Data center workload monitoring via a data center monitoring and management operation
A system, method, and computer-readable medium for performing a data center monitoring and management operation. The data center monitoring and management operation includes: receiving data center asset workload data; capturing a set of the data center asset workload data at predefined time intervals; monitoring data center workload performance using the set of the data center asset workload data; and, predicting an anomaly associated with data center workload performance based upon the monitoring.
1. A computer-implementable method for performing a data center monitoring and management operation, comprising:
receiving data center asset workload data associated with a workload executing on a data center asset;
capturing a set of the data center asset workload data at predefined time intervals, the set of the data center asset workload data representing a sequence of events occurring within a time window on the data center asset, the sequence of events comprising an iteratively captured sequence of image-based console data;
monitoring data center workload performance using the set of the data center asset workload data, the monitoring being performed at a particular point in a lifecycle of the workload executing on the data center asset; and,
predicting an anomaly associated with data center workload performance based upon the monitoring.
2. The method of claim 1 , further comprising:
analyzing a sliding window time slice of the set of data center asset workload data; and,
applying a learning model to the sliding window time slice of the set of data center asset workload data.
3. The method of claim 2 , wherein:
the learning model comprises one or more of a deep learning model and a bidirectional encoder representation from transformers (BERT) model.
4. The method of claim 1 , wherein:
the data center asset workload data comprises installation data.
5. The method of claim 4 , wherein:
the installation data comprises image data and text data.
6. The method of claim 5 , wherein:
the image data comprises an iteratively-captured set of K images; and,
the iteratively-captured set of K images are provided to a convolutional autoencoder, the convolutional autoencoder learning common occurrences of outcomes based upon the iteratively-captured set of K images.
7. A system comprising:
a processor;
a data bus coupled to the processor; and,
a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for:
receiving data center asset workload data associated with a workload executing on a data center asset;
capturing a set of the data center asset workload data at predefined time intervals, the set of the data center asset workload data representing a sequence of events occurring within a time window on the data center asset, the sequence of events comprising an iteratively captured sequence of image-based console data;
monitoring data center workload performance using the set of the data center asset workload data, the monitoring being performed at a particular point in a lifecycle of the workload executing on the data center asset; and,
predicting an anomaly associated with data center workload performance based upon the monitoring.
8. The system of claim 7 , wherein the instructions executable by the processor are further configured for:
analyzing a sliding window time slice of the set of data center asset workload data; and,
applying a learning model to the sliding window time slice of the set of data center asset workload data.
9. The system of claim 8 , wherein:
the learning model comprises one or more of a deep learning model and a bidirectional encoder representation from transformers (BERT) model.
10. The system of claim 7 , wherein:
the data center asset workload data comprises installation data.
11. The system of claim 10 , wherein:
the installation data comprises image data and text data.
12. The system of claim 11 , wherein:
the image data comprises an iteratively-captured set of K images; and,
the iteratively-captured set of K images are provided to a convolutional autoencoder, the convolutional autoencoder learning common occurrences of outcomes based upon the iteratively-captured set of K images.
13. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:
receiving data center asset workload data associated with a workload executing on a data center asset;
capturing a set of the data center asset workload data at predefined time intervals, the set of the data center asset workload data representing a sequence of events occurring within a time window on the data center asset, the sequence of events comprising an iteratively captured sequence of image-based console data;
monitoring data center workload performance using the set of the data center asset workload data, the monitoring being performed at a particular point in a lifecycle of the workload executing on the data center asset; and,
predicting an anomaly associated with data center workload performance based upon the monitoring.
14. The non-transitory, computer-readable storage medium of claim 13 , wherein the computer executable instructions are further configured for:
analyzing a sliding window time slice of the set of data center asset workload data; and,
applying a learning model to the sliding window time slice of the set of data center asset workload data.
15. The non-transitory, computer-readable storage medium of claim 14 , wherein:
the learning model comprises one or more of a deep learning model and a bidirectional encoder representation from transformers (BERT) model.
16. The non-transitory, computer-readable storage medium of claim 13 , wherein:
the data center asset workload data comprises installation data.
17. The non-transitory, computer-readable storage medium of claim 16 , wherein:
the installation data comprises image data and text data.
18. The non-transitory, computer-readable storage medium of claim 17 , wherein:
the image data comprises an iteratively-captured set of K images; and,
the iteratively-captured set of K images are provided to a convolutional autoencoder, the convolutional autoencoder learning common occurrences of outcomes based upon the iteratively-captured set of K images.
19. The non-transitory, computer-readable storage medium of claim 13 , wherein:
the computer executable instructions are deployable to a client system from a server system at a remote location.
20. The non-transitory, computer-readable storage medium of claim 13 , wherein:
the computer executable instructions are provided by a service provider to a user on an on-demand basis.