Abstract: The technology described herein identifies and mitigates phishing attempts by analyzing user input received at the operating system level. Initially, a credential, such as a username or password, is registered with the threat detection system. The technology described herein intercepts user input at the operating system level, generates a hash of the input, and compares it with a hash of a credential being monitored. A credential entry is detected when a hash for the character string entered matches a hash for a credential being monitored. The technology described herein will perform a threat assessment when a secret entry is detected. The threat assessment may use the application context and the network context as inputs to the assessment. Various mitigation actions may be taken when a threat is detected.

Abstract: A computing device has an energy storage device system with multiple energy storage devices. Various different criteria are used to determine which one or more of the multiple energy storage devices to charge or discharge at any given time to provide power to the computing device. The criteria can include characteristics of the energy storage devices as well as hardware and/or physical characteristics of the computing device, characteristics of the energy storage devices and/or the computing device that change while the computing device operates, and predicted behavior or usage of the computing device. These criteria are evaluated during operation of the computing device, and the appropriate energy storage device(s) from which to draw power or to charge at any given time based on these criteria are determined.

Abstract: Pooling computing resources based on inferences about a plurality of hardware devices. The method includes identifying inference information about the plurality of devices. The method further includes based on the inference information optimizing resource usage of the plurality of hardware devices.

Abstract: A computing device has an energy storage device system with one or more energy storage devices. A target run-time is obtained, which refers to how long the computing device is to run given the current amount of energy in the energy storage device(s). A predicted power usage over the target run-time is determined, and what, if any, power management actions to take in order to achieve the target run-time are determined. The power management actions are then taken. A target charge-time is also obtained, which refers to how long the computing device is to take to charge the energy storage device(s) to a threshold level (e.g., 100% or fully charged). A predicted power gain over the target charge-time is determined, and what, if any, power management actions to take in order to achieve the target charge-time are determined. The power management actions are then taken.

Abstract: Pooling computing resources based on inferences about a plurality of hardware devices. The method includes identifying inference information about the plurality of devices. The method further includes based on the inference information optimizing resource usage of the plurality of hardware devices.

Abstract: Deploying containers constrained by power profiles on a host system. A method includes identifying a container template, a set of reusable stored characteristics, to be used for deploying a container instance. The method further includes obtaining a power profile, defining at least one power consumption threshold, for the container instance based on at least one of the set of reusable stored characteristics of the container template or other information about the container instance. The method further includes deploying the container instance on the host system by applying the set of reusable stored characteristics and the power profile by applying one or more configuration layers which causes power to the container instance to be at least one of regulated or monitored based on information in the power profile.

Abstract: Pooling computing resources based on inferences about a plurality of hardware devices. The method includes identifying inference information about the plurality of devices. The method further includes based on the inference information optimizing resource usage of the plurality of hardware devices.

Abstract: A computing device has an energy storage device system with multiple energy storage devices. Various different criteria are used to determine which one or more of the multiple energy storage devices to charge or discharge at any given time to provide power to the computing device. The criteria can include characteristics of the energy storage devices as well as hardware and/or physical characteristics of the computing device, characteristics of the energy storage devices and/or the computing device that change while the computing device operates, and predicted behavior or usage of the computing device. These criteria are evaluated during operation of the computing device, and the appropriate energy storage device(s) from which to draw power or to charge at any given time based on these criteria are determined.

Abstract: A computing device has an energy storage device system with multiple energy storage devices. Various different criteria are used to determine which one or more of the multiple energy storage devices to discharge at any given time to provide power to the computing device. The criteria can include characteristics of the energy storage devices as well as hardware and/or physical characteristics of the computing device, characteristics of the energy storage devices and/or the computing device that change while the computing device operates, and predicted behavior or usage of the computing device. These criteria are evaluated during operation of the computing device, and the appropriate energy storage device(s) from which to draw power at any given time based on these criteria are determined.

Abstract: A computing device has an energy storage device system with multiple energy storage devices. Various different criteria are used to determine which one or more of the multiple energy storage devices to discharge at any given time to provide power to the computing device. The criteria can include characteristics of the energy storage devices as well as hardware and/or physical characteristics of the computing device, characteristics of the energy storage devices and/or the computing device that change while the computing device operates, and predicted behavior or usage of the computing device. These criteria are evaluated during operation of the computing device, and the appropriate energy storage device(s) from which to draw power at any given time based on these criteria are determined.

Abstract: Consumption of power by device components is profiled on a per-session basis of user interaction. A session engine determines start of a user interaction session with the device (e.g., by detecting screen turn on). The engine generates a session identifier (SID) and broadcasts same to device component(s). In response to the SID, the component(s) record power consumption data. Upon receiving a signal indicating end of the user interaction session (e.g., screen turn off), the session engine broadcasts a notice allowing component(s) to stop recording power consumption data. The components communicate aggregated data to the session engine for storage in a centralized location (e.g., trace buffer). In response to a query posed within a command line prompt, stored data may be parsed on a per-session basis for inclusion in a detailed report of power consumption. Power consumption inefficiencies endemic to components and/or user behaviors may be thus be identified and remediated.

