Abstract: Embodiments of self-locating computing devices, systems, and methods are described. In some embodiments, a computing device may include a Wireless Credential Exchange Module (WCEM) to detect one or more location tags and a management engine, coupled to the WCEM, to retrieve information of the one or more location tags from the WCEM, and to provide an asset management server with an identifier of the computing device and the information of the one or more location tags or location information of the computing device. Other embodiments may be described and/or claimed.

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: Embodiments of self-locating computing devices, systems, and methods are described. In some embodiments, a computing device may include a Wireless Credential Exchange Module (WCEM) to detect one or more location tags and a management engine, coupled to the WCEM, to retrieve information of the one or more location tags from the WCEM, and to provide an asset management server with an identifier of the computing device and the information of the one or more location tags or location information of the computing device. Other embodiments may be described and/or claimed.

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.