Abstract: A device may receive a computing resource request. The computing resource request may be related to allocating computing resources for a job. The device may process the computing resource request to identify a set of parameters related to the computing resource request or to the job. The set of parameters may be used to determine an allocation of the computing resources for the job. The device may utilize multiple machine learning models to process data related to the set of parameters identified in the computing resource request. The device may determine the allocation of the computing resources for the job based on utilizing the multiple machine learning models to process the data. The device may generate a set of scripts related to causing the computing resources to be allocated for the job according to the allocation. The device may perform a set of actions based on the set of scripts.

Abstract: A multi-layer compute sizing correction stack may generate prescriptive compute sizing correction tokens for controlling sizing adjustments for computing resources. The input layer of the compute sizing correction stack may generate cleansed utilization data based on historical utilization data received via network connection. A prescriptive engine layer may generate a compute sizing correction trajectory detailing adjustments to sizing for the computing resources. Based on the compute sizing correction trajectory, the prescriptive engine layer may generate the compute sizing correction tokens that that may be used to control compute sizing adjustments prescriptively.

Abstract: Data from an application is analyzed periodically in real-time using a predictive data model to predict potential application failures. Application logs are used to build the predictive data model. The predictive data model includes features created from the application logs which are good predictors of application failures. The predictive data model analyzes and provides anomaly scores for various anomalies for the real-time data collected over a time period. The anomaly scores are compared with a threshold score to predict the probability of occurrences of application failures. If there is a higher probability of occurrence of an incident of application failure, corrective actions to mitigate or reduce the probability of occurrence of the application failure are implemented.

Abstract: Data from an application is analyzed periodically in real-time using a predictive data model to predict potential application failures. Application logs are used to build the predictive data model. The predictive data model includes features created from the application logs which are good predictors of application failures. The predictive data model analyzes and provides anomaly scores for various anomalies for the real-time data collected over a time period. The anomaly scores are compared with a threshold score to predict the probability of occurrences of application failures. If there is a higher probability of occurrence of an incident of application failure, corrective actions to mitigate or reduce the probability of occurrence of the application failure are implemented.

Abstract: Methods, systems, computer-readable media, and apparatuses for detecting unauthorized activity are disclosed. Detecting unauthorized activity is done by accessing first data that represents activity involving a first service provided to a customer, accessing second data that represents activity involving a second service provided to a customer. The activity involving the second service and the activity involving the first service both include authorized customer activity, and the activity associated with the second service further includes unauthorized activity. The first data is filtered using a filtering criteria and a portion of the first data is selected to be retained. The second data and the retained portion of the first data are analyzed, and the analysis includes classifying the activity associated with the second service in a way that distinguishes the unauthorized activity from the authorized activity associated with the second service.

Abstract: As transmitted digital content is vulnerable commodity, its protection from piracy is receiving significant attention. It is possible today to extract the digital content from the temporary storage during processing and also form interfaces during transfer between blocks in a receiver system. When content is processed and frames are temporarily stored in external memory, when frames are transmitted to the display through a LVDS, or other screen/panel interface, they are transmitted non-secured. When captured at these points of vulnerability, the full resolution image is available for reproduction. According to the present invention additional security protection is enabled at these points of vulnerability. The disclosed practice of randomized scrambling of bits or groups of bits at the points of vulnerability in a digital transmit-receive system prevent pirating of useable content.