Comparative Performance and Computational Complexity Analysis of Hybrid WGO–DRL and Heuristic, Metaheuristic and Bio-Inspired Scheduling Algorithms in Cloud Computing
Jul 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
A hybrid scheduling framework that integrates Hybrid Wild Goose Optimization (HWGO) with Deep Reinforcement Learning (DRL) is investigated, indicating that intelligent hybrid optimization techniques can provide adaptive and efficient task scheduling solutions for modern cloud computing environments.
Abstract
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.
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, Ahmed E. Abdel Raouf, Walaa K. Gad et al.· Electronics· 0 citations
Resource allocation in the cloud-edges is extremely important to reduce the time of execution, energy usage, and service-level agreement (SLA) breaches when experiencing dynamic workloads. In this paper, the author has proposed an elite co-evolution hybrid optimization model, Hybrid Hierarchical Optimization for Cloud Allocation (H2O-Cloud), which combines Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) using periodic elite migration and adaptive multi-objective evaluation. The suggested approach is also optimal in terms of makespan, SLA violation rate, energy consumption, and resource usage. Real world Azure LLM Inference Trace datasets (both code-generation, conversational AI) were used to run experiments. The analysis took into account scalable loads of 300 to 1200 tasks on heterogeneous cloud virtual machines, edge-only and hybrid edge-cloud infrastructure. Measurements confirm that H2O-Cloud is drastically more efficient than traditional FCFS scheduling with the maximum makespan decrease of 39.7% and throughput enhancement of 65.8% and SLA violation decrease of 17.1% when using the cloud only (900-task workload). The proposed approach results in an 8.0 percent reduction in makespan and a better resource utilization than PSO. Moreover, H2O-Cloud remains competitive in terms of energy efficiency and also shows better scalability to a larger amount of tasks. The strength of the proposed approach is statistically verified with the help of paired tests. This hybrid co-evolution mechanism contributes to both global exploration and local refinement at the same time, which allows the stable convergence and best load balancing on heterogeneous resources. The framework also exhibits high flexibility in edge-cloud hybrid systems, which minimizes SLA breaches and balances the energy usage. The experimental results confirm H2O-Cloud as a strong, scalable and SLA conscious resource provision strategy to next-generation AI-driven cloud computing systems.
D.Bhuvaneswari, S.Jeevitha, Prashant Sangulagi et al.· 2026 6th International Confe...· 0 citations
A hybrid nature-inspired algorithm called fruit fly optimization–ant colony optimization (FOA-ACO), which combines the exploitative ant colony optimization (ACO) and the exploratory fruit fly optimization algorithm (FOA) is suggested, which enhances overall cloud performance.
Narayana Rao Appini, K. Premnadh, Karnam Sreenu et al.· International Journal of Onl...· 0 citations
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.
Macauley Opuwari, C. Igiri, D. Ikeh· International Journal Of Eng...· 0 citations
A hybrid Deep Reinforcement Learning (DRL) framework that combines Deep Q-Network, Proximal Policy Optimization and Advantage Actor-Critic to enable adaptive resource scheduling in cloud environments is proposed.
P. Priya, J. Geetha, E. Naresh et al.· International Journal of Com...· 0 citations
Experiments show that the proposed Hybrid Framework for Joint Optimization of Resource Allocation and Load Balancing that spans two layers in heterogeneous cloud computing systems obtains 25-30% energy savings compared with ordinary methods, significantly reduces p95 latency and also achieves a relatively better Quality Of Service.
Eram Fatma, Nidhi Mishra, Mohammed Abdul Bari· Journal of Intelligent Decis...· 0 citations