When each task of the longest path in a task-dependent scientific workflow must meet a deadline, the path is called critical. Tasks in a critical path have priority over tasks in non-critical paths. Considering this fact that less methods have already dealt with the critical path problem for workflow scheduling in cloud, this study aims to present a critical-path based method to consider the problem based on our previous optimal workflow scheduling method, GWO-based (Grey Wolf Optimization). We applied our study to balance and imbalance scientific workflows. Our results show that considering the critical path improves the completion time of workflows while maintaining a proper level of resource cost and resource utilization. Moreover, to show the effectiveness of the current study, we compared the performance of the proposed method with non-critical-path aware algorithms, using three different indicators. The simulation demonstrates that compared to PGWO as the base method, the proposed approach achieves (1) approximately 68% improvement for makespan, (2) more accuracy in population sampling for about 70% of workflows, and (3) avoidance of the cost increases in more than 50% of workflows. Moreover, the proposed method decreases makespan approximately 3 times compared to the constrained-based approaches.

当任务相关科学工作流中最长路径的每个任务都必须满足最后期限时，该路径称为关键。关键路径中的任务优先于非关键路径中的任务。考虑到在云中处理工作流调度的关键路径问题的方法较少这一事实，本研究旨在提出一种基于关键路径的方法来考虑基于我们以前的最优工作流调度方法，基于 GWO（灰狼）的问题。优化）。我们将我们的研究应用于平衡和不平衡科学工作流程。我们的结果表明，考虑关键路径可以缩短工作流的完成时间，同时保持适当的资源成本和资源利用率水平. 此外，为了显示当前研究的有效性，我们使用三个不同的指标将所提出的方法与非关键路径感知算法的性能进行了比较。模拟表明，与作为基本方法的 PGWO 相比，所提出的方法实现了 (1) 完工时间约 68% 的改进，(2) 大约 70% 的工作流程的总体抽样精度更高，以及 (3) 避免了成本增加在超过 50% 的工作流程中。此外，与基于约束的方法相比，所提出的方法将完工时间缩短了大约 3 倍。