一种云环境下科学工作流执行计划的优化方法

为降低云环境下科学工作流的执行代价,提出了一种执行计划的优化方法。引入猴群算法,依靠对当前执行计划的层内和层间优化,在保证工作流全局截止时间约束的前提下,通过同层任务的逻辑聚合和任务的层间调整,尽可能减少各层任务数的差异,以避免资源的闲置浪费,缩短任务的等待时间。实验表明,该方法与类似研究相比,可降低资源消耗量,减小总的延迟时间。

An approach to optimizing the execution planof scientific workflows in cloud environment

In order to reduce the cost of scientific workflow execution in cloud environment, we propose an approach to optimizing the execution plans of scientific workflows in cloud environment. It introduces the monkey group algorithm and relies on the intra-level and inter-level optimization of the current execution plan. Under the premise of ensuring the global deadline of the workflow, through the logical aggregation of the same-level tasks and the inter-level adjustment of the tasks, the difference in the number of tasks at each level is minimized to avoid waste of resources and reduce the waiting time of tasks. Experiments show that compared with the BTS algorithm and the SPSWVC algorithm, the proposed method can reduce resource consumption and the total delay time of tasks.