Jul 2026· International Journal of Online and Biomedical Engineering (iJOE)· 0 citations· 12 references
TL;DR
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.
Abstract
Although cloud computing offers on-demand resources, effective job scheduling is still a major challenge to increase efficiency and resource usage. For large-scale, dynamic workloads, traditional algorithms s FCFS and Min-Min are straightforward yet ineffective. In order to generate high-quality task–VM mappings, this study suggests 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). Minimizing makespan, optimizing resource usage, and distributing load evenly among virtual machines (VM) are the objectives. Workload traces from PlanetLab and CloudSim 3.0.3 are used to assess the method in a variety of experimental scenarios involving up to 1000 jobs and numerous diverse VM. The suggested FOA-ACO methodology enhances overall cloud performance by decreasing the makespan by 9.57%, employing more resources by 7.14%, and enhancing the load balancing factor (LBF) by 23.06%. This was observed by comparing it with other approaches such as FCFS, Min-Min, solo ACO, and WOA. An effective solution to the scheduling problems in today’s cloud environments is provided by this hybrid method. Future research may concentrate on refining the system to take into consideration various objectives, such as cost and reliability, enhancing its use of energy, and optimizing its performance with very massive cloud setups. Also, the approach might be refined to facilitate decision-making in real time and incorporate machine learning methods for greater flexibility.
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
The findings suggest that HORAM is far better at using resources; fewer tasks are completed, and the total power consumed is lower than with traditional scheduling algorithms, suggesting the suggested architecture is a viable solution to sustainable cloud infrastructure management.
S. Balakrishnan, K. Aravind, ·. T. Veeramani et al.· SN Computer Science· 0 citations
This paper explores sophisticated Virtual Machine (VM) scheduling approaches in cloud computing and their significance to enhance resource distribution, improve system efficiency, and cost reduction. It provides a recent overviews of key scheduling algorithms, including heuristic, metaheuristic, advanced machine learning-based and hybrid approaches, while assessing their respective strengths, weaknesses and practical applications. The discussion encompasses their applications in managing workloads, optimizing costs, enhancing energy efficiency, improving Quality of Service (QoS) and with a particular focus on scalability and real-time scheduling in cloud settings. Furthermore, the paper analyzes scheduling strategies adopted by major cloud providers through real-world case studies. Ultimately, our analysis identifies critical VM scheduling trade-offs, provides optimization guidelines, and validates the efficacy of hybrid adaptive methods via a new proposed heuristic-machine learning model for dynamic cloud environments.
Chaimae Bahij, Mohamed El Ghmary, Hassan Echoukairi· EPJ Web of Conferences· 0 citations
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
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.
Annaiah H, A. Rajesh· International journal of com...· 0 citations
: Edge computing, a geographically distributed set of computing platforms, is crucial in modern IoT applications for faster processing and application execution. Effective task scheduling in edge-cloud computing enhances resource utilization, lowers makespan, reduces energy consumption, and achieves cost-effectiveness, meeting new time measure requirements for the modern world. This paper presents a new Hybrid Hitchcock Bird Algorithm (HHBA) and Genetic Algorithm (GA)-based dynamic task scheduling method that combines the advantages of HHBA and GA algorithms. While GA operates on the solution space to evolve a more effective solution through selection and mutation operations, refining the task allocation by promoting the use of two-point crossover and uniform crossover for the topic planning in trades, HHBA can be considered as the optimizer, honing in on the set of sub-tasks through local comparisons while ensuring an equal workload among the resources. Experiments indicate that HHBA-GA achieves the best performance in makespan, energy consumption, and cost efficiency compared with GA, PSO, and standalone HHBA. The proposed pathway leads to an increase in energy savings of up to 24% and a reduction in cost reduction of about 22% when compared to traditional approaches. Furthermore, the model significantly enhances resource usage, proving its effectiveness in large-scale edge-cloud applications.
P. Thai, S. Chandre· Proceedings of the 1st Inter...· 0 citations