Microsoft Technology Licensing, LLC

Shadi Abdollahian Noghabi

Ranveer Chandra

Krishna Chintalapudi

Shivkumar Kalyanaraman

Anirudh Badam

Landon Prentice Cox

Behnaz Arzani

Yu Yan

Tsu-wang Hsieh

Peder Andreas Olsen

Srinivasan Iyengar

Riyaz Pishori

Jin Hyun So

A computing system including an edge computing device. The edge computing device may include an edge device processor configured to receive edge device contextual data including computing resource availability data. Based at least in part on the edge device contextual data, the edge device processor may select a processing stage machine learning model of a plurality of processing stage machine learning models and construct a runtime processing pipeline of one or more runtime processing stages including the processing stage machine learning model. The edge device processor may receive a runtime input, and, at the runtime processing pipeline, generate a runtime output based at least in part on the runtime input. The edge device processor may generate runtime pipeline metadata that indicates the one or more runtime processing stages included in the runtime processing pipeline. The edge device processor may output the runtime output and the runtime pipeline metadata.

Constructing processing pipeline at edge computing device

A computing device is provided, including a processor configured to receive imaging relevance data for a geographic area. The processor may be further configured to generate, based at least in part on the imaging relevance data, image mask instructions specifying a region of interest included in the geographic area. The processor may be further configured to transmit the image mask instructions to a satellite. The processor may be further configured to receive, from the satellite, filtered satellite image data of the region of interest. One or more deprioritized regions of the geographic area outside the region of interest may be excluded from the filtered satellite image data.

Mask for satellite image data

A computer system that includes a plurality of compute clusters that are located at different geographical locations. Each compute cluster is powered by a local energy source at a geographical location of that compute cluster. Each local energy source has a pattern of energy supply that is variable over time based on an environmental factor. The computer system further includes a server system that executes a global scheduler that distributes virtual machines that perform compute tasks for server-executed software programs to the plurality of compute clusters of the distributed compute platform. To distribute virtual machines for a target server-executed software program, the global scheduler is configured to select a subset of compute clusters that have different complementary patterns of energy supply such that the subset of compute clusters aggregately provide a target compute resource availability for virtual machines for the target server-executed software program.

Distributed computing with variable energy source availability

A satellite is provided, including an onboard computing device. The onboard computing device may include a processor configured to receive training data while the satellite is in orbit. The processor may be further configured to perform training at a machine learning model based at least in part on the training data. The processor may be further configured to generate model update data that specifies a modification made to the machine learning model during the training. The processor may be further configured to transmit the model update data from the satellite to an additional computing device.

Generating model update data at satellite

A method for data stream prioritization by a session controller is described. Usage data associated with a video communication session is received for one or more client devices of the video communication session. The usage data is based on content within data streams of the video communication session. A first client device of the one or more client devices is identified as having a higher priority level during the video communication session based on the usage data. Instructions are sent to the first client device during the video communication session causing the first client device to improve a quality of a first data stream generated by the first client device for the video communication session.

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Kirkland, WA, United States of America

Shadi Abdollahian Noghabi