Dynamic replication service execution using data change metrics

US2025021578A1 · US · A1

Patent metadata
FieldValue
Publication numberUS-2025021578-A1
Application numberUS-202318352274-A
CountryUS
Kind codeA1
Filing dateJul 14, 2023
Priority dateJul 14, 2023
Publication dateJan 16, 2025
Grant date

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  1. Title

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  2. Abstract

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  5. First independent claim

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Abstract

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A dynamic replication service using a data change metric to select an optimum cloning method that reduces latency of data copying. A model is trained using historical data of backup operations of the saveset to establish past data change metrics for corresponding replication services processing the saveset. The best cloning method for the replication service is selected by using a calculated data change rate of the data saveset, as expressed as a number of bytes changed per unit of time, from among a plurality of different cloning methods based on the data change rate. The service executes the selected cloning method for the replication service to copy the data for storage or further processing.

First claim

Opening claim text (preview).

What is claimed is: 1 . A computer-implemented method of selecting a cloning method for a replication service in a data protection system, the method comprising: receiving the data of a saveset utilizing the replication service; determining a data change rate of the data, as expressed as a number of bytes changed per unit of time; selecting the cloning method from among a plurality of different cloning methods based on the data change rate; and executing the selected cloning method for the replication service to copy the data for storage or further processing. 2 . The method of claim 1 wherein each of the plurality of different cloning methods performs a respective data replication operation at a defined time relative to a backup operation. 3 . The method of claim 2 wherein the plurality of different cloning methods comprises: parallel replication, serial replication, scheduled replication, manual replication, saveset replication, and volume replication. 4 . The method of claim 3 wherein the parallel replication copies the data in parallel with the backup operation, the serial replication copies the data after the backup operation, the scheduled replication copies the data at a scheduled time, the manual replication copies the data upon a manual trigger, the saveset replication copies the data saveset within a defined time range, and the volume replication copies a volume of the data saveset within the defined time range. 5 . The method of claim 2 wherein each of the different cloning methods initiates the data copying at a respective defined time that imposes a corresponding latency to the replication operation. 6 . The method of claim 5 wherein the selected cloning method minimizes an overall latency of the replication operation relative to other cloning methods of the plurality of cloning methods. 7 . The method of claim 6 further comprising training a model using historical data of backup operations of the saveset to establish past data change metrics for corresponding replication services processing the saveset. 8 . The method of claim 7 wherein the model utilizes an artificial intelligence (AI) based component comprising a data collection component, a training component, and an inference component, and contains historical information regarding data objects and clients of the network to continuously train a machine learning (ML) algorithm to determine an optimal cloning method as the selected cloning method. 9 . The method of claim 8 wherein the network comprises a PowerProtect Data Domain deduplication backup system. 10 . A system for selecting a cloning method for a replication service in a data protection system, the method comprising: an interface receiving the data of a saveset utilizing the replication service; determining a data change rate of the data, as expressed as a number of bytes changed per unit of time; an estimator component selecting the cloning method from among a plurality of different cloning methods based on the data change rate; and a replication engine executing the selected cloning method for the replication service to copy the data for storage or further processing. 11 . The system of claim 10 wherein each of the plurality of different cloning methods performs a respective data replication operation at a defined time relative to a backup operation, and further wherein the plurality of different cloning methods comprises: parallel replication, serial replication, scheduled replication, manual replication, saveset replication, and volume replication. 12 . The system of claim 11 wherein the parallel replication copies the data in parallel with the backup operation, the serial replication copies the data after the backup operation, the scheduled replication copies the data at a scheduled time, the manual replication copies the data upon a manual trigger, the saveset replication copies the data saveset within a defined time range, and the volume replication copies a volume of the data saveset within the defined time range. 13 . The system of claim 12 wherein each of the different cloning methods initiates the data copying at a respective defined time that imposes a corresponding latency to the replication operation. 14 . The system of claim 13 wherein the selected cloning method minimizes an overall latency of the replication operation relative to other cloning methods of the plurality of cloning methods. 15 . The system of claim 14 further comprising a model trained using historical data of backup operations of the saveset to establish past data change metrics for corresponding replication services processing the saveset. 16 . The system of claim 15 wherein the model utilizes an artificial intelligence (AI) based component comprising a data collection component, a training component, and an inference component, and contains historical information regarding data objects and clients of the network to continuously train a machine learning (ML) algorithm to determine an optimal cloning method as the selected cloning method. 17 . The system of claim 16 wherein the network comprises a PowerProtect Data Domain deduplication backup system. 18 . A tangible computer program product having stored thereon program instructions that, when executed by a process, cause the processor to perform a method of selecting a cloning method for a replication service in a data protection system, the method comprising: receiving the data of saveset utilizing the replication service; determining a data change rate of the data, as expressed as a number of bytes changed per unit of time; selecting the cloning method from among a plurality of different cloning methods based on the data change rate; and executing the selected cloning method for the replication service to copy the data for storage or further processing. 19 . The product of claim 18 wherein each of the plurality of different cloning methods performs a respective data replication operation at a defined time relative to a backup operation. 20 . The product of claim 19 wherein the plurality of different cloning methods comprises: parallel replication, serial replication, scheduled replication, manual replication, saveset replication, and volume replication.

Assignees

Inventors

Classifications

  • using de-duplication of the data · CPC title

  • Using snapshots, i.e. a logical point-in-time copy of the data · CPC title

  • G06F16/27Primary

    Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor · CPC title

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What does patent US2025021578A1 cover?
A dynamic replication service using a data change metric to select an optimum cloning method that reduces latency of data copying. A model is trained using historical data of backup operations of the saveset to establish past data change metrics for corresponding replication services processing the saveset. The best cloning method for the replication service is selected by using a calculated da…
Who is the assignee on this patent?
Dell Products Lp
What technology area does this patent fall under?
Primary CPC classification G06F16/27. Mapped technology areas include Physics.
When was this patent published?
Publication date Thu Jan 16 2025 00:00:00 GMT+0000 (Coordinated Universal Time) (A1). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 7 related publications on this page (citations in our corpus or others sharing the same primary CPC).