Microsoft Technology Licensing, LLC

Marc Andrew Power

Marcus Jon Jager

Matthias Adam Leibmann

Eric Lynn Eilebrecht

Yang Yang Zhang

Steven Robert Sandke

Matthew Terence Hayes

Grigory V Kaplin

Anastasios Kasiolas

Paranthaman Saravanan

Yi Zeng

Brian D Remick

William Raymond Hoffman

Andrey Lukyanov

Daniel Joseph Rogers

Dmitry Dmitrievich Garaev

Haruya Shida

Marcus Vinicius Silva Gois

Rong Guo

Arpan Jugalkishor Asawa

Ritesh Kumar Sinha

Sidi Lin

Ryan Lee Jobse

Sharanya Chinnusamy

Sejal Kiran Shinde

Yang Zhang

A graph tracks the lineage of customer data, including when it was originally extracted from a customer computing system, and any transformation results indicating transformations that were performed on the customer data. The graph is traversed to identify nodes in the graph that have expired based upon data removal policies. The customer data represented by the expired nodes in the graph is deleted and the graph is modified to delete the expired nodes. The modified graph is then stored in persistent memory until data removal is next triggered.

Tracking data lineage and applying data removal to enforce data removal policies

Techniques of keyless authentication of computing services in distributed computing systems are disclosed herein. One example technique includes upon receiving a command to instantiate a computing service, transmitting a request to an authentication service for an identity assertion token corresponding to an application execution of which instantiates the computing service. The example technique can also include upon receiving the requested identity assertion token, storing the received identity assertion token in the container and modifying an entry of a configuration file in the container that allows the instantiated computing service to access the stored identity assertion token and authenticate to the authentication service using the identity assertion token.

Keyless authentication scheme of computing services

A cross-platform deployment computing system receives an application to be deployed to a cloud. The application is parsed to identify assignment criteria and is assigned to an application availability group the defines security that defines a topology in which the application will be deployed. The application is then provided to a control plane for deployment at a target remote server architecture. In order to deploy the application to a different remote server architecture, the control plane for the different remote server architecture accesses the deployment definitions in the AAG and deploys the application to the different remote server architecture using the deployment definitions in the AAG to which the application is assigned.

Cross-platform deployment of application

Server-side control of client-side quality-of-service tracking is provided. When a service is being provided to a client device, a server provides aggregation rules to a client device as part of the service. The client device executes instructions in the aggregation rules to collect QoS metrics regarding the service on the client device and to manipulate the QoS metrics to generate aggregated QoS data. The client device reports the aggregated QoS data to a server for storage and analysis.

Rule-based system for client-side quality-of-service tracking and reporting

Server-side control of client-side quality-of-service tracking is provided. When an event occurs at a client, a measurement client collects event parameters and generates aggregated measurement data in accordance with aggregation rules that are provided to the measurement client from a server-side measurement service. The measurement client periodically reports the aggregated measurement data to the measurement service for storage and analysis.

Embodiments of sampling techniques for server farms are described herein. In an implementation, the sampling techniques may be performed to find a range of values, in which, a quality of service measurement belongs. Each range is thought of as a 'bucket' and may include a corresponding counter that is incremented for each quality of service measurement that falls within the bucket. The countes may then be converted into measurement curves which are published at periodic intervals for examination.

Sampling techniques

Growing community of inventors

San Jose, CA, United States of America

Marc Andrew Power