Techniques for automatic service level agreement generation
Methods, systems, and devices for data management are described. A data management system (DMS) may receive a request to backup data from a source data storage environment to a target data storage environment. The DMS may then input first workload metadata associated with backing up the data from the source data storage environment to the target data storage environment into a machine learning model that is trained using second workload metadata associated with a set of workloads managed by the data management system. The DMS may generate, via the machine learning model and in response to the request, one or more service level agreement configurations for backing up the data. Then, the DMS may perform the backup of the data from the source data storage environment to the target data storage environment in accordance with at least one of the one or more service level agreement configurations.
1. A method for automatic service level agreement generation, comprising:
receiving, at a data management system, a request to backup data from a source data storage environment to a target data storage environment;
inputting first workload metadata associated with backing up the data from the source data storage environment to the target data storage environment into a machine learning model that is trained using second workload metadata associated with a plurality of workloads managed by the data management system;
generating, via the machine learning model and in response to the request, one or more service level agreement configurations for backing up the data from the source data storage environment to the target data storage environment, wherein generating the one or more service level agreement configurations is based at least in part on the first workload metadata; and
performing the backup of the data from the source data storage environment to the target data storage environment in accordance with at least one of the one or more service level agreement configurations.
2. The method of claim 1 , further comprising:
receiving a plurality of requests to backup data from the source data storage environment to a target data storage environment, wherein the plurality of requests correspond to the plurality of workloads; and
generating a plurality of service level agreement configurations associated with the plurality of requests.
3. The method of claim 2 , further comprising:
training the machine learning model to identify a first subset of service level agreement configurations from the plurality of service level agreement configurations based at least in part on a set of attributes.
4. The method of claim 3 , further comprising:
storing the first subset of service level agreement configurations at the data management system.
5. The method of claim 3 , wherein the set of attributes comprises at least one of a set of retention days per snapshot, a set of archival days per snapshot, replication information per snapshot, a set of cost configurations for one or more cloud environments, a plurality of snapshot sizes per service level agreement per snapshot, an industry associated with a customer, a business priority associated with the first workload, or a combination thereof.
6. The method of claim 1 , further comprising:
receiving, from an administrator, a recommended service level agreement configuration for backing up the data from the source data storage environment to the target data storage environment.
7. The method of claim 1 , further comprising:
retrieving one or more parameters associated with a workload upon receiving the request to backup data from the source data storage environment to the target data storage environment; and
performing a cost estimation for the workload based at least in part on the one or more parameters.
8. The method of claim 7 , wherein the one or more parameters comprise at least one of a plurality of workload sizes, a plurality of read trends, a plurality of write trends, a resource usage, a capacity, a plurality of licenses, or a combination thereof.
9. The method of claim 1 , further comprising:
periodically performing an assessment of the one or more service level agreement configurations; and
updating at least one of the one or more service level agreement configurations based at least in part on the assessment.
10. The method of claim 1 , wherein the machine learning model comprises a K-means clustering algorithm.
11. An apparatus for automatic service level agreement generation, comprising:
a processor;
memory coupled with the processor; and
instructions stored in the memory and executable by the processor to cause the apparatus to:
receive, at a data management system, a request to backup data from a source data storage environment to a target data storage environment;
input first workload metadata associated with backing up the data from the source data storage environment to the target data storage environment into a machine learning model that is trained using second workload metadata associated with a plurality of workloads managed by the data management system;
generate, via the machine learning model and in response to the request, one or more service level agreement configurations for backing up the data from the source data storage environment to the target data storage environment, wherein generating the one or more service level agreement configurations is based at least in part on the first workload metadata; and
perform the backup of the data from the source data storage environment to the target data storage environment in accordance with at least one of the one or more service level agreement configurations.
12. The apparatus of claim 11 , wherein the instructions are further executable by the processor to cause the apparatus to:
receive a plurality of requests to backup data from the source data storage environment to a target data storage environment, wherein the plurality of requests correspond to the plurality of workloads; and
generate a plurality of service level agreement configurations associated with the plurality of requests.
13. The apparatus of claim 12 , wherein the instructions are further executable by the processor to cause the apparatus to:
train the machine learning model to identify a first subset of service level agreement configurations from the plurality of service level agreement configurations based at least in part on a set of attributes.
14. The apparatus of claim 13 , wherein the instructions are further executable by the processor to cause the apparatus to:
store the first subset of service level agreement configurations at the data management system.
15. The apparatus of claim 13 , wherein the set of attributes comprises at least one of a set of retention days per snapshot, a set of archival days per snapshot, replication information per snapshot, a set of cost configurations for one or more cloud environments, a plurality of snapshot sizes per service level agreement per snapshot, an industry associated with a customer, a business priority associated with the first workload, or a combination thereof.
16. The apparatus of claim 11 , wherein the instructions are further executable by the processor to cause the apparatus to:
receive, from an administrator, a recommended service level agreement configuration for backing up the data from the source data storage environment to the target data storage environment.
17. The apparatus of claim 11 , wherein the instructions are further executable by the processor to cause the apparatus to:
retrieve one or more parameters associated with a workload upon receiving the request to backup data from the source data storage environment to the target data storage environment; and
perform a cost estimation for the workload based at least in part on the one or more parameters.
18. The apparatus of claim 17 , wherein the one or more parameters comprise at least one of a plurality of workload sizes, a plurality of read trends, a plurality of write trends, a resource usage, a capacity, a plurality of licenses, or a combination thereof.
19. The apparatus of claim 11 , wherein the instructions are further executable by the processor to cause the apparatus to:
periodically perform an assessment of the one or more service level agreement configurations; and
update at least one of the one or more service level agreement configurations based at least in part on the assessment.
20. A non-transitory computer-readable medium storing code for automatic service level agreement generation, the code comprising instructions executable by a processor to:
receive, at a data management system, a request to backup data from a source data storage environment to a target data storage environment;
input first workload metadata associated with backing up the data from the source data storage environment to the target data storage environment into a machine learning model that is trained using second workload metadata associated with a plurality of workloads managed by the data management system;
generate, via the machine learning model and in response to the request, one or more service level agreement configurations for backing up the data from the source data storage environment to the target data storage environment, wherein generating the one or more service level agreement configurations is based at least in part on the first workload metadata; and
perform the backup of the data from the source data storage environment to the target data storage environment in accordance with at least one of the one or more service level agreement configurations.