Abstract: A system and method configured in an electrical device for providing a monitoring solution that identifies the intermittent or anomalous behavior of electronic/electric appliances when in use, comprising: receiving present real-time power features like current, voltage, power factor, power consumption of the associated device, and maintaining a history of past real-time readings of the device in its ideal working periods; forming rule-based clusters of different internal states of the device from the past history data at ideal working periods; determining which of said present readings are correlated; computing a deviation between at least some of said present and at least some of said clusters; and declaring an anomaly when said present readings deviation exceeds a predetermined threshold from all of the said clusters.

Abstract: Data Center (DC) server power management monitors resource utilization and energy consumption characteristics of an individual host server, and a Virtual Machine (VM) and the applications running inside any VM of DC servers. An analysis and learning module identifies trends and opportunities to optimize DC resources by releasing the underutilized host servers. It derives power metrics to measure the energy footprint of the VMs and the associated applications. It suggests optimal destination servers to migrate each of the VMs with corresponding applications from the underutilized host servers. The power consumption of these VMs with their applications on the power-efficient destination servers is less after the migration. Powering off the underutilized freed-up servers saves energy impacting the overall power consumption of the data center.

Abstract: Data Center (DC) server power management monitors resource utilization and energy consumption characteristics of an individual host server, and a Virtual Machine (VM) and the applications running inside any VM of DC servers. An analysis and learning module identifies trends and opportunities to optimize DC resources by releasing the underutilized host servers. It derives power metrics to measure the energy footprint of the VMs and the associated applications. It suggests optimal destination servers to migrate each of the VMs with corresponding applications from the underutilized host servers. The power consumption of these VMs with their applications on the power-efficient destination servers is less after the migration. Powering off the underutilized freed-up servers saves energy impacting the overall power consumption of the data center.

Abstract: A system and method configured with an electronic device to enable prediction-based power management by providing direct transition to a lower power state such that overall energy consumption is reduced. The system and method includes an idleness information recording module configured to, using a power management agent, non-intrusively observe and record usage and idleness information of the electronic device, a learning module configured to, using a neural network operatively coupled with the power management agent, conduct deep learning of idleness patterns of the electronic device, a prediction module configured to predict future idleness of the electronic device based on the deep learning of the idleness patterns, and a prediction-based lower power state transfer module configured to directly transition the electronic device to lower power state based on the predicted future idleness.

Abstract: A system and method configured with an electronic device to enable prediction-based power management by providing direct transition to a lower power state such that overall energy consumption is reduced. The system and method includes an idleness information recording module configured to, using a power management agent, non-intrusively observe and record usage and idleness information of the electronic device, a learning module configured to, using a neural network operatively coupled with the power management agent, conduct deep learning of idleness patterns of the electronic device, a prediction module configured to predict future idleness of the electronic device based on the deep learning of the idleness patterns, and a prediction-based lower power state transfer module configured to directly transition the electronic device to lower power state based on the predicted future idleness.

Abstract: A system including an intelligent power management device including a plurality of sub-devices, a communication component communicatively connect the device to a communication network, wherein an intelligent power management (IPM) agent is continually run on the device and is configured to save power consumption on the device based on a plurality of power management policies including power management actions for controlling power consumption of a sub-device, wherein the power management policy is received from a remote server, and wherein the IPM agent is configured to adaptively change, using historic usage data of a plurality of users of the intelligent power management device, the power management actions to optimize a power saving on the plurality of sub-devices for each of the plurality of users.

Abstract: A system including an intelligent power management device including a plurality of sub-devices, a communication component communicatively connect the device to a communication network, wherein an intelligent power management (IPM) agent is continually run on the device and is configured to save power consumption on the device based on a plurality of power management policies including power management actions for controlling power consumption of a sub-device, wherein the power management policy is received from a remote server, and wherein the IPM agent is configured to adaptively change, using historic usage data of a plurality of users of the intelligent power management device, the power management actions to optimize a power saving on the plurality of sub-devices for each of the plurality of users.

Abstract: A power management system includes a power management agent and a computing device comprising a CPU, memory, persistent storage, operating system, and communication mechanism. A power management server communicates with the communication mechanism using a secure communication protocol, communicates with the power management agent when the agent is in the connected mode, and provides a non-intrusiveness monitoring function. The power management agent operates in both a connected and disconnected mode, and maintains a list of applications, tasks, and activities and their dependency on power manageable components in the device. An application control framework defines a non-intrusiveness of a device for every application on the device and a usage of the device to allow fine grain control of the device. A management station sends a set of monitoring commands to at least one agent to monitor the intrusiveness of a power management function on the device without enforcing any power management.

Abstract: A power management system includes a power management agent and a computing device comprising a CPU, memory, persistent storage, operating system, and communication mechanism. A power management server communicates with the communication mechanism using a secure communication protocol, communicates with the power management agent when the agent is in the connected mode, and provides a non-intrusiveness monitoring function. The power management agent operates in both a connected and disconnected mode, and maintains a list of applications, tasks, and activities and their dependency on power manageable components in the device. An application control framework defines a non-intrusiveness of a device for every application on the device and a usage of the device to allow fine grain control of the device. A management station sends a set of monitoring commands to at least one agent to monitor the intrusiveness of a power management function on the device without enforcing any power management.