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 is proposed.
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.
Cloud computing has transformed the delivery of modern applications and services by providing scalable, flexible, and cost-effective access to computing resources. One of the most critical challenges in cloud environments is the efficient distribution of dynamic workloads across heterogeneous resources, commonly addressed through load balancing and task scheduling techniques. Efficient scheduling plays a vital role in maximizing resource utilization, minimizing response time, and maintaining acceptable Quality of Service (QoS), particularly under dynamic and large-scale workloads. Despite the progress achieved by traditional heuristics such as Min-Min and metaheuristic approaches like the Improved Sparrow Search Algorithm (ISSA), challenges related to scalability, adaptability, and computational overhead remain. Metaheuristic-based approaches often involve iterative optimization processes that may limit their efficiency in real-time scheduling scenarios. In this paper, we propose a lightweight Stochastic Predictive Energy-Aware Scheduling (SPES) algorithm that integrates predictive execution estimation, multi-resource awareness, and stochastic decision-making. Unlike deterministic scheduling strategies, SPES employs a Top K candidate selection mechanism combined with probabilistic weighting and epsilon-greedy exploration to enhance adaptability and avoid suboptimal resource allocation. The proposed method considers CPU, memory, and I/O demands to achieve balanced utilization across heterogeneous hosts while implicitly addressing energy efficiency through utilization-based modeling. The proposed algorithm is implemented and evaluated using the CloudSim 5.0 simulation framework under heterogeneous multi-region cloud environments with varying workload sizes. Experimental results demonstrate that SPES consistently outperforms ISSA and achieves makespan reductions of up to 23.8% while improving scalability, resource utilization, and scheduling efficiency under dynamic cloud workloads. These results indicate that SPES provides an effective lightweight scheduling solution for large-scale and energy-aware cloud computing environments and supports green computing objectives through improved resource efficiency.
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Keywords:Cloud Computing, Resource Allocation, Heuristic Method, Greedy Algorithm, Cost Optimization, Virtualization, Task Scheduling, Resource Utilization
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