Abstract: Technologies for predicting the power usage of a data center are disclosed. A data center manager gathers sensor data from the compute devices of the data center. The sensor data indicates factors such as power used by the compute device and the intake air inlet temperature. The data center manager trains a machine-learning-based algorithm based on training sensor data, and then applies the machine-learning-based algorithm to sensor data as it is being gathered. The machine-learning-based algorithm can predict a change in future power usage of the data center, and control a cooling unit to compensate before the power usage even begins to change.

Abstract: Examples may include techniques to a schedule a workload to one or more computing resources of a data center. A class is determined for the workload based on a workload type or profile for the workload. Predicted operating values for at least one of the one or more computing resources is determined based on the class and the predicted operating values are used as inputs in at least one scoring model to evaluate the workload being supported by the at least one of the one or more computing resources. The workload is then scheduled to the at least one or more computing resources based on the evaluation.

Abstract: Technologies for predicting the power usage of a data center are disclosed. A data center manager gathers sensor data from the compute devices of the data center. The sensor data indicates factors such as power used by the compute device and the intake air inlet temperature. The data center manager trains a machine-learning-based algorithm based on training sensor data, and then applies the machine-learning-based algorithm to sensor data as it is being gathered. The machine-learning-based algorithm can predict a change in future power usage of the data center, and control a cooling unit to compensate before the power usage even begins to change.

Abstract: Examples may include techniques to a schedule a workload to one or more computing resources of a data center. A class is determined for the workload based on a workload type or profile for the workload. Predicted operating values for at least one of the one or more computing resources is determined based on the class and the predicted operating values are used as inputs in at least one scoring model to evaluate the workload being supported by the at least one of the one or more computing resources. The workload is then scheduled to the at least one or more computing resources based on the evaluation.