Data recovery modification based on performance data exhibited by a network of data centers and data recovery requirement
Techniques regarding adaptive data recovery schemes are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a data management component that can modify a data recovery scheme based on performance data exhibited by a network of data centers and a data recovery requirement. The data recovery scheme can direct a relocation of data within the network.
1. A system, comprising:
a memory that stores computer executable components; and
a processor, operably coupled to the memory, and that executes at least one of the computer executable components that:
generates a machine learning model to optimize relocation of data based on a group of microservice applications, wherein the machine learning model identifies a relationship between a modification made to a data recovery scheme and a data recovery requirement based on performance data of the group of microservice applications.
2. The system of claim 1 , wherein the performance data comprises at least one member selected from a group consisting of location of data centers, bandwidth of a network, latency of the network, bandwidth used by the data, latency exhibited during execution of the data, and latency exhibited during relocation of the data.
3. The system of claim 2 , wherein the data recovery requirement comprises at least one second member selected from a second group consisting of the location of the data centers and a service level objective.
4. The system of claim 1 , wherein the at least one of the computer executable components further:
modifies the data recovery scheme to generate a modified data recovery scheme;
analyzes the modified data recovery scheme to determine whether the modified data recovery scheme is predicted to achieve improved performance data and meet the data recovery requirement in comparison to the data recovery scheme; and
performs the relocation of the data in accordance with the modified data recovery scheme based on a determination that the modified data recovery scheme is predicted to improve the performance data and meet the data recovery requirement.
5. The system of claim 4 , wherein the machine learning model identifies a second relationship between the modification to the data recovery scheme and the data recovery requirement based on the performance data of the group of microservice applications.
6. The system of claim 5 , wherein the performance data further regards at least one data center of a network, and wherein the at least one of the computer executable components further:
generates the machine learning model to optimize the relocation of the data based further on the at least one data center, wherein the machine learning model identifies a third relationship between a second modification made to the data recovery scheme and the data recovery requirement based on the performance data of the at least one data center.
7. A system, comprising:
a memory that stores computer executable components; and
a processor that executes at least one of the computer executable components that:
generates a machine learning model to determine a modification to a data recovery scheme based on performance data exhibited by a network of data centers and a data recovery requirement, wherein the data recovery scheme directs a relocation of data within the network; and
generates a first portion of the machine learning model to optimize the relocation of the data based on the performance data of a group of microservice applications within the data.
8. The system of claim 7 , wherein the performance data comprises at least one member selected from a group consisting of location of the data centers, bandwidth of the network, latency of the network, bandwidth used by the data, latency exhibited during execution of the data, and latency exhibited during the relocation of the data, and wherein the data recovery requirement comprises at least one second member selected from a second group consisting of the location of the data centers and a service level objective.
9. The system of claim 7 , wherein the at least one of the computer executable components further:
generates a second portion of the machine learning model to optimize the relocation of the data based on the performance data of at least one data center of the network.
10. The system of claim 7 , wherein the at least one of the computer executable components further:
performs the relocation of the data in accordance with the modification to the data recovery scheme based on a determination that the modification is predicted to improve performance data and meet the data recovery requirement.
11. The system of claim 7 , wherein the machine learning model identifies a relationship between the modification to the data recovery scheme and the data recovery requirement based on the performance data.
12. The system of claim 7 , wherein the at least one of the computer executable components further;
implements the modification to generate a modified data recovery scheme, and
analyzes the modified data recovery scheme to determine whether the modified data recovery scheme is predicted to achieve improved performance data and meet the data recovery requirement in comparison to the data recovery scheme.
13. A computer-implemented method, comprising:
generating, by a system operatively coupled to a processor, a machine learning model to determine a modification to a data recovery scheme based on performance data exhibited by a network of data centers and a data recovery requirement, wherein the data recovery scheme directs a relocation of data within the network;
generating, by the system, a first portion of the machine learning model to optimize the relocation of the data based on the performance data of a group of microservice applications within the data; and
performing, by the system, the relocation of the data in accordance with the modification to the data recovery scheme based on a determination that the modification is predicted to improve performance data and meet the data recovery requirement.
14. The computer-implemented method of claim 13 , wherein the performance data comprises at least one member selected from a group consisting of location of the data centers, bandwidth of the network, latency of the network, bandwidth used by the data, latency exhibited during execution of the data, and latency exhibited during the relocation of the data, and wherein the data recovery requirement comprises at least one second member selected from a second group consisting of the location of the data centers and a service level objective.
15. The computer-implemented method of claim 13 , further comprises:
generating, by the system, a second portion of the machine learning model to optimize the relocation of the data based further on the performance data of at least one data center of the network.
16. The computer-implemented method of claim 15 , wherein the modification generates a modified data recovery scheme, and wherein the computer-implemented method further comprises:
analyzing, by the system, the modified data recovery scheme to determine whether the modified data recovery scheme is predicted to achieve improved performance data and meet the data recovery requirement in comparison to the data recovery scheme.
17. A computer-implemented method, comprising:
removing, by a system, a secondary data center candidate from a network of data centers based on performance data, wherein the performance data regards a group of microservice applications comprised within the performance data and at least one data center of the network; and
generating, by the system, a machine learning model to optimize relocation of the data based on the group of microservice applications.
18. The computer-implemented method of claim 17 , wherein the performance data comprises at least one member selected from a group consisting of location of the data centers, bandwidth of the network, latency of the network, bandwidth used by the data, latency exhibited during execution of the data, and latency exhibited during the relocation of the data, and wherein the data recovery requirement comprises at least one second member selected from a second group consisting of the location of the data centers and a service level objective.
19. The computer-implemented method of claim 17 , wherein the machine learning model identifies a relationship between a modification made to a data recovery scheme and a data recovery requirement based on the performance data regarding the group of microservice applications.
20. The computer-implemented method of claim 19 , wherein the performance data further regards the at least one data center, and wherein the computer-implemented method further comprises:
generating, by the system, a second machine learning model to optimize the relocation of the data based further on the at least one data center.