Self-adaptive task allocation and scheduling of meta-tasks in non-dedicated heterogeneous computing. (6th February 2006) - Wu, Ming; Sun, Xian-He
The efforts to construct a national scale grid computing environment have brought unprecedented computing capacity and complexity. Exploiting this complex infrastructure requires efficient middleware to support the execution of distributed applications, which presents the challenge of how to schedule tasks in shared heterogeneous systems. Most existing scheduling systems are based on predetermined estimation of task completion time and resources availability. They may not provide appropriate scheduling if the underlying computing resources present an abnormal usage pattern during an application execution. For solving long-running applications in a large-scale grid environment, abnormal usage of some resource may not be uncommon. We have proposed the development of the Grid Harvest Service (GHS) performance evaluation and task scheduling system in our previous work. In this study, we present a novel dynamic self-adaptive scheduling algorithm and its implementation under GHS. Scheduling and rescheduling algorithms and mechanisms are carefully investigated. Experimental results show that, equipped with these new scheduling mechanisms, GHS outperforms existing systems considerably in scheduling large applications in a non-dedicated heterogeneous environment.