Microsoft Technology Licensing, LLC

Aman Kansal

Jie Jerry Liu

Feng Zhao

Suman Kumar Nath

Eric J Horvitz

Daniel Morris

Sriram Govindan

Michel Goraczko

Alice Jane Brush

Julia Maureen Letchner

Scott Saponas

Paramvir Bahl

Douglas Christopher Burger

David Chiyuan Chu

Todd Douglas Mytkowicz

Sean McGrane

Deepak Ganesan

Arka Aloke Bhattacharya

Kathryn Stuart McKinley

Harold Lim

Mohamed Musthag

Rainer Andreas Krause

Ranveer Chandra

John Charles Krumm

Alastair Wolman

Eric Clarence Peterson

Navendu Jain

Sean Michael James

Nissanka Arachchige Bodhi Priyantha

Shaun L Harris

Sachin C Patel

Andrew Michael Rogers

Dimitrios Lymberopoulos

John Joseph Siegler

Romit Roy Choudhury

Brian David Cross

Piali Choudhury

Dany Rouhana

Xuan Bao

Nupur Kothari

Ranier Andreas Krause

Gerardo Garcia

Ming-Wei Chang

Tingxin Yan

Weiwei Xiong

Arsalan Tavakoli-Shiraji

Nissanka Arachchige B Priyantha

Jie Liu

Various technologies described herein pertain to detection of an opportune time period to deliver a notification. Responsive to receipt of the notification (e.g., at a user device), analysis of an attention state of a user can be initialized. Further, the opportune time period to deliver the notification can be detected based on the analysis of the attention state of the user. The opportune time period can be during a breakpoint or an influential context. The breakpoint is when the user has switched between tasks and lacks engagement with the tasks. The influential context is a particular context in which the user is available to attend to the notification. Moreover, the notification can be delivered during the opportune time period.

User interruptibility aware notifications

Portable devices include environmental sensors that generate metrics about the environment (e.g., accelerometers detecting impulses and vibration, and GPS receivers detecting position and velocity). Such devices often use environmental metrics to extract user input directed at the device by the user, and status information about the device and the environment. Presented herein are techniques for using environmental metrics to infer physical activities performed by the user while attached to the device. For example, jogging may be inferred from regular, strong impulses and typical jogging speed; walking may be inferred from regular, weak impulses and typical walking speed; and riding in a vehicle may be inferred from low-level vibrations and high speed (optionally identifying the type of vehicle ridden by the user). Based on these inferences, the device may automatically present applications and/or or adjust user interfaces suitable for the user's physical activity, rather than responsive to user input.

Physical activity inference from environmental metrics

Disaster recovery is provided for an application that is being hosted on a current data center, thus ensuring the availability of the application. An option for replicating session state data for the application is selected. This selection is made from a set of different session state data replication options each of which has different performance and resource cost trade-offs. The selected option determines how the session state data for the application is to be replicated. The selected option is implemented, where the implementation results in the session state data for the application being replicated outside of the current data center, thus ensuring that this data remains available in the event that the current data center goes offline.

Geo-distributed disaster recovery for interactive cloud applications

Architecture that facilitates the estimation of interference among workloads (e.g., virtual machines) due to sharing of a shared resource (e.g., a shared cache of a computer processor), and optimization of a desired performance objective such as power or energy use in the presence of the interference. Estimation is to the extent of interference by characterizing the nature of shared resource usage and its effect on performance. Performance optimization is accomplished using metrics based on the above estimation, or alternatively, an explicit measurement of the interference effects. Methods are employed to estimate interference on the workload's performance with changes in availability of the shared resource or with combinations of other workloads sharing the same resource and allocating workloads to one or more physical computers or resources to workloads such that a desired performance objective is optimized. The methods can include allocating workloads on demand.

