Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

John Lawrence Miller

Edward James Gayles

Hrvoje Benko

Samuel Scott Gigliotti

Abraham Martin Passaglia

Zaka Ur Rehman Ashraf

Rajasimman Madhivanan

Brett Nathan Lynnes

Ruoruo Zhang

Nadir Kiyanclar

David R Perek

Christopher Harry Stoumbos

Esaias Engelbertus Greeff

Peter John Ansell

Robert W Young

Oyvind Haehre

Thomas Moscibroda

Shih-Chun Chang

Andrew Jay Roths

Menashe Haskin

Yong Xu

Aleksandar Uzelac

Weidong Zhao

Qingwei Lin

Robert F Day

James David Meyers

David Alan Stevens

Karthikeyan Subramanian

Shandan Zhou

Daniel Christopher Bay

Si Qin

Eli Cortez

Mark Wodrich

Vlad Troyanker

Timothy David Corrie, Jr

Shanti Kemburu

Sivaramakichenane Somasegar

Christopher Cowdery

Kenneth S Gregg

Describe herein are techniques for providing cloud-based discovery. For example, a device may be configured to provide device registration and de-registration notifications to a cloud-based discovery service. The cloud-based discovery service may be configured to respond to discovery request by identifying registered devices that meet the criteria of the discovery request. The cloud-based discovery service may also be configured to provide endpoint information associated with registered devices in response to the discovery request, such that a device is able to utilize the endpoint information to connect with one or more of the registered devices.

Cloud-based device discovery

The present disclosure relates to systems, methods, and computer readable media for predicting deployment growth on one or more node clusters and selectively permitting deployment requests on a per cluster basis. For example, systems disclosed herein may apply tenant growth prediction system trained to output a deployment growth classification indicative of a predicted growth of deployments on a node cluster. The system disclosed herein may further utilize the deployment growth classification to determine whether a deployment request may be permitted while maintaining a sufficiently sized capacity buffer to avoid deployment failures for existing deployments previously implemented on the node cluster. By selectively permitting or denying deployments based on a variety of factors, the systems described herein can more efficiently utilize cluster resources on a per-cluster basis without causing a significant increase in deployment failures for existing customers.

Allocating cloud resources in accordance with predicted deployment growth

Predicting capacity of shared virtual machine (VM) resources by generating estimated remaining capacities (ERCs) for computing clusters within a virtualization system rather than continuously monitoring actual remaining capacities (ARCs). Generating ERCs for a virtualization system's computing cluster(s) by using physical resource allocation flux data and/or VM type exchange probabilities provides benefits over continuously monitoring ARCs. The physical resource allocation flux data may correspond to commissioning and decommissioning VMs into the cluster and may be obtained during a blind period when current ARCs are unknown. For example, the physical resource allocation flux may be an indication of how many instances of each VM type are commissioned and/or decommissioned from the cluster over a time interval of interest. The VM type exchange probabilities may indicate a likelihood that commissioning or decommissioning a VM type into the computing cluster(s) will take up or free up computing resources available to other VM types, respectively.

Predicting capacity of shared virtual machine resources

An input device has both a touch sensor and a position sensor. A computer using data from the input device uses the relative motion of a contact on a touch sensor with respect to motion from a position detector to disambiguate intentional from incidental motion. The input device provides synchronized position sensor and touch sensor data to the computer to permit processing the relative motion and performing other computations on both position sensor and touch sensor data. The input device can encode the magnitude and direction of motion of the position sensor and combines it with the touch sensor data from the same time frame, and output the synchronized data to the computer.

Disambiguating intentional and incidental contact and motion in multi-touch pointing devices

In embodiments of prediction-based touch contact tracking, touch input sensor data is recognized as a series of components of a contact on a touch-screen display. A first component of the contact can be identified, and a second component can be determined to correlate to the contact. The first component and the second component can then be associated to represent a tracking of the contact. Subsequent components of the contact can be determined and associated with the previous components of the contact to further represent the tracking of the contact.

Prediction-based touch contact tracking

Described herein are systems and methods for secure configuration provisioning of network credentials to configure a device to join one or more networks. One implementation provides for distribution of network credentials to associated devices without user intervention while maintaining security and avoiding distribution of the network credentials to external devices, such as a third-party server. Devices may be associated by purchase from a common merchant, registration to a common account, and so forth.

Secure device configuration

The present invention provides a method and system for performing parametric testing of a functional programming interface. Parametric testing of a function verifies that the function performs as expected when a valid or an invalid parameter is passed to the function. To perform parameter testing on a function, the present invention receives as input prototype information for the function, and then formulates a testing plan. The invention tests the function according to the testing plan. The testing plan specifies a list of invalid and valid values for each parameter of the function. The invention repeatedly invokes the function, each time passing the function various combinations of invalid and valid values. The function passes the test when (1) the function does not return an error code for any combination of valid parameter values and (2) the function returns an error for any combination of parameter values that include an invalid parameter value.

Method and system for performing parametric testing of a functional

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Redmond, WA, United States of America

John Lawrence Miller