Unification of disparate cloud resources using machine learning
A device launches a respective instance on each respective cloud service provider (CSP) of a plurality of CSPs. The device receives, from each respective instance, performance benchmark data for each CSP shape of the respective CSP on which the respective instance is launched. The device inputs the performance benchmark data from each respective instance into a model and receives, as output from the model, a determination of, for each CSP shape, group of a plurality of groups to which the CSP shape belongs. The device ranks each group based on a parameter, and provides for display to a user a recommended CSP shape based on the ranking.
1. A non-transitory computer-readable medium comprising instructions encoded thereon that, when executed by at least one processor, cause the at least one processor to:
launch a respective instance on each respective cloud service provider (CSP) of a plurality of CSPs, the plurality of CSPs including two or more distinct CSPs;
receive, from each respective instance, performance benchmark data for each CSP shape of the respective CSP on which the respective instance is launched;
input the performance benchmark data from each respective instance into a model;
receive, as output from the model, a determination of, for each CSP shape, group of a plurality of groups to which the CSP shape belongs;
rank each group based on a parameter; and
provide for display to a user a recommended CSP shape based on the ranking, the recommended CSP shape implemented using one of the two or more distinct CSPs.
2. The non-transitory computer-readable medium of claim 1 , wherein the model is an unsupervised machine learning model or a statistical model.
3. The non-transitory computer-readable medium of claim 1 , wherein the performance benchmark data is normalized prior to being input into the model.
4. The non-transitory computer-readable medium of claim 1 , wherein instructions to input the performance benchmark data from each respective instance into the model comprise instructions to:
reduce the performance benchmark data into a reduced data set having fewer dimensions than the performance benchmark data; and
input the reduced data set into the model.
5. The non-transitory computer-readable medium of claim 4 , wherein instructions to reduce the performance benchmark data into a reduced data set comprise instructions to perform a principal component analysis on the performance benchmark data.
6. The non-transitory computer-readable medium of claim 1 , wherein each respective instance is a respective launch instance, and further comprises instructions that when executed causes the processor to:
launch a respective testing instance on each respective cloud service provider (CSP) of the plurality of CSPs; and
generate at least a portion of the performance benchmark data using the respective testing instances.
7. The non-transitory computer-readable medium of claim 1 , wherein further comprises instructions that when executed causes the processor to:
generate for display to the user a plurality of selectable options, each selectable option corresponding to a parameter; and
receive a selection of a selectable option corresponding to a given parameter, wherein instructions to provide for display the recommended CSP shape based on the ranking further comprises instructions that when executed causes the processor to provide for display a ranked list of CSP shapes corresponding to the given parameter, the recommended CSP shape being at a top of the ranked list.
8. A method comprising:
launching a respective instance on each respective cloud service provider (CSP) of a plurality of CSPs, the plurality of CSPs including two or more distinct CSPs;
receiving, from each respective instance, performance benchmark data for each CSP shape of the respective CSP on which the respective instance is launched;
inputting the performance benchmark data from each respective instance into a model;
receiving, as output from the model, a determination of, for each CSP shape, group of a plurality of groups to which the CSP shape belongs;
ranking each group based on a parameter; and
providing for display to a user a recommended CSP shape based on the ranking, the recommended CSP shape implemented using one of the two or more distinct CSPs.
9. The method of claim 8 , wherein the model is an unsupervised machine learning model or a statistical model.
10. The method of claim 8 , wherein the performance benchmark data is normalized prior to being input into the model.
11. The method of claim 8 , wherein inputting the performance benchmark data from each respective instance into the model comprises:
reducing the performance benchmark data into a reduced data set having fewer dimensions than the performance benchmark data; and
inputting the reduced data set into the model.
12. The method of claim 11 , wherein reducing the performance benchmark data into a reduced data set comprises performing a principal component analysis on the performance benchmark data.
13. The method of claim 8 , wherein each respective instance is a respective launch instance, and wherein the method further comprises:
launching a respective testing instance on each respective cloud service provider (CSP) of the plurality of CSPs; and
generating at least a portion of the performance benchmark data using the respective testing instances.
14. The method of claim 8 , further comprising:
generating for display to the user a plurality of selectable options, each selectable option corresponding to a parameter; and
receiving a selection of a selectable option corresponding to a given parameter, wherein providing for display the recommended CSP shape based on the ranking comprises providing for display a ranked list of CSP shapes corresponding to the given parameter, the recommended CSP shape being at a top of the ranked list.
15. A system comprising:
an instance module for launching a respective instance on each respective cloud service provider (CSP) of a plurality of CSPs, the plurality of CSPs including two or more distinct CSPs;
a benchmark module for receiving, from each respective instance, performance benchmark data for each CSP shape of the respective CSP on which the respective instance is launched;
a grouping module for:
inputting the performance benchmark data from each respective instance into a model; and
receiving, as output from the model, a determination of, for each CSP shape, group of a plurality of groups to which the CSP shape belongs; and
a ranking module for:
ranking each group based on a parameter; and
providing for display to a user a recommended CSP shape based on the ranking, the recommended CSP shape implemented using one of the two or more distinct CSPs.
16. The system of claim 15 , wherein the model is an unsupervised machine learning model or a statistical model.
17. The system of claim 15 , wherein the performance benchmark data is normalized prior to being input into the model.
18. The system of claim 15 , wherein inputting the performance benchmark data from each respective instance into the model comprises:
reducing the performance benchmark data into a reduced data set having fewer dimensions than the performance benchmark data; and
inputting the reduced data set into the model.
19. The system of claim 18 , wherein reducing the performance benchmark data into a reduced data set comprises performing a principal component analysis on the performance benchmark data.
20. The system of claim 15 , wherein each respective instance is a respective launch instance, wherein the instance module is further for launching a respective testing instance on each respective cloud service provider (CSP) of the plurality of CSPs, and wherein the benchmark module is further for generating at least a portion of the performance benchmark data using the respective testing instances.