Abstract: Hibernating a computing system. The method includes detecting at least one condition which indicates the availability of time to perform hibernation optimization steps on the computing system. The hibernation optimization steps optimize resume time from hibernate. The method further includes detecting a trigger indicating that the computing system is to be put into a hibernate state. As a result of detecting the at least one condition and the trigger, the method further includes performing hibernation steps to hibernate the computing system in a way that optimizes resume time from hibernate of the computing system.

Abstract: Deploying containers constrained by power profiles on a host system. A method includes identifying a container template, a set of reusable stored characteristics, to be used for deploying a container instance. The method further includes obtaining a power profile, defining at least one power consumption threshold, for the container instance based on at least one of the set of reusable stored characteristics of the container template or other information about the container instance. The method further includes deploying the container instance on the host system by applying the set of reusable stored characteristics and the power profile by applying one or more configuration layers which causes power to the container instance to be at least one of regulated or monitored based on information in the power profile.

Abstract: An operating system of a computing device determines an importance of the threads running on the computing device, such as assigning the importance of the threads as critical or non-critical. The operating system determines when there are no threads having at least a threshold importance (e.g., no critical threads), and forces one or more components of the computing device into a forced idle state in response to determining that there are no threads having at least the threshold importance. The forced idle state of a device component is a low power state, such as a state in which program instructions are not executed, so the computing device is forced into a forced idle state that reduces power usage in the computing device despite there being threads (e.g., non-critical threads) to be executed.

Abstract: Adaptive doze to hibernate scheme techniques are described for power management of a computing device. Rather than relying upon a fixed timer to control device power states, the adaptive doze to hibernate scheme monitors various hibernate parameters and adapts the hibernation experience in dependence upon the parameters. The hibernate parameters may include but are not limited to a standby budget, minimum standby time, reserve screen on time, and indications of user presence. In operation, a power manager monitors battery drain rate and adaptively determines when to change the device power states of the computing device based on the observed drain rate and the hibernate parameters. The power manager may selectively switch between various states (e.g., high performance, active, wake, standby, hibernate, off, etc.) accordingly.

Abstract: A computing device has an energy storage device system with one or more energy storage devices. A target run-time is obtained, which refers to how long the computing device is to run given the current amount of energy in the energy storage device(s). A predicted power usage over the target run-time is determined, and what, if any, power management actions to take in order to achieve the target run-time are determined. The power management actions are then taken. A target charge-time is also obtained, which refers to how long the computing device is to take to charge the energy storage device(s) to a threshold level (e.g., 100% or fully charged). A predicted power gain over the target charge-time is determined, and what, if any, power management actions to take in order to achieve the target charge-time are determined. The power management actions are then taken.

Abstract: Consumption of power by device components is profiled on a per-session basis of user interaction. A session engine determines start of a user interaction session with the device (e.g., by detecting screen turn on). The engine generates a session identifier (SID) and broadcasts same to device component(s). In response to the SID, the component(s) record power consumption data. Upon receiving a signal indicating end of the user interaction session (e.g., screen turn off), the session engine broadcasts a notice allowing component(s) to stop recording power consumption data. The components communicate aggregated data to the session engine for storage in a centralized location (e.g., trace buffer). In response to a query posed within a command line prompt, stored data may be parsed on a per-session basis for inclusion in a detailed report of power consumption. Power consumption inefficiencies endemic to components and/or user behaviors may be thus be identified and remediated.

Abstract: When an activity agent desires to perform a particular task on a device, the activity agent communicates a request to a resource control system of the device. The request has an associated amount of energy that is expected to be used by the activity agent to perform the task. The resource control system receives the request, determines whether to grant the request based on the amount of energy expected to be used by the activity agent to carry out the task and various additional factors, and returns an indication to the activity agent that the request is granted or denied. If denied, the activity agent delays performing its desired task. If granted, the activity agent proceeds to perform its desired task. The resource control system also continues to monitor the system state of the device, and may revoke the grant depending on changes in the system state of the device.

Abstract: Moving a computing system to a mandated power state. The method includes a computing system component determining to move the computing system to a deeper power state. As a result, the method further includes the computing system component directing hardware and software agents on the computing system to move to a deeper power state. The method further includes the computing system component observing that at least one agent is preventing the computing system from moving to the deeper power state. As a result, the method includes the computing system component directing a system-wide movement to a mandated power state.

Abstract: Hibernating a computing system. The method includes detecting at least one condition which indicates the availability of time to perform hibernation optimization steps on the computing system. The hibernation optimization steps optimize resume time from hibernate. The method further includes detecting a trigger indicating that the computing system is to be put into a hibernate state. As a result of detecting the at least one condition and the trigger, the method further includes performing hibernation steps to hibernate the computing system in a way that optimizes resume time from hibernate of the computing system.