Automated configuration determinations for data center devices using artificial intelligence techniques

A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:

• to obtain input information pertaining to one or more device-related changes to a data center;
• to obtain telemetry data attributed to one or more devices in the data center;
• to determine one or more device configurations for implementation in at least one device in the data center in connection with the one or more device-related changes by processing the input information and the obtained telemetry data using one or more artificial intelligence techniques, wherein processing the input information and the obtained telemetry data comprises processing the input information and the obtained telemetry data using deep reinforcement learning in conjunction with at least one neural network to predict at least one impact of at least one device configuration change to performance of the data center, wherein using deep reinforcement learning in conjunction with at least one neural network comprises assigning one or more rewards and one or more penalties for impacts of device configuration changes to data center performance, wherein each reward is assigned to a device configuration change for at least one positive impact on data center performance and each penalty is assigned to a device configuration change for at least one negative impact on data center performance;
• and
• to perform at least one automated action based at least in part on the one or more determined device configurations.

An apparatus comprising:

• at least one processing device comprising a processor coupled to a memory;
• the at least one processing device being configured:
• to obtain input information pertaining to one or more device-related changes to a data center;
• to obtain telemetry data attributed to one or more devices in the data center;
• to determine one or more device configurations for implementation in at least one device in the data center in connection with the one or more device-related changes by processing the input information and the obtained telemetry data using one or more artificial intelligence techniques, wherein processing the input information and the obtained telemetry data comprises processing the input information and the obtained telemetry data using deep reinforcement learning in conjunction with at least one neural network to predict at least one impact of at least one device configuration change to performance of the data center, wherein using deep reinforcement learning in conjunction with at least one neural network comprises assigning one or more rewards and one or more penalties for impacts of device configuration changes to data center performance, wherein each reward is assigned to a device configuration change for at least one positive impact on data center performance and each penalty is assigned to a device configuration change for at least one negative impact on data center performance;
• and
• to perform at least one automated action based at least in part on the one or more determined device configurations.