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Resource Provisioning Options for Large-Scale Scientific Workflows

Scientists in many fields are developing large-scale workflows containing millions of tasks and requiring thousands of hours of aggregate computation time. Acquiring the computational resources to execute these workflows poses many challenges for application developers. Although the grid provides ready access to large pools of computational resources, the traditional approach to accessing these resources suffers from many overheads that lead to poor performance. In this paper we examine several techniques based on resource provisioning that may be used to reduce these overheads. These techniques include: advance reservations, multi-level scheduling, and infrastructure as a service (IaaS). We explain the advantages and disadvantages of these techniques in terms of cost, performance and usability.