Abstract: Data processing workloads are selectively assigned within a data center and/or among data centers based on non-data processing overhead within the data center and/or among the data centers. Power consumption of a rack including servers is predicted based on data processing demands that are placed on the servers for a given data processing workload, and power consumed by the rack is measured when the servers are performing the given data processing workload. A metric of power consumed by the rack for non-data processing overhead is derived based on a difference between results of the predicting and the measuring. A future data processing workload is selectively assigned to the rack based on the metric of power of power consumed by the rack for the non-data processing overhead. Assignment may also take place at an aisle and/or data center level based on these metrics.

Abstract: A power utilization index is determined for a server that defines an amount of energy that is consumed by the server for a unit of workload performed by the server. Future power usage by the server may then be predicted based on the power utilization index and a projected workload demand on the server. Moreover, workload for the server may be selectively assigned in response to the predicting. The power utilization index may be determined by a obtaining measurements of power consumed by the server in response to various workloads. Measurements of workload demands placed on the server are also obtained for the workloads. The measurements of workload demands may separately account for demands placed upon a processor subsystem of the server, a memory subsystem of the server, a network communication subsystem of the server and a storage subsystem of the server, for the various workloads.

Abstract: A computing workload is allocated amongst servers based on energy usage considerations. An energy consumption model is created for different server configurations. Based on the energy consumption models for the respective server configurations, different energy consumptions are predicted for executing a workload in corresponding different allocations of the workload on the servers. One of the allocations is selected based on the predicted energy consumptions. The selected allocation could minimize total energy use, reduce peak energy use, spread out energy use, etc.

Abstract: A power utilization index is determined for a server that defines an amount of energy that is consumed by the server for a unit of workload performed by the server. Future power usage by the server may then be predicted based on the power utilization index and a projected workload demand on the server. Moreover, workload for the server may be selectively assigned in response to the predicting. The power utilization index may be determined by a obtaining measurements of power consumed by the server in response to various workloads. Measurements of workload demands placed on the server are also obtained for the workloads. The measurements of workload demands may separately account for demands placed upon a processor subsystem of the server, a memory subsystem of the server, a network communication subsystem of the server and a storage subsystem of the server, for the various workloads.

Abstract: Data processing workloads are selectively assigned within a data center and/or among data centers based on non-data processing overhead within the data center and/or among the data centers. Power consumption of a rack including servers is predicted based on data processing demands that are placed on the servers for a given data processing workload, and power consumed by the rack is measured when the servers are performing the given data processing workload. A metric of power consumed by the rack for non-data processing overhead is derived based on a difference between results of the predicting and the measuring. A future data processing workload is selectively assigned to the rack based on the metric of power of power consumed by the rack for the non-data processing overhead. Assignment may also take place at an aisle and/or data center level based on these metrics.