A method, non-transitory computer readable medium, and provisioning advisor device that obtains an intensity and characteristics for each of a plurality of training workloads from storage device volumes. For each of the training workloads, at least first and second training workload parameters are generated, based on the training workload intensity, and an associated training workload signature is generated, based on the training workload characteristics. The first and second training workload parameters and associated training workload signatures are stored in a mapping table. A signature and an intensity for a query workload are obtained. First and second query workload parameters are determined based on a correlation of the query workload signature with the training workload signatures of the mapping table. An estimated latency for the query workload is determined, based on the first and second query workload parameters and the query workload intensity, and the estimated query workload latency is output.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A provisioning advisor computing device, comprising: a processor coupled to a memory and configured to execute programmed instructions stored in the memory to: obtain a training workload intensity and at least one characteristic for each of a plurality of training workloads from a plurality of storage device volumes; generate, for each of the training workloads, at least first and second training workload parameters, based on the training workload intensity, and an associated training workload signature, based on the training workload characteristics, and store the first and second training workload parameters and associated training workload signatures in a mapping table; obtain a signature and a query workload intensity for a query workload; determine first and second query workload parameters based on a correlation of the query workload signature with at least one of the training workload signatures of the mapping table; and determine an estimated latency for the query workload, based on the first and second query workload parameters and the query workload intensity, and output the estimated query workload latency. 2. The device of claim 1, wherein the training workload intensity comprises latency and at least one of outstanding input/output operations (OIO) or input/output operations per second (IOPS). 3. The device of claim 1, wherein the query workload signature is obtained in response to a request for an impact on performance of provisioning a new workload having the query workload signature or increasing an intensity of an existing workload having the query workload signature. 4. The device of claim 1, wherein the training workload characteristics comprise at least one of a percentage of write operations, a percentage of read operations, a percentage of operations that are neither read nor write operations, an average size of read data, an average size of written data, a percentage of random read operations, or a percentage of random write operations. 5. The device of claim 1, wherein the processor coupled to the memory is further configured to execute programmed instructions stored in the memory to applying a robust envelope technique to the training workload intensity prior to generating the first and second training workload parameters. 6. The device of claim 1, wherein the processor coupled to the memory is further configured to execute programmed instructions stored in the memory to determine a predicted headroom based on at least one of the first or second query workload parameters and output the predicted headroom. 7. A method for provisioning workloads in a storage system using machine learning, the method comprising: obtaining, by a provisioning advisor computing device, a training workload intensity and at least one characteristic for each of a plurality of training workloads from a plurality of storage device volumes; generating, by the provisioning advisor computing device and for each of the training workloads, at least first and second training workload parameters, based on the training workload intensity, and an associated training workload signature, based on the training workload characteristics, and store the first and second training workload parameters and associated training workload signatures in a mapping table; obtaining, by the provisioning advisor computing device, a signature and a query workload intensity for a query workload; determining, by the provisioning advisor computing device, first and second query workload parameters based on a correlation of the query workload signature with at least one of the training workload signatures of the mapping table; and determining, by the provisioning advisor computing device, an estimated latency for the query workload, based on the first and second query workload parameters and the query workload intensity, and outputting the estimated query workload latency. 8. The method of claim 8, wherein the training workload intensity comprises latency and at least one of outstanding input/output operations (OIO) or input/output operations per second (IOPS). 9. The method of claim 8, wherein the query workload signature is obtained in response to a request for an impact on performance of provisioning a new workload having the query workload signature or increasing an intensity of an existing workload having the query workload signature. 10. The method of claim 8, wherein the training workload characteristics comprise at least one of a percentage of write operations, a percentage of read operations, a percentage of operations that are neither read nor write operations, an average size of read data, an average size of written data, a percentage of random read operations, or a percentage of random write operations. 11. The method of claim 8, further comprising applying, by the provisioning advisor computing device, a robust envelope technique to the training workload intensity prior to generating the first and second training workload parameters. 12. The method of claim 8, further comprising determining, by the provisioning advisor computing device, a predicted headroom based on at least one of the first or second query workload parameters and outputting the predicted headroom. 13. A non-transitory computer readable medium having stored thereon instructions for provisioning workloads in a storage system using machine learning comprising machine executable code which when executed by a processor, causes the processor to perform steps comprising: obtaining an intensity and at least one characteristic for each of a plurality of training workloads from a plurality of storage device volumes; generating, for each of the training workloads, at least first and second training workload parameters, based on the training workload intensity, and an associated training workload signature, based on the training workload characteristics, and store the first and second training workload parameters and associated training workload signatures in a mapping table; obtaining a signature and an intensity for a query workload; determining first and second query workload parameters based on a correlation of the query workload signature with at least one of the training workload signatures of the mapping table; and determining an estimated latency for the query workload, based on the first and second query workload parameters and the query workload intensity, and outputting the estimated query workload latency. 14. The medium of claim 15, wherein the training workload intensity comprises latency and at least one of outstanding input/output operations (OIO) or input/output operations per second (IOPS). 15. The medium of claim 15, wherein the query workload signature is obtained in response to a request for an impact on performance of provisioning a new workload having the query workload signature or increasing an intensity of an existing workload having the query workload signature. 16. The medium of claim 15, wherein the training workload characteristics comprise at least one of a percentage of write operations, a percentage of read operations, a percentage of operations that are neither read nor write operations, an average size of read data, an average size of written data, a percentage of random read operations, or a percentage of random write operations. 17. The medium of claim 15, further having stored thereon instructions comprising machine executable code which when executed by the processor, further causes the processor to perform steps further comprising applying a robust envelope technique to the training workload intensity prior to generating the first and second training workload parameters. 18. The medium of claim 15, further having stored thereon instructions comprising machine executable code which when executed by the processor, further causes the processor to perform steps further comprising determining a predicted headroom based on at least one of the first or second query workload parameters and outputting the predicted headroom.