Systems and methods to achieve effective streaming of data blocks in data backups

Patent metadata

Field	Value
Publication number	US-2021286678-A1
Application number	US-202016814671-A
Country	US
Kind code	A1
Filing date	Mar 10, 2020
Priority date	Mar 10, 2020
Publication date	Sep 16, 2021
Grant date	—

Abstract

Methods and systems for block-level data prioritization during a backup operation are disclosed. According to some embodiments, the method includes extracting a backup data criticality from header information of one or more data blocks. The method further includes based on the extracted backup data criticality, assigning a weighted value corresponding to the backup data criticality. The method further includes for each data block, calculating a Euclidean distance of the data block to a consecutive data block using the weighted value.

First claim

What is claimed is:

1 . A computer-implemented method for block-level data prioritization during a backup operation, the method comprising:
extracting a backup data criticality from header information of one or more data blocks; based on the extracted backup data criticality, assigning a weighted value corresponding to the backup data criticality; and for each data block, calculating a Euclidean distance of the data block to a consecutive data block using the weighted value.

2 . The method of claim 1 , further comprising prior to extracting the backup data criticality from the header information of the one or more data blocks,
sniffing data object information of a data object; determining the backup data criticality based on the data object information; and appending the backup data criticality to the header information of the one or more data blocks.

3 . The method of claim 1 , further comprising evaluating the one or more data blocks to identify at least one dependent data block associated with a parent block, wherein the one or more data blocks comprise the parent block and the at least one dependent data block.

4 . The method of claim 1 , wherein calculating the Euclidean distance of the data block to the consecutive data block using the weighted value comprises obtaining a dot product of the weighted value and a sequence of the data block in a queue.

5 . The method of claim 1 , further comprising
sorting the one or more data blocks based on the calculated Euclidean distance of each data block; and routing the sorted one or more data blocks to a stream buffer in a concurrent fashion for streaming operations.

6 . The method of claim 2 , wherein the data object information includes a data type.

7 . The method of claim 5 , wherein sorting the one or more data blocks comprises selecting a nearest and most critical data block to be routed for backup based on the calculated Euclidean distance of the nearest and most critical data block to a current data block.

8 . A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations, the operations comprising:
extracting a backup data criticality from header information of one or more data blocks; based on the extracted backup data criticality, assigning a weighted value corresponding to the backup data criticality; and for each data block, calculating a Euclidean distance of the data block to a consecutive data block using the weighted value.

9 . The non-transitory machine-readable medium of claim 8 , wherein the operations further comprise prior to extracting the backup data criticality from the header information of the one or more data blocks,
sniffing data object information of a data object; determining the backup data criticality based on the data object information; and appending the backup data criticality to the header information of the one or more data blocks.

10 . The non-transitory machine-readable medium of claim 8 , wherein the operations further comprise evaluating the one or more data blocks to identify at least one dependent data block associated with a parent block, wherein the one or more data blocks comprise the parent block and the at least one dependent data block.

11 . The non-transitory machine-readable medium of claim 8 , wherein calculating the Euclidean distance of the data block to the consecutive data block using the weighted value comprises obtaining a dot product of the weighted value and a sequence of the data block in a queue.

12 . The non-transitory machine-readable medium of claim 8 , wherein the operations further comprise:
sorting the one or more data blocks based on the calculated Euclidean distance of each data block; and routing the sorted one or more data blocks to a stream buffer in a concurrent fashion for streaming operations.

13 . The non-transitory machine-readable medium of claim 9 , wherein the data object information includes a data type.

14 . The non-transitory machine-readable medium of claim 12 , wherein sorting the one or more data blocks comprises selecting a nearest and most critical data block to be routed for backup based on the calculated Euclidean distance of the nearest and most critical data block to a current data block.

15 . A data processing system, comprising:
a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations, the operations including: extracting a backup data criticality from header information of one or more data blocks; based on the extracted backup data criticality, assigning a weighted value corresponding to the backup data criticality; and for each data block, calculating a Euclidean distance of the data block to a consecutive data block using the weighted value.

16 . The data processing system of claim 15 , wherein the operations further include prior to extracting the backup data criticality from the header information of the one or more data blocks,
sniffing data object information of a data object; determining the backup data criticality based on the data object information; and appending the backup data criticality to the header information of the one or more data blocks.

17 . The data processing system of claim 15 , wherein the operations further include evaluating the one or more data blocks to identify at least one dependent data block associated with a parent block, wherein the one or more data blocks comprise the parent block and the at least one dependent data block.

18 . The data processing system of claim 15 , wherein calculating the Euclidean distance of the data block to the consecutive data block using the weighted value comprises obtaining a dot product of the weighted value and a sequence of the data block in a queue.

19 . The data processing system of claim 15 , wherein the operations further include:
sorting the one or more data blocks based on the calculated Euclidean distance of each data block; and routing the sorted one or more data blocks to a stream buffer in a concurrent fashion for streaming operations.

20 . The data processing system of claim 19 , wherein sorting the one or more data blocks comprises selecting a nearest and most critical data block to be routed for backup based on the calculated Euclidean distance of the nearest and most critical data block to a current data block.

Assignees

• Emc Ip Holding Co Llc

Inventors

• Reddy A V Mahesh
• Shankar Dh Swaroop
• Battal Chetan

Classifications

• G06F11/1448 Primary

  Management of the data involved in backup or backup restore · CPC title

• G06F11/1464

  for networked environments · CPC title

• G06F11/1451 Primary

  by selection of backup contents · CPC title

• G06F11/3006

  where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems (multiprogramming arrangements G06F9/46; allocation of resources G06F9/50) · CPC title

• G06F11/1461

  Backup scheduling policy · CPC title