System and method for storage optimization using machine learning- driven application and workload identification

1. A computer-implemented method, executed on a computing device, comprising:

receiving a first set of input/output (IO) requests for one or more storage objects stored on a storage array;

extracting one or more IO properties from the first set of IO requests;

associating metadata with the one or more storage objects using one or more machine learning models based upon, at least in part, the one or more IO properties extracted from the first set of IO requests, thus defining storage object metadata,

wherein associating the storage object metadata with the one or more storage objects includes associating temporal storage object metadata with the one or more storage objects, wherein the temporal storage object metadata indicates a period of time for which a particular temporal storage object metadata is relevant for a particular storage object, and

wherein associating the storage object metadata with the one or more storage objects includes associating spatial IO processing metadata with the one or more storage objects, wherein the spatial IO processing metadata indicates how the particular storage object is being apportioned into multiple sub-portions for utilizing the multiple sub-portions in different applications and for distinct workflows;

enabling one or more IO processing rules based upon, at least in part, the storage object metadata;

receiving a subsequent set of IO requests, wherein in response to receiving the subsequent set of IO requests, identifying the storage object metadata associated with the one or more storage objects targeted by the subsequent set of IO requests;

defining the one or more IO processing rules for optimizing processing of the subsequent set of IO requests on the one or more storage objects based upon, at least in part, the storage object metadata associated with the one or more storage objects and monitoring the one or more storage objects before, during, and after the processing of the first set of IO requests and the subsequent set of IO requests;

enabling storage optimization of the processing of the subsequent set of IO requests on the one or more storage objects based upon, at least in part, the storage object metadata and the one or more IO processing rules associated with the one or more storage objects;

dynamically adjusting values of one or more of the storage object metadata and the one or more IO processing rules based upon, at least in part, the processing of the subsequent IO requests on the one or more storage objects; and

updating IO processing rules for new IO requests based on new learnings and new storage object metadata to optimize and automate the storage array.

2. The computer-implemented method of claim 1 , wherein the one or more storage objects include at least one of:

one or more volumes;

one or more files; and

one or more Logical Unit Numbers (LUNs).

3. The computer-implemented method of claim 1 , wherein associating the storage object metadata with the one or more storage objects includes:

associating spatial storage object metadata with the one or more storage objects.

4. The computer-implemented method of claim 1 , wherein associating the storage object metadata with the one or more storage objects includes:

receiving one or more user-defined associations of storage object metadata with the one or more storage objects.

5. The computer-implemented method of claim 4 , wherein associating the storage object metadata with the one or more storage objects includes:

providing the one or more user-defined associations of storage object metadata with the one or more storage objects to the one or more machine learning models as training data.

6. The computer-implemented method of claim 1 , wherein the one or more machine learning models are configured for one or more of: offline training and continuous training.

7. A computer program product residing on a non-transitory computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising:

receiving a first set of input/output (IO) requests for one or more storage objects stored on a storage array;

extracting one or more IO properties from the first set of IO requests;

associating metadata with the one or more storage objects using one or more machine learning models based upon, at least in part, the one or more IO properties extracted from the first set of IO requests, thus defining storage object metadata,

wherein associating the storage object metadata with the one or more storage objects includes associating temporal storage object metadata with the one or more storage objects, wherein the temporal storage object metadata indicates a period of time for which a particular temporal storage object metadata is relevant for a particular storage object, and

wherein associating the storage object metadata with the one or more storage objects includes associating spatial IO processing metadata with the one or more storage objects, wherein the spatial IO processing metadata indicates how the particular storage object is being apportioned into multiple sub-portions for utilizing the multiple sub-portions in different applications and for distinct workflows;

enabling one or more IO processing rules based upon, at least in part, the storage object metadata;

receiving a subsequent set of IO requests, wherein in response to receiving the subsequent set of IO requests, identifying the storage object metadata associated with the one or more storage objects targeted by the subsequent set of IO requests;

defining the one or more IO processing rules for optimizing processing of the subsequent set of IO requests on the one or more storage objects based upon, at least in part, the storage object metadata associated with the one or more storage objects and monitoring the one or more storage objects before, during, and after the processing of the first set of IO requests and the subsequent set of IO requests;

enabling storage optimization of the processing of the subsequent set of IO requests on the one or more storage objects based upon, at least in part, the storage object metadata and the one or more IO processing rules associated with the one or more storage objects;

dynamically adjusting values of one or more of the storage object metadata and the one or more IO processing rules based upon, at least in part, the processing of the subsequent IO requests on the one or more storage objects; and

updating IO processing rules for new IO requests based on new learnings and new storage object metadata.

