Jun 2026· Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications· 0 citations
TL;DR
The proposed Enhanced Dragonfly-Firefly Optimization (EDFO) algorithm, coupled with an Improved Advanced Encryption Standard (IAES) mechanism for safe and effective scheduling of cloud tasks, is proposed and shown to be much more effective than currently used methods such as DFGA and IBPSO-LBS.
Abstract
The large number of users of the cloud has resulted in a rapid increase in the number of requests for
tasks, and scheduling tasks efficiently and load balancing them across non-homogeneous virtual
machines (VMs) has become an urgent matter. This is a problem that is inherently NP-hard,
especially when the Objective is to optimize multiple Objectives, such as makespan, execution cost,
scheduling time, and resource utilization. Current meta-heuristic methods, such as the Particle
Swarm Optimization (PSO), are computationally expensive and do not converge to global optimality
when applied in isolation. To overcome these shortcomings, this paper proposes an Enhanced
Dragonfly-Firefly Optimization (EDFO) algorithm, coupled with an Improved Advanced
Encryption Standard (IAES) mechanism for safe and effective scheduling of cloud tasks. The EDFO
model was an effort to merge the global search capability of the Dragonfly algorithm and the local
refinement strength of the Firefly algorithm. The multi-objective function is formulated to minimize
the maximum difference in VM completion times and to achieve even workloads. The results of the
proposed EDFO-IAES are experimentally evaluated and shown to be much more effective than
currently used methods such as DFGA and IBPSO-LBS. In particular, the imbalance level decreases
to 0.45, the average storage usage increases to about 94%, and makepan is minimized by nearly a
quarter across the workload variabilities. Besides, incorporating IAES increases data security
without imposing a substantial computational burden. All in all, the suggested framework is more
efficient, scalable, and secure, which is why it can be considered a viable solution for a real-life
cloud computing setup.
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
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
: Cloud Computing (CC) is one of the widely used technologies due to its advanced features such as pay-per-use, scalability, and flexibility. The primary objective of CC is to allow users to access and purchase cloud services that are on demand through internet-based applications. Efficient load-balancing in the cloud faces challenges of high-dimensional state spaces and scalability with increasing tasks. To solve this problem, the Masterpiece Optimization Algorithm (MOA) with a priority constraint is employed for load-balancing according to the tasks efficiently. The MOA is integrated with a priority-based cost function to enhance the task scheduling process by introducing a multi-dimensional approach for load balancing. The priority-based framework helps the scheduler to dynamically recalibrate workloads. The experimental results achieve a total energy consumption of 39.8 W and an average CPU resource utilization of 99.54%, which is better than the existing algorithms, such as the hybrid Particle Swarm Grey Wolf Optimization (PSGWO) algorithm.
S. Vijaykumar, S. Chandre· Journal of Computer Science· 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 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
Cloud task scheduling is a critical optimization problem in cloud computing environments, aiming to allocate computational tasks to appropriate virtual machines while reducing execution time, balancing resource load, and minimizing scheduling cost. However, due to the high dimensionality, nonlinear characteristics, and complex constraints of cloud scheduling scenarios, traditional optimization methods often struggle to obtain high-quality solutions efficiently. To address these challenges, this paper proposes a Multi-strategy Improved Golden Sine Optimization Algorithm (MIGoldSA) for global optimization and cloud task scheduling problems. First, an adaptive chaotic opposition initialization strategy is incorporated to improve the distribution quality and diversity of the initial population. Second, a dynamic elite-guided sine evolution strategy is designed to reduce the dependence on a single best individual and improve the coordination between global exploration and local exploitation. Third, an Economic Investment Strategy is introduced to adaptively allocate search efforts according to the optimization potential of individuals. To verify the effectiveness of MIGoldSA, extensive experiments are conducted on the IEEE CEC2017 and CEC2022 benchmark suites and compared with nine advanced optimization algorithms. The results show that MIGoldSA obtains the best or tied-best mean fitness values on 60 out of 84 benchmark cases, accounting for 71.43% of all test cases. In the Wilcoxon signed-rank test, MIGoldSA achieves 662 wins, 57 ties, and 37 losses among 756 pairwise comparisons, corresponding to an overall win rate of 87.57% and a non-inferiority rate of 95.11%. In addition, the Friedman mean ranks of MIGoldSA are 1.47, 2.00, 3.98, and 4.17 under the four benchmark settings, which are reduced by 85.26%, 79.94%, 45.25%, and 42.32%, respectively, compared with the original GoldSA. Furthermore, the proposed algorithm is applied to cloud task scheduling problems under different task scales. The experimental results show that MIGoldSA maintains competitive time-cost performance and achieves clear reductions in load cost, price cost, and comprehensive scheduling cost. Compared with the original GoldSA, the normalized comprehensive scheduling cost is reduced by approximately 9–14% in small-scale scenarios and approximately 18–21% in large-scale scenarios. Meanwhile, the normalized load cost and price cost are reduced by about 18–25% and 10–18%, respectively, and the time cost shows an approximately 8–12% reduction in large-scale scheduling scenarios. These quantitative results demonstrate that MIGoldSA can improve the optimization accuracy, statistical robustness, and overall scheduling cost efficiency of the original GoldSA on most tested problems.