To concretely motivate this problem, we provide here a detailed analysis of six current data-intensive, scientific, batch workloads. From this analysis, we derive quantitative bounds on expected scalability and demonstrate the infeasibility of scheduling these workloads using current CPU-centric systems that lack data-awareness.

Adding data-awareness to CPU-centric batch schedulers allows a careful coordination of both data and CPU allocation thereby reducing the performance cost of remote execution. To achieve this coordinated schedule, however, batch schedulers need complicity from storage systems to allow transfer of control over low-level storage decisions from the storage system to the batch scheduler. Wielding explicit storage control, the batch scheduler can then carefully coordinate storage and CPU allocations using a variety of data-driven scheduling policies.