In cloud computing environments, it is a great challenge to schedule a workflow application because it is an NP-complete problem. Particularly, scheduling workflows with different Quality of Service (QoS) constraints makes the problem more complex. Several approaches have been proposed for QoS workflow scheduling, but most of them are focused on a single QoS constraint. Therefore, this paper presents a new algorithm for multi-QoS constrained workflow scheduling, cost, and time, named Budget-Deadline Constrained Workflow Scheduling (BDCWS). The algorithm builds the task optimistic available budget based on the execution cost of the task on the slowest virtual machine and the optimistic spare budget, and then builds the set of affordable virtual machines according to the task optimistic available budget to control the range of virtual machine selection, and thus effectively controls the task execution cost. Finally, a new balance factor and selection strategy are given according to the optimistic spare deadline and the optimistic spare budget, so that the execution cost and time consumption of the control task are more effective. To evaluate the proposed algorithm, we experimentally evaluated our algorithm using real-world workflow applications. The experimental results show that compared with DBWS (Deadline-Budget Workflow Scheduling) and BDAS (Budget-Deadline Aware Scheduling), the proposed algorithm has a 26.3–79.7% higher success rate. Especially when the deadline and budget are tight, the improvement is more obvious. In addition, the best cost frequency of our algorithm achieves a 98%, which is more cost-competitive than DBWS.

在云计算环境中，安排工作流应用程序是一个很大的挑战，因为它是NP完全问题。特别是，调度具有不同服务质量（QoS）约束的工作流会使问题更加复杂。已经提出了几种用于QoS工作流调度的方法，但是大多数方法集中在单个QoS约束上。因此，本文提出了一种用于多QoS约束工作流调度，成本和时间的新算法，称为预算截止约束工作流调度（BDCWS）。该算法根据最慢虚拟机上的任务执行成本和乐观备用预算来建立任务乐观可用预算。然后根据任务乐观的可用预算构建可负担的虚拟机集，以控制虚拟机选择的范围，从而有效地控制任务执行成本。最后，根据最优备用期限和最优备用预算，给出了新的平衡因子和选择策略，使控制任务的执行成本和时间消耗更加有效。为了评估提出的算法，我们使用实际的工作流应用程序实验性地评估了我们的算法。实验结果表明，与DBWS（截止日期—预算工作流调度）和BDAS（预算—截止日期意识调度）相比，该算法的成功率高26.3–79.7％。尤其是在截止日期和预算紧张的情况下，改进更为明显。此外，