1. A distributed file system node fine grained metering method comprising: receiving from a user, by a processor of a hardware device, job data associated with jobs for execution via a distributed file system; uploading, by said processor to a memory device of said hardware device, characteristics of said jobs; retrieving, by a processor enabling a policy engine circuit of said hardware device, policy and cost metrics data associated with hardware usage metering, wherein said policy and cost metrics data comprises policies implemented as pluggable components defined in advance, by: uploading xml files, via descriptor files or j son files, or via a command line, for said jobs; retrieving, by said processor enabling a hardware device cluster, resource requests associated with hardware resource usage; uploading, by said processor enabling a job descriptor engine of said hardware device, attributes associated with said resource requests; analyzing, by said processor enabling said job descriptor engine of said hardware device, said policy and cost metrics data; querying, by said processor enabling said job descriptor engine of said hardware device, a recommendation circuit with respect to said resource requests; determining, by said processor enabling a machine learning circuit with respect to results of said querying, a set of metrics of said policy and cost metrics data associated with said resource requests; updating, by said processor based on said set of metrics, said machine learning circuit; and determining, by said processor enabling a metrics circuit with respect to said set of metrics, utilized hardware resources with respect to said hardware usage metering and said resource requests.