Abstract: A connection between a power distribution unit (PDU) and an electric device is identified by generating a data packet containing an identity of the electric device, transmitting the data packet by varying, on a power line connecting an output socket of the PDU to the electric device, a power level between distinct intensities for transmitting distinct logical values of the data packet, sensing, at the PDU, the power level on the power line, reporting, to an identification module, a socket number of the output socket connected to the power line and successive power level data obtained by sensing the power level on the power line, decoding the identity of the electric device by monitoring the successive power level data reported by the PDU, and storing, in a database, a reference between the identity of the electric device, an identity of the PDU, and the socket number.

Abstract: A health of a server infrastructure is supervised by sending measurement requests, from a polling node to a server of the server infrastructure, at each of successive polling periods. Measurements received from the server in response to the measurement requests are stored in a database. A machine learning system is trained using accumulated measurements stored in the database to construct a prediction model for the server. Compliance of a latest measurement received from the server with the prediction model for the server is evaluated. A corrective action is taken if the latest measurement does not comply with the prediction model for the server. Measurements received from a group of servers may be aggregated and the machine learning system may construct a prediction model for the group of servers. Compliance to the prediction model may be evaluated for the group of servers.

Abstract: A health of a server infrastructure is supervised by sending measurement requests, from a polling node to a server of the server infrastructure, at each of successive polling periods. Measurements received from the server in response to the measurement requests are stored in a database. A machine learning system is trained using accumulated measurements stored in the database to construct a prediction model for the server. Compliance of a latest measurement received from the server with the prediction model for the server is evaluated. A corrective action is taken if the latest measurement does not comply with the prediction model for the server. Measurements received from a group of servers may be aggregated and the machine learning system may construct a prediction model for the group of servers. Compliance to the prediction model may be evaluated for the group of servers.