Abstract:
In some implementations, a mobile device can be configured to monitor environmental, system and user events associated with the mobile device and/or a peer device. The occurrence of one or more events can trigger adjustments to system settings. The mobile device can be configured to keep frequently invoked applications up to date based on a forecast of predicted invocations by the user. In some implementations, the mobile device can receive push notifications associated with applications that indicate that new content is available for the applications to download. The mobile device can launch the applications associated with the push notifications in the background and download the new content. Before running an application or communicating with a peer device, the mobile device can be configured to check energy and data budgets and environmental conditions of the mobile device and/or a peer device to ensure a high quality user experience.
Abstract:
In some examples, in a supply-and-demand system, e.g., a cloud computing environment or an electrical grid, a coordinator may collect resource consumption data from one or more consuming entities. Based on the collected resource consumption data, the coordinator may be configured to predict resource consumption requirement of each consuming entity in a subsequent time period. Further, in accordance with the prediction, the coordinator may allocate the resources to the consuming entities or recycle the resources currently consumed by the consuming entities.
Abstract:
A method and system configured to cap utilisation of a resource to a defined global target value in a decentralised manner. Groups, 101 - 104, of resource consumers 10 are formed and a local utilisation cap value is determined for each group based on the global target value. A local controller, 101C - 104C, in each group controls access to the resource for each member such that the local utilisation cap value is not exceeded, and hence enforcing that the resources consumed by all the resource consumers 10 in respective group do not exceed the defined global target value.
Abstract:
An apparatus 2 for processing data includes first execution circuitry 4, such as an out-of-order processor, and second execution circuitry 6, such as an in-order processor. The first execution circuitry 4 is of higher performance but uses more energy than the second execution circuitry 6. Control circuitry 24 switches between the first execution circuitry 4 being active and the second execution circuitry 6 being active. The control circuitry includes prediction circuitry which is configured to predict a predicted identity of a next sequence of program instructions to be executed in dependence upon a most recently executed sequence of program instructions and then in dependence upon this predicted identity to predict a predicted execution target corresponding to whether the next sequence of program instructions should be executed by the first execution circuitry or the second execution circuitry.
Abstract:
A method for power throttling upon a system includes: obtaining at least one characteristic information of a power source that is used for providing energy for the system; and, determining an available power range for the system according to the at least one characteristic information, so as to make the system control a behavior of the system according to the available power range.
Abstract:
Methods and apparatus relating to optimizing boot-time peak power consumption for server and/or rack systems are described. In an embodiment, a module execution sequence for a computing device is determined to indicate a sequence of module execution during a boot process of the computing device. The module execution sequence is determined based at least partially on power consumption data and timeline data for each module of the computing device during the boot process of the computing device. Other embodiments are also claimed and described.
Abstract:
Technologies are generally provided to switch virtual machines based on processor power states. In some examples, a virtual machine manager (VMM) may determine that a processor configured to execute a first virtual machine (VM) is to execute a VM switch, and cause the processor to enter a low-power state and store a first VM state. The VMM, which may be a VM itself, may then replace the stored first VM state with a second VM state and cause the processor to exit the low-power state. When the processor exits the low-power state, it may load the second VM state and execute a second VM.