Abstract: 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.

Abstract: The described implementations relate to automatically performing device actions. One implementation can obtain a contextual value of a contextor. The implementation can decide, using a decision engine, whether to perform an action on a computing device based on the contextual value. In an instance when the decision engine decides that the action is to be performed, the implementation can perform the action on the computing device. The implementation can also update the decision engine using feedback related to the action. As a specific example, the action can be prelaunching an application before a user has requested to execute the application. Prelaunching the application can reduce application latency relative to waiting for the user to request to execute the application before launching the application.

Abstract: Embodiments provide implementations for estimating a test application's energy usage on a target device based on execution of the test application. Resource usage associated with the execution of the test application is measured. The measured resource usage is input into a power model of the target device to estimate the energy that the test application uses when executed on the target device. An emulation system is configured to execute the test application in a virtual execution environment using resource scaling and simulated target device application programming interfaces to substantially simulate the corresponding capabilities of the target device.

Abstract: 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—a task which it could not otherwise perform due to its native limitations.

Abstract: 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.

Abstract: 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.

Abstract: The subject disclosure is directed towards executing jobs based on resource usage. When a plurality of jobs is received, one or more jobs are mapped to one or more other jobs based on which resources are fully utilized or overloaded. The utilization of these resources by the one or more jobs complements utilization of these resources by the one or more other jobs. The resources are partitioned at one or more servers in order to efficiently execute the one or more jobs and the one or more other jobs. The resources may be partitioned equally or proportionally based on the resource usage or priorities.

Abstract: Magnetic stripe-based transaction enabled mobile communication device embodiments are presented which generally involve a mobile communication device which has been configured to perform transactions that heretofore were completed using a magnetic stripe found on magnetic-stripe cards. In one general embodiment, a mobile communication device generates magnetic stripe data which is used to perform a magnetic stripe-based transaction. To this end, the mobile communication device includes a magnetic stripe device and a computing device. The computing device stores the magnetic stripe data, and the magnetic stripe device is employed to transfer the stored magnetic stripe information so that it can be used to conduct transactions as if a traditional magnetic stripe card were being used.

Abstract: The subject disclosure is directed towards a context-aware mobile crowd sourcing service/system. Context information is automatically collected for a mobile device via mobile-device sensors. When a task is received that specifies context-related criteria, a worker is selected for that task based at least in part upon the context information associated with that worker's mobile device. Sensors on the device may be leveraged to capture information related to performing the task. Also described is a cross-platform task configuration that allows a task to be written once and run on different mobile device platforms.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The claimed subject matter provides systems and/or techniques that facilitate and/or effectuate efficient stream sharing for multi-user sensor data collection. The system includes mechanisms that locate within a query a specification that includes spatial regions over which data is required, based on the spatial regions the mechanism determines which sensors are situated within the region or ascertains which of the sensors in the region is actively streaming data and/or has persisted data in order to fulfill the query, constructs or utilizes a directed acyclic graph to obviate redundancy in a functional requirement included in the specification and imposed by the query, and formulates a result in fulfillment of the query that is delivered to an application.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments include processes, systems, and devices for developing a virtual sensor. The virtual sensor includes one or more inference models. A decision engine utilizes an inference model associated with a mobile device to determine another inference model that is configured to accept physical sensor data from another mobile device. In this way, the virtual sensor can be developed for use with many mobile devices using initial inference models developed for a small number of mobile devices or a single mobile device. Embodiments also include methods to select mobile devices from which to request physical sensor data for virtual sensor input. Embodiments also include architectures that provide a library of virtual sensors.

Abstract: 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.

Abstract: 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.

Abstract: The described implementations relate to predictive computing device energy management. One implementation measures resource usage of a computing device that employs a power policy. This implementation also estimates resource usage of the computing device having at least one different power policy without actually running the at least one different power policy on the computing device.

Abstract: 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.