8. The computer program product of claim 7 , wherein the one or more storage objects include at least one of:

one or more volumes;

one or more files; and

one or more Logical Unit Numbers (LUNs).

9. The computer program product of claim 7 , wherein associating the storage object metadata with the one or more storage objects includes:

associating spatial storage object metadata with the one or more storage objects.

10. The computer program product of claim 7 , wherein associating the storage object metadata with the one or more storage objects includes:

receiving one or more user-defined associations of storage object metadata with the one or more storage objects.

11. The computer program product of claim 10 , wherein associating the storage object metadata with the one or more storage objects includes:

providing the one or more user-defined associations of storage object metadata with the one or more storage objects to the one or more machine learning models as training data.

12. The computer program product of claim 7 , wherein the one or more machine learning models are configured for one or more of: offline training and continuous training.

13. A computing system comprising:

a memory; and

a processor configured to receive a first set of input/output (IO) requests for one or more storage objects stored on a storage array, wherein the processor is further configured to extract one or more IO properties from the first set of IO requests, wherein the processor is further configured to associate metadata with the one or more storage objects using one or more machine learning models based upon, at least in part, the one or more IO properties extracted from the first set of IO requests, thus defining storage object metadata, wherein associating the storage object metadata with the one or more storage objects includes associating temporal storage object metadata with the one or more storage objects, wherein the temporal storage object metadata indicates a period of time for which a particular temporal storage object metadata is relevant for a particular storage object, wherein associating the storage object metadata with the one or more storage objects includes associating spatial IO processing metadata with the one or more storage objects, wherein the spatial IO processing metadata indicates how the particular storage object is being apportioned into multiple sub-portions for utilizing the multiple sub-portions in different applications and for distinct workflows, wherein the processor is further configured to enable one or more IO processing rules based upon, at least in part, the storage object metadata objects, wherein the processor is further configured to receive a subsequent set of IO requests, wherein in response to receiving the subsequent set of IO requests, the processor is further configured to identify the storage object metadata associated with the one or more storage objects targeted by the subsequent set of IO requests, wherein the processor is further configured to define the one or more IO processing rules for optimizing processing of the subsequent set of IO requests on the one or more storage objects based upon, at least in part, the storage object metadata associated with the one or more storage objects and monitoring the one or more storage objects before, during, and after the processing of the first set of IO requests and the subsequent set of IO requests, wherein the processor is further configured to enable storage optimization of the processing of the subsequent set of IO requests on the one or more storage objects based upon, at least in part, the storage object metadata and the one or more IO processing rules associated with the one or more storage objects, wherein the processor is further configured to dynamically adjust values of one or more of the storage object metadata and the one or more IO processing rules based upon, at least in part, the processing of the subsequent IO requests on the one or more storage objects, and wherein the processor is further configured to update IO processing rules for new IO requests based on new learnings and new storage object metadata.

14. The computing system of claim 13 , wherein the one or more storage objects include at least one of:

one or more volumes;

one or more files; and

one or more Logical Unit Numbers (LUNs).

15. The computing system of claim 13 , wherein associating the storage object metadata with the one or more storage objects includes:

associating spatial storage object metadata with the one or more storage objects.

16. The computing system of claim 13 , wherein associating the storage object metadata with the one or more storage objects includes:

receiving one or more user-defined associations of storage object metadata with the one or more storage objects.

17. The computing system of claim 16 , wherein associating the storage object metadata with the one or more storage objects includes:

providing the one or more user-defined associations of storage object metadata with the one or more storage objects to the one or more machine learning models as training data.