Machine learning for database migration source
First Claim
1. A method to maintain data store performances upon transfer between cloud computing environments, the method comprising:
- submitting data requests to an original data store at a source datacenter;
submitting the data requests to a filter to record and analyze;
creating a new key value structure at a new data store at a target datacenter based on results of the requests to the original data store and analyses by the filter that employs machine learning;
assigning a score to each original data store performance and each new data store performance;
collecting scores of original data store performances and scores of new data store performances for a query and a corresponding key value structure at the new data store;
comparing the scores of the original data store performances and the scores of the new data store performances; and
in response to a determination that the scores of the original data store performances are not substantially equal to the scores of the new data store performances, discarding the corresponding key value structure.
3 Assignments
0 Petitions
Accused Products
Abstract
Technologies are generally provided for maintaining performance level of a database being migrated between different cloud-based service providers employing machine learning. In some examples, data requests submitted to an original data store/database may be submitted to a machine learning-based filter for recording and analysis. Based on the results of the data requests and the filter analyses, new key value structures for a new data store/database may be created. The filter may assign performance scores to the original data requests (made to the original data store) and data requests made to the newly-created key value structures. The filter may then compare the performance scores associated with the created key value structures to each other and to performance scores associated with the original data requests and may select the created key value structures with performance scores that are at least substantially equal to those of the original data requests for the new data store.
63 Citations
17 Claims
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1. A method to maintain data store performances upon transfer between cloud computing environments, the method comprising:
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submitting data requests to an original data store at a source datacenter; submitting the data requests to a filter to record and analyze; creating a new key value structure at a new data store at a target datacenter based on results of the requests to the original data store and analyses by the filter that employs machine learning; assigning a score to each original data store performance and each new data store performance; collecting scores of original data store performances and scores of new data store performances for a query and a corresponding key value structure at the new data store; comparing the scores of the original data store performances and the scores of the new data store performances; and in response to a determination that the scores of the original data store performances are not substantially equal to the scores of the new data store performances, discarding the corresponding key value structure. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A computing device to maintain data store performances upon transfer between cloud computing environments, the computing device comprising:
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a memory configured to store instructions; and a processing unit configured to execute a migration module in conjunction with the instructions, wherein the migration module is configured to; submit data requests to an original data store at a source datacenter; submit the data requests to a filter to record and analyze, wherein the filter is further configured to abstract each request to the original data store; and create a new key value structure at a new data store at a target datacenter based on results of the requests to the original data store and analyses by the filter that employs machine learning; assign a score to each original data store performance and each new data store performance; collect scores of original data store performances and scores of new data store performances for a query and a corresponding key value structure at the new data store; compare the scores of the original data store performances and the scores of the new data store performances; and in response to a determination that the scores of the original data store performances are not substantially equal to the scores of the new data store performances, discard the corresponding key value structure. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14, 15)
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16. A non-transitory computer-readable storage medium having instructions stored thereon to maintain data store performances upon transfer between cloud computing environments, the instructions comprising:
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submitting data requests to an original data store at a source datacenter; submitting the data requests to a filter to record and analyze; creating a new key value structure at a new data store at a target datacenter based on results of the requests to the original data store and analyses by the filter that employs machine learning; and assigning a score to each original data store performance and each new data store performance; collecting scores of original data store performances and scores of new data store performances for a query and a corresponding key value structure at the new data store; comparing the scores of the original data store performances and the scores of the new data store performances; and in response to a determination that the scores of the original data store performances are not substantially equal to the scores of the new data store performances, discarding the corresponding key value structure. - View Dependent Claims (17)
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Specification