Artificial intelligence-based redundancy management framework
Methods, apparatus, and processor-readable storage media for artificial intelligence-based redundancy management are provided herein. An example computer-implemented method includes obtaining telemetry data from one or more client devices within at least one system; predicting one or more hardware component failures in at least a portion of the one or more client devices within the at least one system by processing at least a portion of the telemetry data using a first set of one or more artificial intelligence techniques; determining, using a second set of one or more artificial intelligence techniques, one or more redundant hardware components for implementation in connection with the one or more predicted hardware component failures; and performing at least one automated action based at least in part on the one or more redundant hardware components.
1. A computer-implemented method comprising:
obtaining telemetry data from one or more client devices within at least one system;
predicting one or more hardware component failures in at least a portion of the one or more client devices within the at least one system by processing at least a portion of the telemetry data using a first set of one or more artificial intelligence techniques;
determining, using a second set of one or more artificial intelligence techniques, one or more redundant hardware components for implementation in connection with the one or more predicted hardware component failures; and
performing at least one automated action based at least in part on the one or more redundant hardware components, wherein performing the at least one automated action comprises classifying at least a given one of the one or more redundant hardware components as one of a customer replaceable unit and a field replaceable unit;
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 the first set of one or more artificial intelligence techniques comprises at least one of a naïve Bayes classifier algorithm and a supervised machine learning model.
3. The computer-implemented method of claim 2 , wherein the naïve Bayes classifier algorithm comprises a Bernoulli naïve Bayes classifier.
4. The computer-implemented method of claim 1 , further comprising:
training the first set of one or more artificial intelligence techniques using one or more of historical telemetry data pertaining to the at least one system, sensor data related to the at least one system, product data pertaining to the at least one system, and manufacturing information related to the at least one system.
5. The computer-implemented method of claim 1 , wherein the second set of one or more artificial intelligence techniques comprises at least one of a restricted Boltzmann machine and a stochastic neural network.
6. The computer-implemented method of claim 5 , wherein the restricted Boltzmann machine comprises a first layer of one or more visible units and a second layer of one or more hidden units, wherein the one or more units in each layer are not connected within the layer, and wherein the one or more units in each layer are connected to at least a portion of the one or more units in the other layer.
7. The computer-implemented method of claim 1 , further comprising:
training the second set of one or more artificial intelligence techniques using one or more of historical service data related to the at least one system, hardware component dispatch information, product data pertaining to the at least one system, hardware component configuration data, and supply chain data related to the at least one system.
8. The computer-implemented method of claim 1 , wherein performing the at least one automated action comprises initiating provision of a replacement of at least a portion of the one or more determined redundant hardware components to at least a portion of the one or more client devices within the at least one system.
9. The computer-implemented method of claim 1 , wherein performing the at least one automated action comprises outputting one or more notifications, related to at least one of the one or more predicted hardware component failures and the one or more redundant hardware components, to at least one customer relationship management system.
10. The computer-implemented method of claim 1 , wherein the one or more redundant hardware components comprise multiple redundant hardware components, and wherein performing the at least one automated action comprises classifying each of a plurality of the multiple redundant hardware components as one of a customer replaceable unit and a field replaceable unit.
11. The computer-implemented method of claim 1 , wherein performing the at least one automated action comprises instructing at least one of the one or more client devices within the at least one system to automatically transition to at least one of the one or more redundant hardware components.
12. The computer-implemented method of claim 1 , further comprising:
calculating a remaining lifespan of the one or more redundant hardware components based at least in part on analysis of one or more of additional telemetry data, system environment information, one or more component-related internal factors, and one or more component-related external factors.
13. The computer-implemented method of claim 1 , wherein the telemetry data comprise one or more of resource utilization data derived from the at least one system, information pertaining to errors within the at least one system, log information derived from the at least one system, and system alerts related to the at least one system.
14. 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 telemetry data from one or more client devices within at least one system;
to predict one or more hardware component failures in at least a portion of the one or more client devices within the at least one system by processing at least a portion of the telemetry data using a first set of one or more artificial intelligence techniques;
to determine, using a second set of one or more artificial intelligence techniques, one or more redundant hardware components for implementation in connection with the one or more predicted hardware component failures; and
to perform at least one automated action based at least in part on the one or more redundant hardware components, wherein performing the at least one automated action comprises classifying at least a given one of the one or more redundant hardware components as one of a customer replaceable unit and a field replaceable unit.
15. The non-transitory processor-readable storage medium of claim 14 , wherein the first set of one or more artificial intelligence techniques comprises at least one of a naïve Bayes classifier algorithm and a supervised machine learning model.
16. The non-transitory processor-readable storage medium of claim 14 , wherein the second set of one or more artificial intelligence techniques comprises at least one of a restricted Boltzmann machine and a stochastic neural network, and wherein the restricted Boltzmann machine comprises a first layer of one or more visible units and a second layer of one or more hidden units, wherein the one or more units in each layer are not connected within the layer, and wherein the one or more units in each layer are connected to at least a portion of the one or more units in the other layer.
17. The non-transitory processor-readable storage medium of claim 14 , wherein performing the at least one automated action comprises initiating provision of a replacement of at least a portion of the one or more determined redundant hardware components to at least a portion of the one or more client devices within the at least one system.
18. 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 telemetry data from one or more client devices within at least one system;
to predict one or more hardware component failures in at least a portion of the one or more client devices within the at least one system by processing at least a portion of the telemetry data using a first set of one or more artificial intelligence techniques;
to determine, using a second set of one or more artificial intelligence techniques, one or more redundant hardware components for implementation in connection with the one or more predicted hardware component failures; and
to perform at least one automated action based at least in part on the one or more redundant hardware components, wherein performing the at least one automated action comprises classifying at least a given one of the one or more redundant hardware components as one of a customer replaceable unit and a field replaceable unit.
19. The apparatus of claim 18 , wherein the first set of one or more artificial intelligence techniques comprises at least one of a naïve Bayes classifier algorithm and a supervised machine learning model.
20. The apparatus of claim 18 , wherein the second set of one or more artificial intelligence techniques comprises at least one of a restricted Boltzmann machine and a stochastic neural network, and wherein the restricted Boltzmann machine comprises a first layer of one or more visible units and a second layer of one or more hidden units, wherein the one or more units in each layer are not connected within the layer, and wherein the one or more units in each layer are connected to at least a portion of the one or more units in the other layer.