Hardware Component Monitoring-based Performance of a Remedial Action

Abstract

An illustrative method may include identifying, based on data associated with an operation of a hardware component, an anomaly in the data; determining that the anomaly is representative of an issue associated with the hardware component; and performing, based on the determining that the anomaly is representative of the issue associated with the hardware component, a remedial action that affects a performance of the operation of the hardware component.

What is claimed is: 
     
         1 . A method comprising:
 identifying, based on data associated with an operation of a hardware component, an anomaly in the data;   determining that the anomaly is representative of an issue associated with the hardware component; and   performing, based on the determining that the anomaly is representative of the issue associated with the hardware component, a remedial action that affects a performance of the operation of the hardware component.   
     
     
         2 . The method of  claim 1 , wherein the performing the remedial action comprises slowing down a performance of the operation of the hardware component. 
     
     
         3 . The method of  claim 1 , wherein the performing the remedial action comprises preventing the operation of the hardware component from being performed. 
     
     
         4 . The method of  claim 1 , wherein the performing the remedial action comprises disabling the hardware component. 
     
     
         5 . The method of  claim 1 , wherein the performing the remedial action comprises directing an additional hardware component to perform the operation in place of the hardware component. 
     
     
         6 . The method of  claim 1 , wherein the determining that the anomaly is representative of the issue associated with the hardware component comprises applying data representative of the anomaly as an input to machine learning model, the machine learning model configured to:
 determine a confidence score for the anomaly;   determine that the confidence score is above a threshold associated with the issue; and   classify, in response to the determination that the confidence score is above the threshold, the anomaly as being representative of the issue associated with the hardware component.   
     
     
         7 . The method of  claim 6 , further comprising:
 receiving user input confirming or refuting that the anomaly is an issue associated with the hardware component; and   providing the user input as a training input to the machine learning model.   
     
     
         8 . The method of  claim 1 , wherein the determining that the anomaly is representative of the issue associated with the hardware component comprises:
 applying a rule set to data representative of the anomaly to generate a confidence score for the anomaly; and   determining that the confidence score is above a threshold associated with the issue.   
     
     
         9 . The method of  claim 1 , further comprising:
 identifying an additional anomaly in the data;   determining that the additional anomaly is not representative of an issue associated with the hardware component; and   abstaining, in response to the determining that the additional anomaly is not representative of the issue associated with the hardware component, from performing a remedial action associated with the additional anomaly.   
     
     
         10 . The method of  claim 1 , further comprising:
 receiving, by way of a network, phone-home logs from a hardware-based system that includes the hardware component; and   extracting the data from the phone-home logs.   
     
     
         11 . The method of  claim 1 , further comprising extracting the data with an application executed by a hardware-based system that includes the hardware component. 
     
     
         12 . The method of  claim 1 , wherein the data comprises at least one of data associated with a physical aspect of the hardware component or data associated with an operation of a software component that utilizes the hardware component. 
     
     
         13 . The method of  claim 1 , wherein the data comprises at least one of data representative of a power consumption of the hardware component, data indicating an occurrence of a power recycle of the hardware component, data representative of an alert generated with respect to the hardware component, data representative of an occurrence of an event associated with the hardware component, data associated with a wear cycle of the hardware component, data representative of a temperature of the hardware component, data representative of a clock speed of the hardware component, data representative of a usage of the hardware component, or data representative of an available storage capacity of the hardware component. 
     
     
         14 . The method of  claim 1 , wherein the identifying the anomaly in the data comprises:
 providing the data as an input to a machine learning model; and   identifying the anomaly based on an output of the machine learning model.   
     
     
         15 . The method of  claim 14 , wherein the machine learning model is configured to implement a variational autoencoder heuristic by:
 encoding the data to generate encoded data;   decoding the encoded data to generate decoded data;   determining an error measurement that represents a decoding error between the data and the decoded data; and   generating, based on the decoding error, a confidence score for a data subset of the data that indicates that the data subset includes the anomaly.   
     
     
         16 . The method of  claim 1 , further comprising providing contextual information related to at least one of the anomaly or the remedial action by way of a natural language processing component. 
     
     
         17 . The method of  claim 1 , further comprising identifying, based on the anomaly, a performance characteristic of the hardware component. 
     
     
         18 . A system comprising:
 a memory storing instructions; and   a processor communicatively coupled to the memory and configured to execute the instructions to perform a process comprising:
 identifying, based on data associated with an operation of a hardware component, an anomaly in the data; 
 determining that the anomaly is representative of an issue associated with the hardware component; and 
 performing, based on the determining that the anomaly is representative of the issue associated with the hardware component, a remedial action that affects a performance of the operation of the hardware component. 
   
     
     
         19 . The system of  claim 18 , wherein the identifying the anomaly in the data comprises:
 providing the data as an input to a machine learning model; and   identifying the anomaly based on an output of the machine learning model.   
     
     
         20 . A non-transitory computer-readable medium storing instructions that, when executed, direct a processor of a computing device to perform a process comprising:
 identifying, based on data associated with an operation of a hardware component, an anomaly in the data;   determining that the anomaly is representative of an issue associated with the hardware component; and   performing, based on the determining that the anomaly is representative of the issue associated with the hardware component, a remedial action that affects a performance of the operation of the hardware component.