Generating data replication configurations using artificial intelligence techniques

Patent metadata

Field	Value
Publication number	US-11775196-B2
Application number	US-202016884622-A
Country	US
Kind code	B2
Filing date	May 27, 2020
Priority date	May 27, 2020
Publication date	Oct 3, 2023
Grant date	Oct 3, 2023

Abstract

Methods, apparatus, and processor-readable storage media for generating data replication configurations using AI techniques are provided herein. An example computer-implemented method includes obtaining input data pertaining to at least one data replication operation; determining a set of configuration parameters for the at least one data replication operation by applying one or more AI techniques to at least a portion of the input data; and performing one or more automated actions based at least in part on the determined set of configuration parameters for the at least one data replication operation.

First claim

What is claimed is:

1. A computer-implemented method comprising:
obtaining input data pertaining to at least one data replication operation, wherein the input data comprises information pertaining to requirements for the at least one data replication operation, the information pertaining to requirements for the at least one data replication operation comprising (i) transactional volume per structured dataset per at least one given temporal increment for at least one source, and (ii) a number of structured datasets subject to replication;
determining a set of configuration parameters for the at least one data replication operation by applying one or more artificial intelligence techniques to at least a portion of the input data, wherein applying the one or more artificial intelligence techniques to at least a portion of the input data comprises processing at least a portion of the information pertaining to requirements for the at least one data replication operation using a time series stationary stochastic model comprising at least one autoregression and moving average technique and at least one activation function; and
performing one or more automated actions based at least in part on the determined set of configuration parameters for the at least one data replication operation, wherein performing the one or more automated actions comprises:
performing a comparison of the determined set of configuration parameters for the at least one data replication operation to user-provided configuration parameters for the at least one data replication operation;
displaying, via at least one graphical user interface and based at least in part on the comparison, one or more configuration parameter changes, to the user-provided configuration parameters, recommended for the at least one data replication operation; and
updating, in at least one user data replication environment and by initiating one or more application programming interface calls, the at least one data replication operation in accordance with one or more of (i) at least one configuration parameter of the determined set of configuration parameters and (ii) at least one of the one or more configuration parameter changes;

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 applying the one or more artificial intelligence techniques comprises:
predicting, for each of multiple candidate configuration parameters, a probability of success if deployed in the at least one data replication operation by processing the configuration parameter and the at least a portion of the input data using the at least one autoregression and moving average technique; and
performing, for each of the multiple candidate configuration parameters, a binary classification by processing the predictions generated by the at least one autoregression and moving average technique using the at least one activation function.

3. The computer-implemented method of claim 1 , further comprising:
building the time series stationary stochastic model by processing historical data pertaining to multiple data replication operations.

4. The computer-implemented method of claim 3 , wherein processing the historical data pertaining to multiple data replication operations comprises performing statistical analysis of how multiple configuration parameters change over one or more periods of time.

5. The computer-implemented method of claim 4 , wherein performing the statistical analysis comprises determining a baseline value for each of the multiple configuration parameters.

6. The computer-implemented method of claim 4 , wherein performing the statistical analysis comprises determining one or more trends associated with each of the multiple configuration parameters.

7. The computer-implemented method of claim 4 , wherein performing the statistical analysis comprises determining one or more seasonality patterns associated with each of the multiple configuration parameters.

8. The computer-implemented method of claim 4 , wherein performing the statistical analysis comprises determining variability associated with each of the multiple configuration parameters.

9. The computer-implemented method of claim 1 , wherein performing the one or more automated actions comprises arranging the at least one data replication operation in accordance with the determined set of configuration parameters.

10. 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 data pertaining to at least one data replication operation, wherein the input data comprises information pertaining to requirements for the at least one data replication operation, the information pertaining to requirements for the at least one data replication operation comprising (i) transactional volume per structured dataset per at least one given temporal increment for at least one source, and (ii) a number of structured datasets subject to replication;
to determine a set of configuration parameters for the at least one data replication operation by applying one or more artificial intelligence techniques to at least a portion of the input data, wherein applying the one or more artificial intelligence techniques to at least a portion of the input data comprises processing at least a portion of the information pertaining to requirements for the at least one data replication operation using a time series stationary stochastic model comprising at least one autoregression and moving average technique and at least one activation function; and
to perform one or more automated actions based at least in part on the determined set of configuration parameters for the at least one data replication operation, wherein performing the one or more automated actions comprises:
performing a comparison of the determined set of configuration parameters for the at least one data replication operation to user-provided configuration parameters for the at least one data replication operation;
displaying, via at least one graphical user interface and based at least in part on the comparison, one or more configuration parameter changes, to the user-provided configuration parameters, recommended for the at least one data replication operation; and
updating, in at least one user data replication environment and by initiating one or more application programming interface calls, the at least one data replication operation in accordance with one or more of (i) at least one configuration parameter of the determined set of configuration parameters and (ii) at least one of the one or more configuration parameter changes.

11. The non-transitory processor-readable storage medium of claim 10 , wherein applying the one or more artificial intelligence techniques comprises:
predicting, for each of multiple candidate configuration parameters, a probability of success if deployed in the at least one data replication operation by processing the configuration parameter and the at least a portion of the input data using the at least one autoregression and moving average technique; and
performing, for each of the multiple candidate configuration parameters, a binary classification by processing the predictions generated by the at least one autoregression and moving average technique using the at least one activation function.

12. The non-transitory processor-readable storage medium of claim 10 , wherein performing the one or more automated actions comprises arranging

Assignees

• Emc Ip Holding Co Llc

Inventors

• Nandan Kasnadi Sitaram
• Kolluri Mohit
• Kumar Vinod
• Sundaram Sujay Prasheel
• Manchiraju Sarat
• Mohanty Bijan Kumar
• Dinh Hung T
• Nath Subrato
• Silvester Naveen

Classifications

• G06F3/065 Primary

  Replication mechanisms · CPC title

• G06F3/0604

  Improving or facilitating administration, e.g. storage management · CPC title

• G06F3/0673

  Single storage device · CPC title

• G06N5/04

  Inference or reasoning models · CPC title

• G06N20/00

  Machine learning · CPC title