Abstract: Cloud computing techniques utilizing distributed application execution are disclosed herein. One example technique includes receiving a command to launch an application, and in response, determining an execution location corresponding to a type of data consumed by individual components of the application. Upon determining that one of the components is to be executed in a local computing facility, the example technique includes transmitting, from a public computing facility to the local computing facility, a request to execute the one of the components in the local computing facility instead of the public computing facility. Upon being authorized by the local computing facility, data is requested and received from the one of the components executed at the local computing facility without having direct access from the public computing facility to a data source at the local computing facility.

Abstract: Cloud computing techniques utilizing distributed application execution are disclosed herein. One example technique includes receiving a command to launch an application, and in response, determining an execution location corresponding to a type of data consumed by individual components of the application. Upon determining that one of the components is to be executed in a local computing facility, the example technique includes transmitting, from a public computing facility to the local computing facility, a request to execute the one of the components in the local computing facility instead of the public computing facility. Upon being authorized by the local computing facility, data is requested and received from the one of the components executed at the local computing facility without having direct access from the public computing facility to a data source at the local computing facility.

Abstract: Cloud computing techniques utilizing distributed application execution are disclosed herein. One example technique includes receiving a command to launch an application, and in response, determining an execution location corresponding to a type of data consumed by individual components of the application. Upon determining that one of the components is to be executed in a local computing facility, the example technique includes transmitting, from a public computing facility to the local computing facility, a request to execute the one of the components in the local computing facility instead of the public computing facility. Upon being authorized by the local computing facility, data is requested and received from the one of the components executed at the local computing facility without having direct access from the public computing facility to a data source at the local computing facility.

Abstract: Cloud computing techniques utilizing distributed application execution are disclosed herein. One example technique includes receiving a command to launch an application, and in response, determining an execution location corresponding to a type of data consumed by individual components of the application. Upon determining that one of the components is to be executed in a local computing facility, the example technique includes transmitting, from a cloud computing facility to the local computing facility, a request to execute the one of the components in the local computing facility instead of the cloud computing facility. Upon being authorized by the local computing facility, data is requested and received from the one of the components executed at the local computing facility without having direct access from the cloud computing facility to a data source at the local computing facility.

Abstract: Cloud computing techniques utilizing distributed application execution are disclosed herein. One example technique includes receiving a command to launch an application, and in response, determining an execution location corresponding to a type of data consumed by individual components of the application. Upon determining that one of the components is to be executed in a local computing facility, the example technique includes transmitting, from a cloud computing facility to the local computing facility, a request to execute the one of the components in the local computing facility instead of the cloud computing facility. Upon being authorized by the local computing facility, data is requested and received from the one of the components executed at the local computing facility without having direct access from the cloud computing facility to a data source at the local computing facility.

Abstract: User intentions can be derived from observations of user actions or they can be programmatically specified by an application or component that is performing an action. The intentions can then be utilized to adjust the operation of resource managers to better suit the actions being performed by the user or application, especially if such actions are not “typical”. Resource managers can inform a centralized intention manager of environmental constraints, including constraints on the resources they manage and constraints on their operation, such as various, pre-programmed independent modes of operation optimized for differencing circumstances. The intention manager can then instruct the resource managers in accordance with these environmental constraints when the intention manager is made aware of the intentions. If no further optimization can be achieved, specified intentions may not result in directives from the intention manager to the resource managers.

Abstract: This document describes techniques and apparatuses enabling context-based ratings and recommendations for media. The techniques and apparatuses can build and continually improve the predictive accuracy of a user's reaction history based on a context in which the user's reactions to media are sensed. Further, the techniques and apparatuses may take into account a current context of a user when a request for a rating or recommendation is made. Based on the user's reaction history and the user's current context, the techniques and apparatuses may provide accurate ratings and recommendations.

Abstract: User intentions can be derived from observations of user actions or they can be programmatically specified by an application or component that is performing an action. The intentions can then be utilized to adjust the operation of resource managers to better suit the actions being performed by the user or application, especially if such actions are not “typical”. Resource managers can inform a centralized intention manager of environmental constraints, including constraints on the resources they manage and constraints on their operation, such as various, pre-programmed independent modes of operation optimized for differencing circumstances. The intention manager can then instruct the resource managers in accordance with these environmental constraints when the intention manager is made aware of the intentions. If no further optimization can be achieved, specified intentions may not result in directives from the intention manager to the resource managers.

Abstract: Task schedulers endeavor to share computing resources, such as the CPU, among many threads. However, the task scheduler may be unable to identify the resources that will be utilized by a thread, and may allocate resources inefficiently due to incorrect predictions of resource utility. Task scheduling may be improved by identifying the rate determining factors for various thread tasks comprising a thread, e.g., a first task that is rate-limited by a communications bus, a second task that is rate-limited by the CPU, and a third task that is rate-limited by a communications network. If the instructions are so identified, the operating system may be able to schedule tasks and to allocate resources based on the resources to be utilized by the threads, which may improve efficiency and computing performance.

Abstract: Managing applications associated with an operating system. An application object is defined as a primitive of the operating system. An identity property of the application object identifies at least one application associated with the operating system and a metadata property of the application object declares behavioral characteristics of the identified application. An application state representative of a dynamic state of the identified application defines semantics of an interaction between the application object and the application state.