Abstract: Methods, systems and apparatus, including computer programs encoded on computer storage medium, for predicting a likelihood of a future computer memory failure. In one aspect training data inputs are obtained, where each training data input includes correctable memory error data that describes correctable errors that occurred in a computer memory and data indicating whether the correctable errors produced a failure of the computer memory. For each training data input, image representations of the correctable memory error data included in the training data input are generated. The image representations are processed using a machine learning model to output an estimated likelihood of a future failure of the computer memory. A difference between the estimated likelihood of the future failure of the computer memory and the data indicating whether the correctable errors produced a failure of the computer memory is computed. Values of model parameters are updated using the computed difference.

Abstract: Methods, systems and apparatus, including computer programs encoded on computer storage medium, for predicting a likelihood of a future computer memory failure. In one aspect training data inputs are obtained, where each training data input includes correctable memory error data that describes correctable errors that occurred in a computer memory and data indicating whether the correctable errors produced a failure of the computer memory. For each training data input, image representations of the correctable memory error data included in the training data input are generated. The image representations are processed using a machine learning model to output an estimated likelihood of a future failure of the computer memory. A difference between the estimated likelihood of the future failure of the computer memory and the data indicating whether the correctable errors produced a failure of the computer memory is computed. Values of model parameters are updated using the computed difference.

Abstract: Methods, systems and apparatus, including computer programs encoded on computer storage medium, for predicting a likelihood of a future computer memory failure. In one aspect training data inputs are obtained, where each training data input includes correctable memory error data that describes correctable errors that occurred in a computer memory and data indicating whether the correctable errors produced a failure of the computer memory. For each training data input, image representations of the correctable memory error data included in the training data input are generated. The image representations are processed using a machine learning model to output an estimated likelihood of a future failure of the computer memory. A difference between the estimated likelihood of the future failure of the computer memory and the data indicating whether the correctable errors produced a failure of the computer memory is computed. Values of model parameters are updated using the computed difference.

Abstract: A patch panel device has communication ports respectively associated with a unique binary number having a number of binary digits, and a plurality of computing devices are each respectively coupled to at least one of the ports. A connectivity test includes configuring the patch panel device to send a message to a first sending device in response to receiving data at a port associated with a binary number having a first value as the respective binary digit, and not to send a message to a second sending device in response to receiving data at a port associated with a binary number having a second value as the respective binary digit. Each of the computing devices is caused to transmit data for arrival at a corresponding port, an indication is received for each device regarding whether the device received a message. A connectivity map is generated.