Microsoft Technology Licensing, LLC

Gurpreet Virdi

Jared Adam Brown

Thomas Moscibroda

Yunus Mohammed

Karthikeyan Subramanian

Patrick Evan Little

John Thomas Kimbell

Marianna Levant

Rory Akira Malcolm Fairweather

Lisa Marie Wright

Grigoriy Vyacheslavovich Lyukshin

David Chege Gitau

Arun Upadhyaya Kishan

Murari Sridharan

Shira Weinberg

Emily Nicole Wilson

Steve Seixeiro

Paresh J Maisuria

Shandan Zhou

Si Qin

Eddie Fusaro

Hang Dong

Bo Qiao

Marko Lalic

Yuwen Yang

Mohammed Nashaat Soliman

Lijuan Qin

Robert Michael Howard

Michael Burt Goulding

Richa Prasad

Inigo Goiri Presa

Sorin Iftimie

Eli Cortez Custodio Vilarinho

Sanjay Ramanujan

Reza Ferrydiansyah

Felipe Vieira Frujeri

Yandan Wang

David Jeremy Guy Wood

Lucas Scotta

Rakesh Akkera

Sandeep Kumta Vishnu

Fernando Gonzalez Todisco

Vi Lam Nguyen

Rostislav Sudakov

Xing Wen Wang

The present disclosure relates to systems, methods, and computer readable media for predicting surplus capacity on a set of server nodes and determining a quantity of deferrable virtual machines (VMs) that may be scheduled over an upcoming period of time. This determination of VM quantity may be determined while minimizing risks associated with allocation failures on the set of server nodes. This disclosure described systems that facilitate features and functionality related to improving utilization of surplus resource capacity on a plurality of server nodes by implementing VMs having some flexibility in timing of deployment while also avoiding significant risk caused as a result of over-allocated storage and computing resources. In one or more embodiments, the quantity of deferrable VMs is determined and scheduled in accordance with rules of a scheduling policy.

Allocating computing resources for deferrable virtual machines

Systems and methods are provided for scheduling a virtual machine (VM) to host a workload in a cloud system. In particular, the disclosed technology schedules an evicted VM for redeploying an interruptible workload. The scheduling is based on capacity prediction and inference data associated with a type of the evicted VM. Capacity signal predictor generates training data for training a machine learning model using capacity signal history data of the cloud system. The machine-learning model, once trained, predicts capacity including a rate of evictions for the types of the evicted VM. The predicted data is based on at least the current status of available computing resources. Upon receiving a notice associated with a workload interruption, the intelligent scheduler prioritizes the evicted VM for scheduling and determines whether to defer redeploying the evicted VM based on the rate of eviction for the type of the evicted VM.

Machine-learning-based replenishment of interruptible workloads in cloud environment

A method of operating a device, which includes providing a user interface with a display artifact; in response to an indication a user selected to copy the display artifact, storing multiple levels of detail of the display artifact; allowing the user to paste a selected level of detail by steps of: in response to an indication a user selected an input field and a paste command, displaying a plurality of paste options for pasting the display artifact into the input field, wherein the plurality of paste options each have a unique level of detail for representing the display artifact; allowing the user to select from the plurality of paste options; and in response to an indication a user selected a paste option from the plurality of paste options, pasting the display artifact with a level of detail associated with the selected paste option into the input filed.

Copying user interface artifacts with dynamic levels of detail and shortcut keys

A cloud computing capacity management system can include a fine-grained admission control layer, a policy engine, and an enforcement layer. The fine-grained admission control layer can be configured to ingest capacity signals and create a capacity mitigation policy, based at least in part on the capacity signals, to protect available capacity of a cloud computing system for prioritized users. The capacity mitigation policy can be directed to users of the cloud computing system. The policy engine can be configured to control how the capacity mitigation policy is applied to the cloud computing system. The enforcement layer can be configured to handle incoming resource requests and to enforce resource limits based on the capacity mitigation policy as applied by the policy engine.

Cloud computing capacity management system using automated fine-grained admission control

Pooling computing resources based on inferences about a plurality of hardware devices. The method includes identifying inference information about the plurality of devices. The method further includes based on the inference information optimizing resource usage of the plurality of hardware devices.

User presence prediction driven device management

A routing system is described herein for intelligently routing actions directed to any one of a collection of user devices that are associated with a user. In operation, the routing system receives a request from a source entity to perform an action. The routing system determines a mode of carrying out the action that involves use of one or more user devices, selected from the collection of user devices. It then instructs the selected user device(s) to carry out the action. In this manner of operation, requests that are directed to an individual user device are no longer necessarily carried out by that individual user device. The routing system chooses the user device(s) to carry out the action based on current context information together with relationship information extracted from a user graph.

Routing actions to user devices based on a user graph

A digital assistant supported on a local device and/or a remote digital assistant service is disclosed herein. The device may interact with a user and perform various functions or tasks based on the user's input (e.g., web search, set calendar appointments, capture images or video). The digital assistant may identify and store artifacts associated with each action or function. Artifacts may include, for example, people in an image, search results from a web search, participants in a meeting, etc. Subsequently, the user may query the digital assistant for information about one of the artifacts, such as who the participants were in a meeting conducted the prior Thursday. The digital assistant can sift through the various artifacts and provide the user with the relevant information. Additionally, the digital assistant can suggest actions for the user based on the retrieved artifacts, such as to message one of the identified participants.

Task creation and completion with bi-directional user interactions

Computing resource management of computing devices

A digital assistant supported on computing devices such as smartphones, tablet computers, personal computers (PCs), wearable devices, game consoles, smart home devices and the like is configured with a notification system that interfaces with applications and/or other systems and resources so that the digital assistant may collect notifications across a range of a user's devices. The digital assistant may utilize a natural language interface (which may cooperate with touch and gesture interfaces) and applies awareness of context and user's behaviors to intelligently present the collected notifications on a single device for the user as a native digital assistant user experience. The digital assistant can sort notifications, group notifications together, and interact with the user to answer questions, provide information and status, and enable the user to action on a notification from the collection.

Interaction with notifications across devices with a digital assistant

Growing community of inventors

Redmond, WA, United States of America

Gurpreet Virdi