Vol. 4 No. 2 (2026)

Open Access

Article

Article ID: 4439

Static vs. dynamic cloud scheduling: A benchmark driven performance and energy study

by Saba Naz, Altaf Hussain

Computing and Artificial Intelligence, Vol.4, No.2, 2026;

Major multinational corporations, including Amazon, Microsoft, IBM, and Google, continue to advance network and computational infrastructures through the deployment of highly efficient data centers worldwide to strengthen their cloud computing services. Despite these technological advancements, cloud computing environments still face several critical challenges, such as optimal resource utilization, cost efficiency, security, fault tolerance, scalability, and energy consumption. For organizations operating private clouds or delivering cloud-based services, the primary objectives include maximizing resource utilization, minimizing response and waiting times, increasing throughput and profitability, and enhancing the overall user experience, with energy efficiency being a particularly important concern. This study presents an empirical evaluation of various static and dynamic cloud job scheduling algorithms using the CloudSimPlus simulation framework. The algorithms are assessed using two well-established scientific benchmark datasets: HCSP and GoCJ instances. Experimental results reveal that the Resource-Aware Load Balancing Algorithm (RALBA) emerges as the most energy-efficient static scheduling approach, achieving reduced makespan, improved resource utilization, and lower energy consumption. In contrast, the Max–Min algorithm exhibits the highest makespan, the lowest throughput, inefficient resource utilization, and the greatest energy consumption. Among the dynamic scheduling techniques, DE-RALBA demonstrates superior resource utilization and competitive makespan performance while maintaining relatively efficient energy usage. However, the results also indicate that dynamic scheduling approaches may incur higher energy consumption depending on workload characteristics and rescheduling overhead. Unlike prior studies that evaluate scheduling strategies in isolation, this work provides a unified, benchmark-driven, and energy-aware comparison of static and dynamic scheduling paradigms, enabling deeper insights into performance–energy trade-offs.