored software application, where each of the plurality of user modifiable utilization views is customizable to fit a domain of analysis by identifying one or more resource types of multiple resource components. Each of the plurality of user modifiable utilization views is displayed in one more graphical user interfaces. The method includes determining a desired ordering of the one or more resource types within a hierarchy of types and filtering one or more details of the multiple resource components to create a customized view. A user can navigate between each of the plurality of user modifiable utilization views to dynamically construct related target analysis views including filtered details across one or more other domains of analysis.

Visualization for resource capacity management

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Bellevue, WA, United States of America

Narayan Nevrekar