1. A machine implemented method, comprising: selecting by a processor, a plurality of training workloads from a plurality of applications accessing a plurality of storage devices of a networked storage system using a plurality of storage volumes, wherein each training workload is defined by a training workload intensity and at least one workload characteristic, where the training workload intensity comprises of latency in processing input/output requests and at least one of outstanding input/output operations (OIO) or input/output operations per second (IOPS) processed by the plurality of storage device volumes and the at least one workload characteristic is based on a percentage of write operations, a percentage of read operations, and a percentage of operations that are neither read nor write operations; generating by the processor, a training workload signature for each of the plurality of training workloads using a workload characteristic corresponding to each of the plurality of training workloads; identifying by the processor, at least a first training workload parameter and a second training workload parameter corresponding to each of the plurality of training workloads based on the training workload intensity defined by a relationship between latency and one of OIO and IOPS for each training workload; storing by the processor, the first training workload parameter and the second workload parameter at a mapping data structure for mapping the training workload signature of each training workload to the corresponding first training workload parameter and the second training workload parameter; receiving by the processor, a first query for increasing intensity of an existing workload for an application currently using a storage device from among the plurality of storage devices; obtaining by the processor, workload characteristic for the existing workload from the storage device storing data for the application for the existing workload; generating by the processor, a first query workload signature for the existing workload using the obtained characteristic and with increased intensity; selecting by the processor, training workloads from the mapping data structure by comparing the first query workload signature with stored training workload signatures using a score value that matches a threshold value indicating similarity between the first workload signature and stored training workload signatures; using by the processor, the score value for the selected training workloads for determining a first query workload parameter and a second query workload parameter for the existing workload with increased intensity; determining by the processor, an estimated latency for the first query, based on the first and second query workload parameters and additional intensity for the existing workload; outputting the estimated latency; and determining by the processor, a predicted headroom based on at least one of the first and second query workload parameters and using the predicted headroom for a provisioning decision for increasing intensity of the existing workload, wherein the predicted headroom indicates a maximum number of input/output operations that can be processed for the first query after which only latency increases indicating system saturation for using the storage device by the application.