Automated configuration determinations for data center devices using artificial intelligence techniques
Methods, apparatus, and processor-readable storage media for automated configuration determinations for data center devices using artificial intelligence are provided herein. An example computer-implemented method includes obtaining input information pertaining to one or more device-related changes to a data center; obtaining telemetry data attributed to one or more devices in the data center; determining 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; and performing at least one automated action based at least in part on the one or more determined device configurations.
1. A computer-implemented method comprising:
obtaining input information pertaining to one or more device-related changes to a data center;
obtaining telemetry data attributed to one or more devices in the data center;
determining 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
performing at least one automated action based at least in part on the one or more determined device configurations;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.
2. The computer-implemented method of claim 1 , wherein processing the input information and the obtained telemetry data using deep reinforcement learning in conjunction with at least one neural network comprises converting at least a portion of the input information and the obtained telemetry data into one or more multi-dimensional vectors.
3. The computer-implemented method of claim 2 , further comprising:
providing the one or more multi-dimensional vectors to the at least one neural network.
4. The computer-implemented method of claim 3 , wherein providing the one or more multi-dimensional vectors to the at least one neural network comprises passing the one or more multi-dimensional vectors through one or more convolutional layers, one or more pooling layers, and one or more fully connected linear layers of the at least one neural network.
5. The computer-implemented method of claim 1 , wherein the at least one neural network comprises one or more of at least one deep Q network and at least one convolutional neural network.
6. The computer-implemented method of claim 1 , wherein the telemetry data measure hardware performance status associated with at least a portion of the data center at a given point of time.
7. The computer-implemented method of claim 1 , wherein performing the at least one automated action comprises outputting identifying information for the one or more determined device configurations to at least one data center administrator.
8. The computer-implemented method of claim 1 , wherein performing the at least one automated action comprises updating device configurations for the at least one device in the data center based on the one or more determined device configurations.
9. 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.
10. The non-transitory processor-readable storage medium of claim 9 , wherein processing the input information and the obtained telemetry data using deep reinforcement learning in conjunction with at least one neural network comprises converting at least a portion of the input information and the obtained telemetry data into one or more multi-dimensional vectors.
11. The non-transitory processor-readable storage medium of claim 10 , wherein the program code when executed by the at least one processing device further causes the at least one processing device:
to provide the one or more multi-dimensional vectors to the at least one neural network.
12. The non-transitory processor-readable storage medium of claim 11 , wherein providing the one or more multi-dimensional vectors to the at least one neural network comprises passing the one or more multi-dimensional vectors through one or more convolutional layers, one or more pooling layers, and one or more fully connected linear layers of the at least one neural network.
13. 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.
14. The apparatus of claim 13 , wherein processing the input information and the obtained telemetry data using deep reinforcement learning in conjunction with at least one neural network comprises converting at least a portion of the input information and the obtained telemetry data into one or more multi-dimensional vectors.
15. The apparatus of claim 14 , wherein the at least one processing device is further configured:
to provide the one or more multi-dimensional vectors to the at least one neural network.
16. The apparatus of claim 15 , wherein providing the one or more multi-dimensional vectors to the at least one neural network comprises passing the one or more multi-dimensional vectors through one or more convolutional layers, one or more pooling layers, and one or more fully connected linear layers of the at least one neural network.
17. The apparatus of claim 13 , wherein performing the at least one automated action comprises outputting identifying information for the one or more determined device configurations to at least one data center administrator.
18. The apparatus of claim 13 , wherein performing the at least one automated action comprises updating device configurations for the at least one device in the data center based on the one or more determined device configurations.
19. The apparatus of claim 13 , wherein the at least one neural network comprises one or more of at least one deep Q network and at least one convolutional neural network.
20. The apparatus of claim 13 , wherein the telemetry data measure hardware performance status associated with at least a portion of the data center at a given point of time.