Abstract: A system and method for characterizing the data used to train a model for machine learning inference. Training data and production data may both be fingerprinted, and the fingerprints may be compared to detect undesirable variances between training and production data. This may allow performance issues relating to differences in the training data set versus the production data set to be more easily identified. Parameters used for characterization can be determined based on the type of training data such as numerical data, image data, or audio data.

Abstract: A system and method for managing large numbers of computing devices in a data center are disclosed. The computing devices are configured to flash their indicator lights in a pattern that encodes a device ID, and an image capture device such as a mobile phone or tablet captures the flashes in a series of images/video of the data center. The images/video are processed to create a three-dimensional (3D) model of the data center with computing device IDs positioned therein. The 3D model, including correctly positioned device ID indicators, can be rendered for the user of the mobile device to enable the user to more easily identify computing device locations.

Abstract: A management device for managing a plurality of computing devices in a data center may comprise a network interface, a first module that periodically sends health status queries to the computing devices via the network interface, a second module configured to receive responses to the health status queries and collect and store health status data for the computing devices, a third module configured to create support tickets, and/or a fourth module configured to (i) create and periodically update a Cox proportional hazards (CPH) model based on the health status data; (ii) apply a deep neural network (DNN) to the input of the CPH model; (iii) determine a probability of failure for each computing device; (iv) compare each probability of failure with a threshold; and (v) cause the third module to generate a pre-failure support ticket for each computing device having determined probabilities of failure above the threshold.

Abstract: A system and method for managing large numbers of computing devices in a data center are disclosed. The computing devices are configured to flash their indicator lights in a pattern that encodes a device ID, and an image capture device such as a mobile phone or tablet captures the flashes in a series of images/video of the data center. The images/video are processed to create a three-dimensional (3D) model of the data center with computing device IDs positioned therein. The 3D model, including correctly positioned device ID indicators, can be rendered for the user of the mobile device to enable the user to more easily identify computing device locations.

Abstract: A management device for managing a plurality of computing devices in a data center may comprise a network interface, a first module that periodically sends health status queries to the computing devices via the network interface, a second module configured to receive responses to the health status queries and collect and store health status data for the computing devices, a third module configured to create support tickets, and/or a fourth module configured to (i) create and periodically update a Cox proportional hazards (CPH) model based on the health status data; (ii) apply a deep neural network (DNN) to the input of the CPH model; (iii) determine a probability of failure for each computing device; (iv) compare each probability of failure with a threshold; and (v) cause the third module to generate a pre-failure support ticket for each computing device having determined probabilities of failure above the threshold.

Abstract: A system and method for managing large numbers of computing devices in a data center are disclosed. The computing devices are configured to flash their indicator lights in a pattern that encodes a device ID, and an image capture device such as a mobile phone or tablet captures the flashes in a series of images/video of the data center. The images/video are processed to create a three-dimensional (3D) model of the data center with computing device IDs positioned therein. The 3D model, including correctly positioned device ID indicators, can be rendered for the user of the mobile device to enable the user to more easily identify computing device locations.

Abstract: A system and method for automating management and repair of a plurality of computing devices located in a data center is disclosed. Health status queries are issued for one or more of the computing devices. If responses not indicative of good device health are received, one or more repair instructions are automatically sent to the unhealthy computing device to repair the computing device by moving it to an acceptable state. If the repair instructions are not successful, a support ticket is automatically generated for the corresponding computing device or devices. Problematic statuses across areas of the data center may be detected and ticketed in addition to individual problematic devices. So-called repeat offender devices may be detected and ticketed even if the repair instructions are successful.