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 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: A peak power management system for networked smart IT devices. These smart devices have computing capability with at least one CPU and memory and can be networked. An uninterruptible power supply provides power to the smart devices. A central intelligent power management server controls the power consumed by all the smart devices networked with the server. The system uses priority based peak power management policies for smart IT devices assisted by fine grain control of external power drawn by each device. By applying different power management policies at different scheduled intervals and controlling the power consumption on the smart devices, the aggregated peak power demand is controlled. The policies can be adapted in-time to suit the actual, real-time power requirement of devices, their priorities, and applicable peak power limit at that time. Also, dynamic policy based peak power management can be extended to an intelligent hierarchical power distribution network.

Abstract: A peak power management system for networked smart IT devices. These smart devices have computing capability with at least one CPU and memory and can be networked. An uninterruptible power supply provides power to the smart devices. A central intelligent power management server controls the power consumed by all the smart devices networked with the server. The system uses priority based peak power management policies for smart IT devices assisted by fine grain control of external power drawn by each device. By applying different power management policies at different scheduled intervals and controlling the power consumption on the smart devices, the aggregated peak power demand is controlled. The policies can be adapted in-time to suit the actual, real-time power requirement of devices, their priorities, and applicable peak power limit at that time. Also, dynamic policy based peak power management can be extended to an intelligent hierarchical power distribution network.