CSCR: A Cross-View Intelligent Scheduling Method Implemented via Cloud Computing Workflow Reduction

The surge in the development of artificial intelligence has led to increases in the complexity of computational tasks and the resource demands within cloud computing scenarios. Therefore, intelligent scheduling methods have formed a crucial research area. Solving complex scheduling problems requires many problem feature and long-sequence decision-making observations as possible. To address the workflow scheduling problem under the limited capabilities of models, workflow reduction and cross-view workflow scheduling problems are first proposed in this article, with the optimization objectives and constraints of each problem described. Second, a cross-view intelligent scheduling method implemented via cloud computing workflow reduction (CSCR), including a workflow reduction sorting algorithm (Task-priority ranker), an intelligent reduction algorithm (Workflow view-transformer), and a cross-view intelligent scheduling algorithm (Joint-scheduler), is proposed. We also propose an intelligent scheduling architecture under the workflow reduction paradigm. By reducing the workflow, we provide multiple views that support the decision-making processes of deep reinforcement learning-based scheduling models and coordinate workflow views before and after the reduction step to achieve cross-view joint scheduling. Experimental results show that CSCR achieves minimum advantages of 42.1%, 43.2%, and 33.3% in terms of three workflow reduction indicators over four other algorithms, significantly optimizing the effect of the employed scheduling model.