# US 12,224,922 B1 · Data center workload monitoring via a data center monitoring and management operation

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- Number: US 12,224,922 B1
- Document ID: US12224922B1
- Status: Granted
- Kind: B1
- Application number: 18/226,902
- Filed: 2023-07-27
- Published: 2025-01-30
- Granted: 2025-02-11
- Earliest claimed: 2023-07-27
- Assignees: Dell Products L.P.
- Applicants: Dell Products L.P.
- Inventors: Vinay Sawal; Ching-Yun Chao; Anil Varkhedi; Deepak Krishna; Rajini Karthik; Muzhar S. Khokhar
- Web page: https://patentgrep.com/patent/US12224922B1

## CPC

- H04L 43/067: using time frame reporting
- H04L 41/16: using machine learning or artificial intelligence
- H04L 43/0817: by checking functioning
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

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.

## Claims

### Claim 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.

### Claim 2 (depends on 1)

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.

### Claim 3 (depends on 2)

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.

### Claim 4 (depends on 1)

The method of claim 1, wherein:
the data center asset workload data comprises installation data.

### Claim 5 (depends on 4)

The method of claim 4, wherein:
the installation data comprises image data and text data.

### Claim 6 (depends on 5)

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.

### Claim 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.

### Claim 8 (depends on 7)

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.

### Claim 9 (depends on 8)

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.

### Claim 10 (depends on 7)

The system of claim 7, wherein:
the data center asset workload data comprises installation data.

### Claim 11 (depends on 10)

The system of claim 10, wherein:
the installation data comprises image data and text data.

### Claim 12 (depends on 11)

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.

### Claim 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.

### Claim 14 (depends on 13)

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.

### Claim 15 (depends on 14)

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.

### Claim 16 (depends on 13)

The non-transitory, computer-readable storage medium of claim 13, wherein:
the data center asset workload data comprises installation data.

### Claim 17 (depends on 16)

The non-transitory, computer-readable storage medium of claim 16, wherein:
the installation data comprises image data and text data.

### Claim 18 (depends on 17)

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.

### Claim 19 (depends on 13)

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.

### Claim 20 (depends on 13)

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.