C. Liu, S. Baskiyar, and C. Wang (USA)
Data grid, peer-to-peer scheduler, distributed scheduling
In a grid computing environment, dynamicity and geographically distributed sites, make task scheduling problems challenging to solve. It is hard for a local site to obtain precise real-time information about other sites given that specific information on a site such as load and computing resources may change rapidly. Moreover, in data grid environment, large scale data intensive applications make scheduling problems even more challenging since both computational and data storage resources must be taken into consideration. In this paper we propose an innovative peer-to-peer scheduler to solve these problems. This scheduler is distributed and scalable. We used simulation to evaluate the performance of the scheduler under different circumstances, such as different number of hops to search suitable sites and different number of incoming tasks. Results show that our scheduler can successfully schedule around 75% of incoming tasks within their deadlines in average. For computation-intensive tasks, it can successfully schedule more than 90% of incoming tasks.
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