Workload interference estimation and performance optimization

A power supply is described herein which provides power to a load, such as a load including one or more computing devices. The power supply uses a slow-response power source (such as a fuel-driven mechanism) to handle a slow-moving component of the demand level presented by the load, and uses a fast-response power source (such as a battery or a capacitor, etc.) to handle a fast-moving component of the demand level. By virtue of this approach, the power supply can manage the load level as it appears to the slow-response power source, allowing, in turn, the slow-response power source to service even fast-changing loads&#x2014;a task which it could not otherwise perform due to its native limitations.

Power supply for use with a slow-response power source

A description of computing resource requirements for execution of an application associated with a publicly available service is obtained. Access to computing resources is opportunistically obtained from a computing entity that includes a private computing device that is external to, and separate from, the publicly available service. The computing resource requirements are intelligently matched to available computing resources of the computing entity with private computing resources that are temporarily available from a private computing device source. The intelligent matching is performed using an optimization analysis.

Opportunistically connecting private computational resources to external services

In a device, one or more low energy sensors are used to determine a mobility state of the device. Based on both the mobility state of the device and one or more places of interest for the device, a determination is made as to when to use a high energy sensor to determine a location of the device. One of the one or more places of interest for the device within which the device is located at any particular time can be determined based on the mobility state of the device and/or the location of the device as determined by the high energy sensor.

Energy efficient location detection

An implementation of location estimation using image analysis is described. In this implementation, an image of a place is obtained and matched with previously stored images. The matching may be achieved by employing methods based on key feature extraction algorithm, color histogram analysis, pattern matching or other image comparison techniques. Upon determining a match, the location information associated with the image provides the location. The location information may be in the form of location tags or location keywords and the location information may be used by the user or other applications for the purposes of location determination. The technique allows for the user to enter location information. The location information may be assigned to the previously stored images residing in local and remote databases for users and applications to assign information or keywords to images.

Location estimation using image analysis

A method disclosed herein includes the act of computing a value of information for obtaining data from a personal sensor of a user for utilization in a utilitarian computing application, wherein a mobile computing device comprises the personal sensor of the user. The method further includes the act of requesting that the mobile computing device transmit a data packet to the computing device based at least in part upon the value of information for obtaining data from the personal sensor of the user.

Selection of sensors for monitoring phenomena considering the value of information and data sharing preferences

Embodiments of the virtual machine power metering system and method measure the power consumption of individual virtual machines. Power meter measurements for a physical host server are converted into individual virtual machine power meters that measure the power consumption of each individual virtual machine residing on the host server. The virtual machine power consumption is computed by generating a power model using the total power consumption of the host server and resource utilization for a virtual machine. Optimal power model coefficients are computed using the power model. The energy used by the virtual machine is computed using one of two embodiments. Embodiments of the system and method also can be used to obtain the power consumption for a specific activity (such as a service, request, or search query). In addition, the virtual machine power metering can be used for virtual machine power capping to allow power oversubscription in virtualized environments.

Virtual machine power consumption measurement and management

The subject disclosure is directed towards automatically labeling location-related information such as corresponding to GPS data or the like with a semantic label. A classifier trained with machine learning is provided with feature data corresponding to the location-related information and other features, such as user demographics data of a person associated with location-related information. The semantic label is received from the classifier, and associated with the location-related information. Other features may be used, such as other egocentric features corresponding to a person's particular visit, features from a sequence of visits, and/or features from other user information. The semantic label may be used to trigger an action, label a location on a map or the like, and so on.

Semantic place labels

Virtualized application power budgeting can manage power budgeting for multiple applications in data centers. This power budgeting may be done in intelligent and/or dynamic ways and may be useful for updating power budgets, resolving conflicts in requests for power, and may improve the efficiency of the distribution of power to multiple applications. Virtualized application power budgeting can distinguish between priority applications and non-priority applications at a granular, virtual machine level and reduce the power consumption to only non-priority applications when there are power consumption conflicts. Virtualized application power budgeting may be able to determine the most efficient manner of providing power to each application in a data center. Further, virtualized application power budgeting may be able to distribute power according to application priority and other predetermined requirements and improve the efficiency of the power consumption by the devices in the data center.

Virtualized application power budgeting

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Issaquah, WA, United States of America

Aman Kansal