Storage context aware tiering policy advisor

Patent metadata

Field	Value
Publication number	US-11960746-B2
Application number	US-202217583275-A
Country	US
Kind code	B2
Filing date	Jan 25, 2022
Priority date	Jan 25, 2022
Publication date	Apr 16, 2024
Grant date	Apr 16, 2024

Abstract

Technology described herein can be employed to automatically recommend a tiering policy for data storage of data at a data storage system, such as a cloud storage system. An example method can comprise determining, by a system comprising a processor, context information defining a data storage attribute applicable to data at a cloud storage system. The method can comprise, in response to determining the context information, generating, by the system, a tiering policy defining an element of tiering storage of data at the cloud storage system, wherein the tiering policy is based on the data storage attribute defined by the context information. The method also can comprise, in response to generating the tiering policy, outputting, by the system, the tiering policy to a storage device associated with a customer. The analysis can be performed using an artificial intelligence process, machine learning process or a combination thereof.

First claim

What is claimed is:

1. A method, comprising:
determining, by a system comprising a processor, context information defining a data storage attribute applicable to data at a cloud storage system;
in response to determining the context information, generating, by the system, a tiering policy defining an element of tiering storage of data at the cloud storage system, wherein the tiering policy is based on the data storage attribute defined by the context information; and
in response to generating the tiering policy, outputting, by the system, the tiering policy to a storage device associated with a customer;
wherein the determining the context information comprises generating and analyzing one or more questionnaires or chatbot conversations, and
wherein the questionnaire, or the chat executed by a chatbot, is generated based on requested feedback from a user entity of the cloud storage system.

2. The method of claim 1 , further comprising:
in response to customer feedback received via a customer device, modifying, by the system, the tiering policy.

3. The method of claim 1 , wherein the element of tiering storage is an organization element, an upload requirement, or a data retention element.

4. The method of claim 1 , wherein the tiering policy comprises a threshold for the data storage attribute, and wherein the data storage attribute is applicable to a file size, a last accessed time, a last attribute changed time or a total storage capacity.

5. The method of claim 1 , wherein the determining of the context information comprises employing respective weights associated with different data storage attributes of different workloads analyzed by the system, the different data storage attributes comprising the data storage attribute, and wherein the weights are employed to determine one or more data storage attributes that define the tiering policy.

6. The method of claim 1 , further comprising:
training, by the system, a machine learning model based on historical data representing historical selections of tiering policies for corresponding historical contexts, wherein an output of an analysis of the context information using the machine learning model is employed for the generating the tiering policy setting based on the context information.

7. The method of claim 1 , wherein the context information comprises historical context information.

8. The method of claim 1 , further comprising:
repeatedly, by the system, executing the generating and the outputting the tiering policy in response to repeated customer feedback received from the customer via a customer device in response to the outputting the tiering policy.

9. A system, comprising:
a processor, and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising:
determining, using at least one of an artificial intelligence process or a machine learning process, context information defining a data storage attribute applicable to data at a cloud storage system;
in response to determining the context information, generating, using the at least one of the artificial intelligence process pr the machine learning process, a tiering policy defining an element of tiering storage of data at the cloud storage system, wherein the tiering policy is based on the data storage attribute defined by the context information; and
in response to generating the tiering policy, outputting the tiering policy to a storage device associated with a customer;
wherein the determining the context information comprises generating and analyzing, using the at least one of the artificial intelligence process or the machine learning process, one or more questionnaires or chatbot conversations.

10. The system of claim 9 , wherein the operations further comprise:
in response to customer feedback received via a customer device, modifying, using the at least one of the artificial intelligence process or the machine learning process, the tiering policy.

11. The system of claim 9 , wherein the element of tiering storage is an organization element, an upload requirement, or a data retention element.

12. The system of claim 9 , wherein the tiering policy comprises a threshold for the data storage attribute, and wherein the data storage attribute is applicable to a file size, a last accessed time, a last attribute changed time or a total storage capacity.

13. The system of claim 9 , wherein the determining of the context information comprises employing respective weights associated with different data storage attributes of different workloads analyzed by the at least one of the artificial intelligence process or the machine learning process, the different data storage attributes comprising the data storage attribute, and wherein the weights are employed to determine one or more data storage attributes that define the tiering policy.

14. The system of claim 9 , wherein the operations further comprise:
training a machine learning model based on historical data representing historical selections of tiering policies for corresponding historical contexts,
wherein an output of an analysis of the context information using the machine learning model is employed for the generating the tiering policy setting based on the context information, and
wherein the at least one of the artificial intelligence process or the machine learning process employs the machine learning model.

15. The system of claim 9 , wherein the context information comprises historical context information.

16. The system of claim 9 , wherein the operations further comprise:
repeatedly executing the generating and the outputting the tiering policy in response to repeated customer feedback received from the customer via a customer device in response to the outputting the tiering policy.

17. The system of claim 9 , wherein the questionnaire, or the chat executed by a chatbot, is generated, using the at least one of the artificial intelligence process or the machine learning process, based on requested feedback corresponding to a user entity of the cloud storage system.

18. A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor facilitate performance of operations, comprising:
determining context information defining a data storage attribute applicable to data at a cloud storage system;
in response to determining the context information, generating a tiering policy defining an element of tiering storage of data at the cloud storage system, wherein the tiering policy is based on the data storage attribute defined by the context information; and
in response to generating the tiering policy, outputting the tiering policy to a storage device associated with a customer;
wherein the determining the context information comprises generating and analyzing one or more questionnaires or chatbot conversations, and
wherein the questionnaire, or chat executed by a chatbot, is generated based on requested feedback from a user entity of the cloud storage system.

Assignees

• Dell Products Lp

Inventors

• Talukdar Rohit
• Ms Rekha
• John Joji
• Mishra Pradyot

Classifications

• G06F3/0646 Primary

  Horizontal data movement in storage systems, i.e. moving data in between storage devices or systems · CPC title

• G06F3/0605

  by facilitating the interaction with a user or administrator · CPC title

• G06F3/067

  Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS] · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• G06F3/0649

  Lifecycle management · CPC title