New Worker-Centric Scheduling Strategies for Data-Intensive Grid Applications

In this paper we argue that a worker-centric scheduler design is more desirable for data-intensive applications in Grid environments. Previous research on task-centric scheduling for dataintensive applications has identi ed that reusing the data present in a Grid site improves performance. However, task-centric scheduling bears two problems - unbalanced task assignments and premature scheduling decisions. On the contrary, both of these problems can be avoided by using worker-centric scheduling, thus worker-centric scheduling leads to a simpler scheduler design and better performance. Therefore, we propose a series of workercentric scheduling strategies for data-intensive applications and evaluate, with a real application (Coadd), how each strategy performs compared to a task-centric one. Our results show that worker-centric strategies improve the performance in terms of makespan and bandwidth usage