Budget-deadline constrained approach for scientific workflows scheduling in a cloud environment

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.

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