Microsoft Technology Licensing, LLC

Yang Yang Zhang

Bradley Gene Calder

Ju Wang

Sriram Sankaran

Hemal Khatri

Pradeep Kumar Gunda

Marvin McNett, Ii

Shyam Antony

Kavitha Manivannan

Vaman Bedekar

Hugh Evan Williams

Sanaz Ahari

Nitin Agrawal

Tabreez Govani

Michael Maxwell Cameron

Jamie Paul Buckley

Srinath Reddy Aaleti

Liwei Chen

Matthias Adam Leibmann

Arild Einar Skjolsvold

Grigory V Kaplin

Marc Andrew Power

Paranthaman Saravanan

Yi Zeng

Lanlan Cong

Vanam Bedekar

Renqi Zhu

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

Systems and methods are provided for assigning and associating resources in a cloud computing environment. Virtual machines in the cloud computing environment can be assigned or associated with pools corresponding to users as dedicated, standby, or preemptible machines. The various states provide users with the ability to reserve a desired level of resources while also allowing the operator of the cloud computing environment to increase resource utilization.

Assignment of resources in virtual machine pools

Methods, computer-storage media, and graphical user interfaces are provided for identifying and presenting rich related sites for task-oriented search queries. Upon receipt of a search query input by a user, one or more query logs are analyzed to determine if the search query is a related to a task being performed by the user. If the query is determined to be a task-oriented search query, search results are identified, as is one or more Uniform Resource Locators (URLs) related to a particular search result. The related URL is presented to the user in association with the particular search result. Additional controls, e.g., search tools that facilitate querying of those URLs determined to be relevant to a particular search result, may also be provided to aid the user in performing the task at hand.

Related URLs for task-oriented query results

In various embodiments, systems and methods are presented for providing resources by way of a platform as a service in a distributed computing environment to perform a job. The system may be comprised of a number of components, such as a task machine, a task location service machine, and a high-level location service machines that in combination are useable to accomplish functions provided herein. It is contemplated that the system performs methods for providing resources by determining resources of the system, such as virtual machines, and applying auto-scaling rules to the system to scale those resources. Based on the determination of the auto-scaling rules, the resources may be allocated to achieve a desired result.

Auto-scaling of pool of virtual machines based on auto-scaling rules of user associated with the pool

Systems and methods are presented for providing resources by way of a platform as a service in a distributed computing environment to perform a job. Resources of the system, job performing on the system, and schedulers of the jobs performing on the system are decoupled in a manner that allows a job to easily migrate among resources. It is contemplated that the migration of jobs from a first pool of resource to a second pool of resource is performed by the system without human intervention. The migration of a job may utilize different schedulers for the different resources. Further, it is contemplated that a pool of resources may automatically allocate additional or fewer resources in response to a migration of a job.

Migration of task to different pool of resources based on task retry count during task lease

Power consumption and computational availability of a cluster are automatically managed by monitoring consumption, predicting demand based on a job queue and/or historic data, and checking certain conditions and constraints. Threshold conditions for switching the cluster's capacity level are based on the current level and the predicted demand. Switching levels is avoided when a stability constraint would be violated. Cluster capacity levels can be uniformly or nonuniformly spaced, and may have decreasing gaps between levels as capacity decreases. Capacity level definitions and level switching conditions and constraints may be defaults and/or be provided by an administrator.

Cluster computational capacity level switching based on demand prediction and stability constraint and power consumption management

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Bellevue, WA, United States of America

Yang Yang Zhang