Task Scheduling in Cloud Computing: A Novel Approach Using Artificial Intelligence Algorithms - Kashlan Yaser, Zainab (Journal of Al-Qadisiyah for Computer Science and Mathematics, 2025)
Task scheduling is an important problem that is encountered in distributed systems, project management, and cloud computing. Task dependencies used to model the so-called directed acyclic graphs (DAGs) and for the scheduling optimization have been widely used. However, such methods do not work well in dynamic environments and complex constraints. Task scheduling using DAGs is proposed in this paper, along with the application of Artificial Intelligence (AI) algorithms exploring Artificial Neural Networks (ANN), Artificial Intelligence algorithms, which include Genetic Algorithms (GA) and a specific type (Reinforcement Learning (RL)) as novelty addition to the scheduling problem. The proposed method is developed to enhance inventory efficiency, bound make span, and intend to dynamically cope with changes in resource availability and task priorities. The unusual effect that paper accounts for in that race was the application of artificial intelligence to enhance the process of scheduling. The workflowSim simulator is used to evaluate the proposed method on both synthetic and real-world workflows. Experimental results highlight the method’s superior effectiveness when compared with alternative algorithms.