Path-Aware Scheduling Algorithm for Cost Optimization of Deadline-Constrained Scientific Workflows in Cloud Environments

Cloud computing is now widely used in all major industries. This transformative technology allows users to access the resources they need through a pay-as-you-go model, providing an efficient and convenient service. Although cloud computing can provide users with flexible resource scheduling and payment models, improper use may lead to cost overruns. In addition to makespan, the total cost of cloud services is also a key user requirement. Therefore, in this paper, we propose a heuristic algorithm aimed at solving the optimization problem of workflow scheduling with deadline constraints in cloud environments in order to minimize the total cost. The PACM algorithm assigns the corresponding deadline to each task in the workflow in upward probabilistic order, and achieves a more reasonable deadline assignment for the tasks based on the Critical Path Factor and Dependency Amplification Factor. Finally, the tasks are then assigned to cloud services to meet their sub-deadline requirements. Experiments are conducted using well-known scientific workflows for performance evaluation, and the results show that the algorithm outperforms previous heuristic algorithms, proving the effectiveness of the algorithm.