Abstract: Systems, methods, and techniques described herein relate to forecasting power consumption for maintenance of data centers. In an aspect, a maintenance window request for a requested maintenance task with respect to a data center is received. A machine learning (ML) model utilized to generate a window recommendation result, the model trained to determine a period of time where the task's impact on performance of the data center satisfies an impact criterion. The window recommendation result comprising a window indicating a period of time wherein impact of the requested maintenance task satisfies the impact criterion. Performance of the maintenance task is to be performed during the window. In an alternative aspect, the ML model is trained to forecast power consumption of a data center in a period of time. In this alternative, a maintenance window determiner determines the window recommendation result based on a power consumption forecast of the ML model.

Abstract: A power controller allocates input power to a datacenter between computing power for computing services, backup power, and overhead power for overhead systems. The power controller reallocates the overhead power and/or the backup power to the computing power. This may increase the overall utilization of the datacenter by allowing additional processing power of the servers to be used.

Abstract: A power controller allocates input power to a datacenter between computing power for computing services, backup power, and overhead power for overhead systems. The power controller reallocates the overhead power and/or the backup power to the computing power. This may increase the overall utilization of the datacenter by allowing additional processing power of the servers to be used.

Abstract: A method of power management in a datacenter includes obtaining at least one workload status of at least one server rack, obtaining at least one infrastructure parameter, obtaining at least one utility telemetry, and comparing the at least one workload status to the at least one utility telemetry. The method further includes determining a workload demand based at least partially on a difference between the at least one workload status and the at least one utility telemetry and changing the at least one infrastructure parameter based on the workload demand and the at least one infrastructure parameter.