GPS: A Lightweight Greedy-Predictive Scheduling Approach for Energy-Aware Multi-Region Cloud Computing

Abstract

Efficient resource allocation and task scheduling remain fundamental challenges in cloud computing because of resource heterogeneity, dynamic workload characteristics, and the increasing demand for scalable, energy-efficient, and sustainable cloud infrastructures. Conventional scheduling approaches, including Min-Min and the Improved Sparrow Search Algorithm (ISSA), have improved resource utilization and load balancing. However, they still face limitations in scalability, execution efficiency, and adaptive scheduling under heterogeneous and dynamically changing cloud workloads. To overcome these limitations without introducing the computational overhead associated with iterative optimization techniques, this paper proposes a lightweight Greedy Predictive Scheduling (GPS) algorithm that combines predictive resource utilization estimation with greedy host selection to improve scheduling decisions across heterogeneous multi-region cloud environments. The proposed scheduler integrates predictive execution estimation, multi-resource awareness, adaptive greedy decision making, and utilization-aware energy consideration to improve scheduling decisions across heterogeneous multi-region cloud environments. The proposed approach is implemented and evaluated using the CloudSim Plus 5.0 simulation framework, and the experimental results demonstrate that GPS achieves better performance than ISSA across our experiments. GPS achieves a makespan reduction of up to 31.25% compared with ISSA while consistently improving execution efficiency, scalability, and balanced resource utilization across heterogeneous multi-region cloud environments, demonstrating that GPS provides an effective lightweight scheduling solution for large-scale energy-aware cloud computing.