树型网格计算环境下的独立任务调度

Independent Tasks Scheduling on Tree-Based Grid Computing Platforms

任务调度是实现高性能网格计算的一个基本问题,然而,设计和实现高效的调度算法是非常具有挑战性的.讨论了在网格资源计算能力和网络通信速度异构的树型计算网格环境下,独立任务的调度问题.与实现最小化任务总的执行时间不同(该问题已被证明是NP难题),为该任务调度问题建立了整数线性规划模型,并从该线性规划模型中得到最优任务分配方案??各计算节点最优任务分配数.然后,基于最优任务分配方案,构造了两种动态的需求驱动的任务分配启发式算法:OPCHATA(optimization-based priority-computation heuristic algorithm for task allocation)和OPBHATA(optimization-basedpriority-bandwidth heuristic algorithm for task allocation).实验结果表明:在异构的树型计算网格环境下实现大量独立任务调度时,该算法的性能明显优于其他算法.

Task scheduling is a fundamental issue in achieving high performance in grid computing systems. However, it is a big challenge for efficient scheduling algorithm design and implementation. In this paper, the problem of scheduling independent tasks on tree-based grid computing platforms, where resources have different speeds of computation and communication, is discussed. In contrast to minimizing the total execution time, which is NP-hard in most formulations, an integer linear programming model for this problem is presented. Using the model, the optimal scheduling scheme that determines the optimal number of tasks assigned to each computing node is obtained. With the optimal scheduling scheme, two demand-driven and dynamic heuristic algorithms for task allocation are proposed： OPCHATA （optimization-based priority-computation heuristic algorithm for task allocation） and OPBHATA（optimization-based priority-bandwidth heuristic algorithm for task allocation）. The experimental results show that the proposed algorithms for the scheduling problem obtain better performance than other algorithms.