Random Forest Algorithm-Driven Workflow Cost Optimization Scheduling in a Multi-Cloud Environment

The high heterogeneity of resource types in multi-cloud computing environments and the complexity and diversity of billing models make the workflow scheduling problem more complex and challenging when scientific workflows need to be completed within strict deadline constraints. Although existing research has progressed in reducing the completion time of workflow scheduling in multi-cloud environments, many difficulties still exist in minimizing scheduling costs while satisfying deadlines. Therefore, a random forest enhanced particle swarm optimization algorithm (RFPSO) is proposed in this study. The RFPSO algorithm implements intelligent initialization of resource allocation through a random forest model, which improves the efficiency of finding optimal solutions. Moreover, it designs a reflective boundary constraint mechanism and a hierarchical task allocation mechanism based on critical path. This design ensures that critical tasks can prioritize access to higher-performance computing resources, which effectively guarantees that tasks within the workflow are scheduled and completed by their deadlines. In addition, the quality of the optimal solution is improved through a local neighborhood search mechanism. Experimental results on scientific workflow datasets such as Epigenomics and Montage show that, compared with existing state-of-the-art methods, RFPSO reduces execution costs by an average of 57.31%.