System and method for automating formation and execution of a backup strategy

Patent metadata

Field	Value
Publication number	US-12174706-B2
Application number	US-202117488489-A
Country	US
Kind code	B2
Filing date	Sep 29, 2021
Priority date	Nov 22, 2017
Publication date	Dec 24, 2024
Grant date	Dec 24, 2024

Abstract

Disclosed herein are systems and method for forming and executing a backup strategy. In one aspect, an exemplary method comprises forming a respective backup strategy for each respective file of a plurality of files stored in a data source based on a frequency of occurrence, a desired recovery time, and a criticality of data loss for the respective file. The method further comprises executing the respective backup strategy for the respective file.

First claim

What is claimed is:

1. A method for forming and executing a backup strategy comprising:
forming a respective backup strategy for each respective file of a plurality of files stored in a data source, by:
determining a frequency of occurrence for the respective file based on (1) metadata regarding a location of the respective file, (2) whether an authorship of the respective file matches an owner of the data source, and (3) by comparing at least a portion of the respective file to a frequency database indicating commonality of various files;
adding a secure backup strategy to the respective backup strategy for the respective file, when the frequency of occurrence is greater than a frequency of occurrence threshold;
adding a distributed backup to the respective backup strategy, when a criticality of data loss for the respective file is higher than a predetermined criticality threshold;
adding an instant copying to the respective backup strategy, when the respective file is determined as having a desired recovery time less than a predetermined recovery time threshold;
copying the respective file to a distributed backup storage device, when the instant copying is added to the backup strategy; and

executing each respective backup strategy for the plurality of files.

2. The method of claim 1 , further comprising:
generating the frequency database by inspecting files in a second data source;
hashing the files in the second data source;
associating the hashes of the files with commonality and availability values in the second data source, wherein comparing at least the portion of the respective file to the frequency database comprises comparing a hash of the respective file to the hashes of the files.

3. The method of claim 2 , wherein the second data source is the Internet.

4. The method of claim 1 , further comprising:
adding a local backup to the respective backup strategy, when the criticality of data loss is lower than the predetermined criticality threshold.

5. The method of claim 1 , further comprising:
categorizing the respective file into a hierarchy of logical types according to properties of the respective file by:
analyzing metadata associated with the respective file;
analyzing contents of the respective file; and
classifying the respective file based on the metadata associated with the respective file and the contents of the respective file; and

wherein forming the respective backup strategy for the respective file is further according to the categorization of the respective file.

6. The method of claim 1 , further comprising:
evaluating a confidentiality of the respective file prior to forming the respective backup strategy;
determining a degree of confidentiality, when the respective file is confidential;
selecting a strength of one or more encryption algorithms according to the determined degree of confidentiality; and
encrypting the respective file with the selected encryption algorithm.

7. The method of claim 1 , the respective backup strategy further being formed according to one or more of: an importance, a recovery time, and a recovery point objective of the respective file.

8. The method of claim 1 , wherein the frequency of occurrence is determined based on a deep learning analysis of one of: a local data set and an external data set, wherein the local data set comprises an archive and the external data set comprises the Internet.

9. The method of claim 8 , wherein the determination of the frequency of occurrence based on the deep learning analysis comprises one or more of:
considering the authorship of the respective file, considering the metadata regarding the location of the respective file in combination with the authorship of the respective file, and considering an availability of the respective file in the local and external data sets.

10. A system for forming and executing a backup strategy, comprising:
a hardware processor configured to:
form a respective backup strategy for each respective file of a plurality of files stored in a data source, by:
determining a frequency of occurrence for the respective file based on (1) metadata regarding a location of the respective file, (2) whether an authorship of the respective file matches an owner of the data source, and (3) by comparing at least a portion of the respective file to a frequency database indicating commonality of various files;
adding a secure backup strategy to the respective backup strategy for the respective file, when the frequency of occurrence is greater than a frequency of occurrence threshold;
adding a distributed backup to the respective backup strategy, when a criticality of data loss for the respective file is higher than a predetermined criticality threshold;
adding an instant copying to the respective backup strategy, when the respective file is determined as having a desired recovery time less than a predetermined recovery time threshold;
copying the respective file to a distributed backup storage device, when the instant copying is added to the backup strategy; and

execute each respective backup strategy for the plurality of files.

11. The system of claim 10 , wherein the hardware processor is further configured to:
generate the frequency database by inspecting files in a second data source;
hash the files in the second data source;
associate the hashes of the files with commonality and availability values in the second data source, wherein the hardware processor is further configured to compare at least the portion of the respective file to the frequency database by comparing a hash of the respective file to the hashes of the files.

12. The system of claim 11 , wherein the second data source is the Internet.

13. The system of claim 10 , wherein the hardware processor is further configured to:
add a local backup to the respective backup strategy, when the criticality of data loss is lower than the predetermined criticality threshold.

14. The system of claim 10 , wherein the hardware processor is further configured to:
categorize the respective file into a hierarchy of logical types according to properties of the respective file by:
analyzing metadata associated with the respective file;
analyzing contents of the respective file; and
classifying the respective file based on the metadata associated with the respective file and the contents of the respective file; and

wherein forming the respective backup strategy for the respective file is further according to the categorization of the respective file.

15. The system of claim 10 , wherein the hardware processor is further configured to:
evaluate a confidentiality of the respective file prior to forming the respective backup strategy;
determine a degree of confidentiality, when the respective file is confidential;
select a strength of one or more encryption algorithms according to the determined degree of confidentiality; and
encrypt the respective file with the selected encryption algorithm.

16. The system of claim 10 , the respective backup strategy further being formed according to one or more of: an importance, a recovery time, and a recovery point objective of the respective file.

17. The system of claim 10 , wherein the frequency of occurrence is determined based on a deep learning analysis of one of: a local data set and an external data set, wherein the local data set comprises an archive and the external data set comprises the Internet.

18. The system of claim 17 , wherein the determination of the frequency of occurrence based on the deep

Assignees

• Acronis Int Gmbh

Inventors

• Protasov Stanislav
• Beloussov Serguei
• Martynov Dmitry
• Ulasen Sergey

Classifications

• G06F11/1461

  Backup scheduling policy · CPC title

• G06F3/0604

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

• G06F16/122

  using management policies (point-in-time backing up or restoration of persistent data G06F11/1446; file migration policies for HSM systems G06F16/185) · CPC title

• G06F3/065

  Replication mechanisms · CPC title

• G06F3/0643

  Management of files · CPC title