HICA: A Hybrid Scientific Workflow Scheduling Algorithm for Symmetric Homogeneous Resource Cloud Environments

With the increasing volume of scientific computation data and the advancement of computer performance, scientific computation is becoming more dependent on the powerful computing capabilities of cloud computing. On cloud platforms, tasks in scientific workflows are assigned to computational resources and executed according to specific strategies. Therefore, workflow scheduling has become a key factor affecting efficiency. This paper proposes a hybrid scientific workflow scheduling algorithm, HICA, to address the scheduling problem of scientific workflows in symmetric homogeneous cloud environments with optimization goals of makespan and cost. HICA combines the Imperialist Competitive Algorithm (ICA) with the HEFT algorithm, integrating HEFT into the initial population of the ICA to accelerate the convergence of the ICA. Experimental results show that the proposed hybrid approach outperforms other algorithms in real-world workflow applications. Specifically, when the workflow scale is 100, the average improvements in makespan and cost are 133.89 and 273.33, respectively; when the workflow scale is 1000, the improvements are 371.62 and 9178.98. The scheduling results for the Earth System Model parameter tuning workflow show that compared to the scenario without using a scheduling algorithm, the makespan and cost were improved by 13% and 21%, respectively.