Microsoft Technology Licensing, LLC

Sameh Elnikety

Yuxiong He

Kathryn S Mckinley

Shaolei Ren

Thomas Moscibroda

James R Larus

Austin N Donnelly

Ricardo Gouvea Bianchini

Marcus Felipe Fontoura

Antony Rowstron

Ke Wang

Dushyanth Narayanan

Yimin Deng

Aditya Bhandari

Eno Thereska

Ricardo Gouvêa Bianchini

Chandrasekhar Pasupuleti

Brian Jacob Corell

Inigo Goiri Presa

Stanko Novakovic

Sherif Sakr

Kapil Arya

Alper Gun

Íñigo Goiri Presa

Chenyu Yan

The present disclosure describes a type of virtual machine, which the present disclosure may refer to as a harvest virtual machine, that may allow improved utilization of physical computing resources on a cloud-computing system. First, the harvest virtual machine may be evictable. In other words, higher priority virtual machines may preempt the harvest virtual machine's access to physical computing resources. Second, the harvest virtual machine may receive access to a dynamic amount of physical computing resources during the course of its operating life. Third, the harvest virtual machine may have a minimum size (in terms of an amount of physical computing resources) and may terminate whenever the harvest virtual machine has access to an amount of physical computing resources less than the minimum size.

Harvest virtual machine for utilizing cloud-computing resources

A processing system is described which assigns jobs to heterogeneous processing modules. The processing system assigns jobs to the processing modules in a manner that attempts to accommodate the service demands of the jobs, but without advance knowledge of the service demands. In one case, the processing system implements the processing modules as computing units that have different physical characteristics. Alternatively, or in addition, the processing system may implement the processing modules as threads that are executed by computing units. Each thread which runs on a computing unit offers a level of performance that depends on a number of other threads that are simultaneously being executed by the same computing unit.

Assigning jobs to heterogeneous processing modules

Systems and methods related to harvesting of the unused resources in a distributed computing system are described. An example method, in a system including a host server, having a first instruction set architecture (ISA), and an interface card, having a second ISA is provided. The method includes designating at least one type of resource, associated with the host server for harvesting by compute entities configured for execution using the processor having the second ISA, where the host server is configured to execute compute entities requiring execution by the processor having the first ISA. The method further includes in response to a request for accessing the at least one type of resource by a compute entity, executing on the processor having the second ISA, automatically allowing the compute entity to access the at least one type of resource associated with the host server.

Harvesting unused resources in a distributed computing system

The subject disclosure is directed towards scheduling requests using quality values that are defined for partial responses to the requests. For each request in a queue, an associated processing time is determined using a system load and/or the quality values. The associated processing time is less than or equal to a service demand, which represents an amount of time to produce a complete response.

Scheduling execution requests to allow partial results

Graph queries are processed using a plurality of independent query execution engines. A graph query submitted to a graph database which is modeled by an attributed graph is received. The graph query is decomposed into a plurality of query components. For each of the query components, a one of the query execution engines that is available to process the query component is identified, a sub-query representing the query component is generated, the sub-query is sent to the identified query execution engine for processing, and results for the sub-query are received from the identified query execution engine. The results received are then combined to generate a response to the graph query.

Graph query processing using plurality of engines

Improved storage systems which use write off-loading are described. When a request to store some data in a particular storage location is received, if the particular storage location is unavailable, the data is stored in an alternative location. In an embodiment, the particular storage location may be unavailable because it is powered down or because it is overloaded. The data stored in the alternative location may be subsequently recovered and written to the particular storage location once it becomes available.

Storage systems using write off-loading

Growing community of inventors

Redmond, WA, United States of America

Sameh Elnikety