Abstract: Systems, computer program products, and methods are described herein for determining causes of network anomalies across a distributed network. The method includes determining one or more known indicator patterns from one or more error logs for one or more applications on a network. The method also includes receiving one or more performance indicators during an execution of an application. The method further includes comparing the one or more performance indicators to one or more known indicator patterns. The method also includes determining at least one of the one or more performance indicators that is malfeasant. The method also includes determining a malfeasant component of the network based on the one or more performance indicators that is malfeasant. The method also includes determining a remediation action to be executed in response to the comparison of the one or more performance indicators to one or more known indicator patterns.

Abstract: A method includes receiving a learning requirement for a user. A preliminary learning recommendation is generated based on the learning requirement of the user and learning histories of a plurality of users. The preliminary learning recommendation is filtered to generate a filtered learning recommendation. A problem of finding an improved learning recommendation from the filtered learning recommendation is mapped to a quantum Ising model. A quantum annealing process is performed one or more times to determine one or more final quantum states from an initial quantum state. One or more energies of the quantum Ising model are determined for the one or more final quantum states. A minimum energy is determined among the determined energies. A target quantum state that corresponds to the minimum energy is determined among the determined final quantum states. The improved learning recommendation is determined from the target quantum state and is sent to the user.

Abstract: Systems, computer program products, and methods are described herein for end-to-end automation of network infrastructure development changes. The present disclosure is configured to collect and archive data comprising feature logs, resource logs, and events in a central historical data storage. This data is subsequently normalized and events are correlated to specific features. This correlation involves locating differences in pre and post-deployment feature performance, which includes resource utilization, and maintaining a history of the analyzed data in the central historical data storage.