Research on Deadline Division and Scheduling of Batch Scientific Workflow in Cloud Environment

Data intensive batch processing scientific workflow is a typical application model in the era of big data. A reasonable scheduling method can improve the resource utilization rate and reduce the rental cost on the premise of meeting the deadline requirements. In this paper, an iterative floating interval allocation method suitable for batch scientific workflow is proposed in the deadline allocation stage, and then in the resource mapping stage, a task scheduling algorithm considering the utilization of time gaps and minimizing the cost of renting virtual machines is proposed, which weighted the expected utilization of time slices. Experiments show that compared with similar scheduling algorithms, in the specified deadline time, it can not only better solve the data transmission bottleneck, but also better improve the execution efficiency and reduce the total rental cost.