Abstract: A method for achieving optimal use of compute, storage and network resources in a distributed computing system is provided. In one embodiment, a job request associated with a file is submitted. A Job Scheduler in conjunction with a workflow manager determines the best fit for dynamically chunking size of the file. The distributed storage subsystem provides a dynamic view to the compute resources where the job will be run. This knowledge is also used to pre-staging the data by the distributed storage subsystem. Furthermore, keeping a history of the job and their data allows for further improvements in resources and job execution times by being able to skip processing on data that has been done previously. Given the ability to chunk the file dynamically, it is possible to re-balance resources for running the job dynamically to meet its service level agreement.

Abstract: A method for achieving optimal use of compute, storage and network resources in a distributed computing system is provided. In one embodiment, a job request associated with a file is submitted. A Job Scheduler in conjunction with a workflow manager determines the best fit for dynamically chunking size of the file. The distributed storage subsystem provides a dynamic view to the compute resources where the job will be run. This knowledge is also used to pre-staging the data by the distributed storage subsystem. Furthermore, keeping a history of the job and their data allows for further improvements in resources and job execution times by being able to skip processing on data that has been done previously. Given the ability to chunk the file dynamically, it is possible to re-balance resources for running the job dynamically to meet its service level agreement.