Facilitating automatic enabled replication for detected storage context in advanced communication networks

Patent metadata

Field	Value
Publication number	US-12488019-B2
Application number	US-202418651192-A
Country	US
Kind code	B2
Filing date	Apr 30, 2024
Priority date	Apr 30, 2024
Publication date	Dec 2, 2025
Grant date	Dec 2, 2025

Abstract

Facilitating auto enable replication for detected storage context in advanced communication networks is described. An example method includes determining, by a system comprising at least one processor, that data stored in a source system is to be replicated to a target system. The method also includes, based on historical data related to a storage context, determining, by the system, a replication policy for a replication of the data to the target system. The replication policy can include at least one rule utilized to replicate the data to a remote system. Further, the method includes, based on an acceptance of the replication policy, facilitating, by the system, the replication of the data to the target system according to the replication policy. In an example, the storage context can include a combination of information indicative of one or more parameters used to define the replication policy.

First claim

What is claimed is:

1 . A method, comprising:
determining, by a system comprising at least one processor, that data, stored in a source system, is to be replicated to a target system; assigning, by the system, respective weights to replication policy parameters, wherein the assigning comprises, based on a determination that two replication policy parameters of an identified policy comprise a same weight, selecting a replication policy parameter of the two replication policy parameters determined to have a higher parameter value than the other replication policy parameter of the two replication policy parameters and setting the weight of the identified policy as a respective weight of the identified policy; based on historical data related to a storage context and the respective weights, determining, by the system, a replication policy for a replication of the data to the target system, wherein the replication policy comprises at least one rule utilized to replicate the data to a remote system; and based on an acceptance of the replication policy, facilitating, by the system, the replication of the data to the target system according to the replication policy.

2 . The method of claim 1 , further comprising:
prior to the determining of the replication policy, receiving, by the system, user reinforced feedback data associated with a previous replication of data from the source system to the target system or another target system, wherein the determining of the replication policy comprises determining the replication policy based on the historical data and the user reinforced feedback data.

3 . The method of claim 1 , wherein the determining of the replication policy comprises:
training, by the system employing artificial intelligence, a model using historical storage array data as training input, wherein the training comprises training the model to a defined confidence level, resulting in a trained model; and based on the trained model, facilitating, by the system, implementation of a storage context aware replication policy.

4 . The method of claim 3 , further comprising:
based on the facilitating of the implementation of the storage context aware replication policy, receiving, by the system, feedback data; and retraining, by the system, the model based on the feedback data, resulting in a retrained model.

5 . The method of claim 1 ,
wherein the respective weights identify user preferences.

6 . The method of claim 1 , wherein the determining of the replication policy comprises determining a recovery point objective for the replication.

7 . The method of claim 1 , wherein the determining of the replication policy comprises selecting the target system from a group of target systems, and wherein the selecting is based on the storage context of the target system as compared to other storage contexts of the other target systems of the group of target systems.

8 . The method of claim 1 , wherein the determining of the replication policy comprises selecting a default policy based on a predefined replication policy metric.

9 . The method of claim 1 , further comprising:
based on a rejection of the replication policy, facilitating, by the system, customization of the replication policy.

10 . The method of claim 1 , wherein the storage context comprises a combination of information comprising parameter information indicative of at least one parameter used to define the replication policy.

11 . A system, comprising:
at least one processor; and at least one memory that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations, comprising:
determining that data, stored in a source system, is scheduled for replication to a target system;
setting respective weights to respective policy parameters, wherein based on a determination that a policy is assigned two policy parameters having a same weight,
selecting a policy parameter of the two policy parameters determined to have a higher parameter value as compared to the other policy parameter of the two policy parameters, resulting in a selected policy parameter, and
setting the weight of the selected policy parameter as a respective weight of the policy;

based on historical storage context data and the respective weights, determining a replication policy for the replication of the data to the target system; and
based on acceptance of the replication policy, causing the replication of the data to the target system.

12 . The system of claim 11 , wherein the operations further comprise:
prior to the determining of the replication policy, receiving user reinforced feedback data associated with a previous replication of data from the source system to the target system or another target system, and wherein the determining of the replication policy comprises determining the replication policy based on the storage context data and the user reinforced feedback data.

13 . The system of claim 11 , wherein the determining of the replication policy comprises:
based on employing historical storage array data as training input, training via artificial intelligence, a model to a defined confidence level, resulting in a trained model; and based on the trained model, causing a storage context aware replication policy to be implemented.

14 . The system of claim 13 , wherein the operations further comprise:
based on the causing of the storage context aware replication policy to be implemented, receiving feedback data; and retraining the model based on the feedback data, resulting in a retrained model.

15 . The system of claim 11 , wherein the determining of the replication policy comprises selecting the target system from a group of target systems, and wherein the selecting is based on the historical storage context data of the target system.

16 . The system of claim 11 ,
wherein the respective weights identify user preferences.

17 . The system of claim 11 , wherein the historical storage context data comprises information indicative of parameters used to define the replication policy.

18 . A non-transitory machine-readable medium, comprising executable instructions that, when executed by at least one processor of network equipment, facilitate performance of operations, wherein the operations comprise:
determining that data, stored in a source system, is to be replicated to a target system; assigning respective weights to replication policy parameters, wherein the assigning comprises, based on a determination that two replication policy parameters of a defined policy comprise a same weight, selecting a replication policy parameter of the two replication policy parameters determined to have a higher parameter value than the other replication policy parameter of the two replication policy parameters and setting the weight of the defined policy as a respective weight of the defined policy; based on historical data related to a storage context and the respective weights, determining a replication policy for a replication of the data to the target system; and based on an acceptance of the replication policy, facilitating the replication of the data to the target system according to the replication policy.

19 . The non-transitory machine-readable medium of claim 18 , wherein the determining of the replication policy comprises:
training, using machine learning, a model using historical storage array data as training input, wherein the training comprises training the model to a defined confi

Assignees

• Dell Products Lp

Inventors

• Shashidhar Rashmi
• Akash Bathulwar
• Ms Rekha

Classifications

• G06F16/1844

  Management specifically adapted to replicated file systems · CPC title

• G06F16/27 Primary

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