A scheduling middleware for data intensive applications on a grid

A grid consists of high-end computational, storage, and network resources that, while known a priori, are dynamic with respect to activity and availability. Efficient scheduling of requests to use grid resources must adapt to this dynamic environment while meeting administrative policies. This paper discusses the necessary requirements of such a scheduler and proposes a framework that can administrate grid policies and schedule complex and data intensive scientific applications. We present early experimental results for proposed a framework that effectively utilizes other grid infrastructure such as work-flow management systems and execution systems. These results demonstrate that proposed a framework can effectively schedule work across a large number of distributed clusters that are owned by multiple units in a virtual organization.