2026· International Journal Of Engineering And Computer Science· 0 citations
Abstract
Efficient energy scheduling in heterogeneous computing environments is a critical challenge, as task allocation decisions directly affect both energy consumption and execution performance. This work presents an energy aware scheduling framework based on a discretized grasshopper optimization algorithm (GOA), designed to balance energy reduction with acceptable makespan. The model formulates scheduling as a constrained objectives optimization problem, incorporating energy use, makespan, heterogeneous resource capacities, workflow precedence, and non preemptive execution. A constant aware representation and repair based decoding strategy enable GOA to generate feasible task to resources assignments. Implemented in Python, the framework is evaluated against HEFT, Min and Random scheduling under varying workload. Results show that the schedules based on GOA achieves lower energy consumption and improved performance delay energy while maintaining competitive makespan, with performance gains becoming more pronounced as workload complexity increases. These findings demonstrate the scalability and effectiveness of discretised GOA as a metaheuristic solution for energy aware scheduling in heterogeneous systems.
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.
M. Yacoub, A. Raouf, Walaa K. Gad et al.· Electronics· 0 citations
Energy-aware task scheduling in heterogeneous cloud infrastructures remains challenging due to the combinatorial growth of task-to-resource assignments, resource heterogeneity, and the need to balance energy consumption with scheduling performance. This paper proposes an Adaptive Dominance-Guided Grey Wolf Optimizer (ADG-GWO) for non-preemptive task scheduling in heterogeneous cloud environments. ADG-GWO adapts Grey Wolf Optimization to discrete task-to-VM assignment by integrating dominance-guided genetic reproduction, Hamming-distance-based diversity regulation, and adaptive reproduction control. These mechanisms are designed to improve search stability, reduce premature convergence, and support effective exploration of high-dimensional assignment spaces without expanding the externally tuned hyperparameter space.The proposed method is evaluated through simulation under workload-scaling and capacity-scaling scenarios using heterogeneous cloud configurations. For evaluation, workload instances and heterogeneous VM configurations are derived from Google Cluster Trace 2019 to instantiate realistic task-to-VM scheduling scenarios. The results show that the proposed dominance-guided adaptive search improves energy-aware scheduling effectiveness while maintaining competitive scheduling efficiency in heterogeneous cloud environments.
Saleh Al Shamaa, Wei Shi, J. Corriveau· IEEE International Conferenc...· 0 citations
Cloud computing environments are becoming increasingly dynamic and heterogeneous, making efficient task scheduling a critical challenge for cloud service providers. Traditional heuristic scheduling methods such as Round Robin (RR), Min–Min, Max–Min, and First Come First Serve are commonly used because of their simplicity and low computational overhead. However, these approaches often struggle to adapt to fluctuating workloads and heterogeneous resource conditions, which may result in inefficient resource utilization, increased energy consumption, and longer execution times. To address these limitations, this study investigates a hybrid scheduling framework that integrates Hybrid Wild Goose Optimization (HWGO) with Deep Reinforcement Learning (DRL). In the proposed framework, the DRL component observes the current system state and generates adaptive scheduling decisions, while the HWGO algorithm refines these decisions through multi-objective optimization. The hybrid model aims to enhance scheduling efficiency by reducing makespan, minimizing energy consumption, balancing CPU load across virtual machines, and decreasing migration overhead. The performance of the HWGO–DRL framework is evaluated through a comparative analysis with conventional heuristic scheduling algorithms including(RR, FCFS, Min-Min, Max-Min), metaheuristic algorithms (GA, PSO, ACO), and bio-inspired algorithms (CSO, DFA, FDA, LOA) under identical cloud simulation conditions. Experimental results demonstrate that the hybrid approach achieves improved scheduling performance across multiple evaluation metrics. Notable improvements are observed in execution efficiency, workload distribution, and energy utilization. The proposed model achieves up to 30–40% reduction in makespan and 20–30% improvement in energy efficiency compared to baseline methods.. These findings indicate that intelligent hybrid optimization techniques can provide adaptive and efficient task scheduling solutions for modern cloud computing environments.
Annaiah H, A. Rajesh· International journal of com...· 0 citations
Minimizing energy consumption and carbon emissions while maintaining system performance is a critical challenge in cloud task scheduling. This paper presents a multi-objective scheduling framework based on a Memetic Algorithm (MA) designed to optimize task-to-VM mapping with respect to energy efficiency, carbon footprint, and throughput. The algorithm employs a weighted fitness function that integrates actual and idle energy usage, simulated time-varying carbon intensity, and task throughput. To enhance solution quality, MA combines global evolutionary operations (selection, crossover, mutation) with local search heuristics that adaptively refine candidate solutions based on workload characteristics and green energy opportunities. The carbon emission model incorporates dynamic emission factors (γ) derived from location- and time-sensitive datasets, reflecting real-world variability in grid carbon intensity. The proposed method is evaluated using the NASA Ames iPSC/860 workload under both low and high resource utilization scenarios. Comparative results demonstrate that the proposed MA approach achieves reduces the carbon emission by 20.2%, minimizes energy consumption by 17.7%, and enhances throughput by 21.2% over conventional techniques such as HDDPGTS and RAPTS, while also ensuring competitive performance in terms of makespan and resource utilization. These improvements underscore the potential of memetic-based hybrid scheduling to support environmentally sustainable and performance-efficient cloud infrastructures. The findings highlight the importance of integrating eco-aware intelligence into task scheduling policies, particularly for mission-critical and energy-intensive cloud applications.
Chennoji Sandhya, Mandla Alphonsa, Vankudoth Biksham et al.· International Journal of Int...· 0 citations
A novel QoS-aware task deployment methodology to enhance the Quality of Service (QoS) under resource limitations is introduced and results demonstrate that the proposed method achieves superior system performance compared to existing approaches.
Haotong Zhu, Lei Mo, T. Al-Hasan et al.· ACM Transactions on Design A...· 0 citations
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.
M. Yacoub, Ahmed E. Abdel Raouf, Walaa K. Gad et al.· Future Internet· 0 citations