云环境下基于聚簇的科学工作流执行优化策略

基于云环境下的科学工作流,以提高处理机利用率、降低费用为目标,提出了一种基于聚簇的执行优化策略。该策略首先基于合理的任务复制和分簇,以实现关键任务的尽早调度;在此基础上,对任务簇再次进行聚集,以充分利用任务簇中任务间可能的空闲时间。实验表明,该策略能够提高任务的并行度,提前工作流的最早完成时间,并且在提高处理机的利用率和降低科学工作流的执行费用方面有显著效果。

Execution optimization policy of scientific workflow based on cluster aggregation under cloud environment

Focusing on the higher ratio of processor utilization and lower execution cost of a scientific workflow in cloud, a policy of execution optimization based on task cluster aggregation was proposed. First, the tasks were reasonably replicated and aggregated into several clusters. Therefore, the key tasks could be scheduled as early as possible. Then, the task clusters were aggregated again to facilitate the spare time among the tasks in the task cluster. The experimental results show that the proposed policy can improve the parallelism of workflow tasks, advance the earliest finish time of the whole workflow and it has a significant effect in improving the utilization ratio of processors and lowering the cost of workflow execution.