Challenges in Executing Data Intensive Biometric Workloads on a Desktop Grid

Desktop grids have traditionally focused on executing computation intensive workloads. Can they also be used to execute data-intensive workloads? To answer this question, we present a case study of a data intensive biometric application which is infeasible to process on a single machine. We evaluate the capacity of a desktop grid to store and deliver the data need to execute the workload, and compare several general techniques for data deployment. Selecting the most scalable technique, we execute and evaluate five large production workloads on a 350-CPU desktop grid. We observe that this technique is sensitive to many parameters, and propose that an ideal system should be responsible for choosing the proper decomposition of a workload.

Capacity planning and stochastic scheduling in grid computing environments

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