Data reduction improvement using aggregated machine learning
A method system, and computer program product for improving data reduction using aggregate machine learning systems comprising receiving, by an aggregating machine learning system from one or more machine learning systems associated with a set of one or more storage arrays, a first set of output parameters indicative of performance metrics for the set of the one or more storage arrays, aggregating, by the aggregating machine learning system, the first set of output parameters, resulting in a second set of output parameters, and sending, from the aggregating machine learning system, at least one member of the second set of output parameters as an input to at least one of the one or more machine learning systems associated with the set of the one or more storage arrays.
1. A method, comprising:
receiving, by an aggregating machine learning system from one or more machine learning systems associated with a set of one or more storage arrays, a first set of output parameters indicative of performance metrics for the set of the one or more storage arrays;
aggregating, by the aggregating machine learning system, the first set of output parameters, resulting in a second set of output parameters; and
sending, from the aggregating machine learning system, at least one member of the second set of output parameters as an input to at least one of the one or more machine learning systems associated with the set of the one or more storage arrays.
2. The method of claim 1 , wherein the first set of output parameters include one or more of compression rate, deduplication rate, and aggregate data reduction rate.
3. The method of claim 1 , wherein the set of the one or more storage arrays include storage arrays of at least two different types.
4. The method of claim 1 , wherein the at least one of the values of the first set of output parameters reflects actual behavior of one or more applications associated with the set of the one or more storage arrays.
5. The method of claim 1 , wherein the first set of output parameters reflect an estimation by the one or more machine learning systems associated with the set of one or more storage arrays of the performance of the set of one or more storage arrays.
6. The method of claim 1 , further comprising:
adding an additional storage array to the set of the one or more storage arrays, wherein the additional storage array is associated with an additional machine learning system, wherein the additional machine learning system has an additional set of output parameters indicative of performance metrics for the associated additional storage array; and
preventing the aggregating machine learning system from aggregating the additional set of output parameters until the at least one of the additional output parameters from the additional set of output parameters reaches a threshold condition.
7. The method of claim 1 , further comprising:
sending, from the aggregating machine learning system, at least one member of the second set of output parameters to a database.
8. A system, comprising:
an aggregating machine learning system; and
computer-executable program logic operating in memory, wherein the computer executable logic program enables execution across one or more processors of:
receiving, by the aggregating machine learning system from one or more machine learning systems associated with a set of one or more storage arrays, a first set of output parameters indicative of performance metrics for the set of the one or more storage arrays;
aggregating, by the aggregating machine learning system, the first set of output parameters, resulting in a second set of output parameters; and
sending, from the aggregating machine learning system, at least one member of the second set of output parameters as an input to at least one of the one or more machine learning systems associated with the set of the one or more storage arrays.
9. The system of claim 8 , wherein the first set of output parameters include one or more of compression rate, deduplication rate, and aggregate data reduction rate.
10. The system of claim 8 , wherein the set of the one or more storage arrays include storage arrays of at least two different types.
11. The system of claim 8 , wherein the at least one of the values of the first set of output parameters reflects actual behavior of one or more applications associated with the set of the one or more storage arrays.
12. The system of claim 8 , wherein the first set of output parameters reflect an estimation by the one or more machine learning systems associated with the set of one or more storage arrays of the performance of the set of one or more storage arrays.
13. The system of claim 8 , wherein the computer executable logic program further enables execution across the one or more processors of:
adding an additional storage array to the set of the one or more storage arrays, wherein the additional storage array is associated with an additional machine learning system, wherein the additional machine learning system has an additional set of output parameters indicative of performance metrics for the associated additional storage array; and
preventing the aggregating machine learning system from aggregating the additional set of output parameters until the at least one of the additional output parameters from the additional set of output parameters reaches a threshold condition.
14. The system of claim 8 , wherein the computer executable logic program further enables execution across the one or more processors of:
sending, from the aggregating machine learning system, at least one member of the second set of output parameters to a database.
15. A computer program product, comprising:
a non-transitory computer readable medium encoded with computer executable program code, the code enabling execution across one or more processors of:
receiving, by an aggregating machine learning system from one or more machine learning systems associated with a set of one or more storage arrays, a first set of output parameters indicative of performance metrics for the set of the one or more storage arrays;
aggregating, by the aggregating machine learning system, the first set of output parameters, resulting in a second set of output parameters; and
sending, from the aggregating machine learning system, at least one member of the second set of output parameters as an input to at least one of the one or more machine learning systems associated with the set of the one or more storage arrays.
16. The computer program product of claim 15 , wherein the first set of output parameters include one or more of compression rate, deduplication rate, and aggregate data reduction rate.
17. The computer program product of claim 15 , wherein the set of the one or more storage arrays include storage arrays of at least two different types.
18. The computer program product of claim 15 , wherein the at least one of the values of the first set of output parameters reflects actual behavior of one or more applications associated with the set of the one or more storage arrays.
19. The computer program product of claim 15 , wherein the first set of output parameters reflect an estimation by the one or more machine learning systems associated with the set of one or more storage arrays of the performance of the set of one or more storage arrays.
20. The computer program product of claim 15 , the code is further enabling execution across the one or more processors of:
adding an additional storage array to the set of the one or more storage arrays, wherein the additional storage array is associated with an additional machine learning system, wherein the additional machine learning system has an additional set of output parameters indicative of performance metrics for the associated additional storage array; and
preventing the aggregating machine learning system from aggregating the additional set of output parameters until the at least one of the additional output parameters from the additional set of output parameters reaches a threshold condition.