Abstract: In disclosed techniques, simulations are performed to determine data center performance under certain conditions. The simulations are dynamic and allow for changes in power demand due to temporal data center activities. In order to accommodate predicted and unpredicted fluctuations in power demand of a data center, one or more power caches are configured to supply additional power during periods of peak demand. Power caches provide supplemental power during periods of peak demand. The simulations are used for a variety of purposes, including determining the effects of power caches going offline under various conditions. Disclosed techniques can simulate the cycling of a power cache and can determine if additional configuration changes to the data center are warranted to maintain optimal health of the power caches. Thus, power scenario simulation of a data center can provide information vital to efficient operation of the data center.

Abstract: Disclosed techniques include time-varying power management within datacenters. A set of power policies for managing power within a datacenter is obtained. The set of policies varies over time. A priority is determined for a policy within the set of policies for managing the power within the datacenter. The policy within the set varies over time. A situation within the datacenter is identified where the situation matches that described in the policy within the set of policies. A power arrangement within the datacenter is modified based on the policy within the set of policies. The power arrangement within the datacenter applies to a section of the datacenter including one or more IT racks. The modifying includes powering a set of loads within the datacenter by a specific power source, changing a topology within the datacenter, powering down a portion of the datacenter, or changing a service level agreement support level.

Abstract: In disclosed techniques, augmented power control within a datacenter uses predictive modeling. A power usage by a first data rack within a datacenter is measured, using one or more processors, over a first period of time. A predicted power usage by the first data rack over a second period of time is generated on a computing device, wherein the second period of time is subsequent to the first period of time. A power correlation model is calculated that correlates a power prediction to an actual power usage. The predicted power usage for the second period of time is refined, based on the predicted power usage and the power correlation model. The refining is accomplished using a power prediction model comprising the predicted power usage and the power correlation model. Datacenter power structure is configured, based on the refined power usage prediction.

Abstract: Disclosed techniques include power manipulation using power caches within a datacenter. The power caches store power for later use when power demand exceeds the power available. A power source is coupled across a plurality of data racks to a power load within a datacenter. A power cache is coupled to the power load. Power for the power load is provided from the power source. The power cache is enabled to provide power to the power load when power requirements of the power load exceed a power threshold. The power threshold is determined by a set of power policies for the datacenter. The enabling the power cache to provide power to the power load provides power bursting capability.

Abstract: Disclosed techniques enable power manipulation within a data center based on power policies. Power policies are used to establish behavior based on limits for power consumption, power generation, and other criteria. Rules are established to provide fractional power sharing and control under certain conditions. Power caches are used to supplement power sources under conditions of peak power requirements. When power requirements are below a threshold set forth in a power policy, the power caches are replenished.

Abstract: Disclosed techniques include time-varying power management within datacenters. A set of power policies for managing power within a datacenter is obtained. The set of policies varies over time. A priority is determined for a policy within the set of policies for managing the power within the datacenter. The policy within the set varies over time. A situation within the datacenter is identified where the situation matches that described in the policy within the set of policies. A power arrangement within the datacenter is modified based on the policy within the set of policies. The power arrangement within the datacenter applies to a section of the datacenter including one or more IT racks. The modifying includes powering a set of loads within the datacenter by a specific power source, changing a topology within the datacenter, powering down a portion of the datacenter, or changing a service level agreement support level.

Abstract: In disclosed techniques, simulations are performed to determine data center performance under certain conditions. The simulations are dynamic and allow for changes in power demand due to temporal data center activities. In order to accommodate predicted and unpredicted fluctuations in power demand of a data center, one or more power caches are configured to supply additional power during periods of peak demand. Power caches provide supplemental power during periods of peak demand. The simulations are used for a variety of purposes, including determining the effects of power caches going offline under various conditions. Disclosed techniques can simulate the cycling of a power cache and can determine if additional configuration changes to the data center are warranted to maintain optimal health of the power caches. Thus, power scenario simulation of a data center can provide information vital to efficient operation of the data center.

Abstract: In disclosed techniques, augmented power control within a datacenter uses predictive modeling. A power usage by a first data rack within a datacenter is measured, using one or more processors, over a first period of time. A predicted power usage by the first data rack over a second period of time is generated on a computing device, wherein the second period of time is subsequent to the first period of time. A power correlation model is calculated that correlates a power prediction to an actual power usage. The predicted power usage for the second period of time is refined, based on the predicted power usage and the power correlation model. The refining is accomplished using a power prediction model comprising the predicted power usage and the power correlation model. Datacenter power structure is configured, based on the refined power usage prediction.

Abstract: Disclosed techniques enable power manipulation within a data center based on power policies. Power policies are used to establish behavior based on limits for power consumption, power generation, and other criteria. Rules are established to provide fractional power sharing and control under certain conditions. Power caches are used to supplement power sources under conditions of peak power requirements. When power requirements are below a threshold set forth in a power policy, the power caches are replenished.