Hybrid Fuzzy Spark Lion Whale Algorithm for Energy-Efficient Resource Allocation and Task Migration in Cloud Data Centers

Hybrid Constraint-Aware Metaheuristic for Energy-Efficient Task Scheduling and Resource Allocation in Cloud Computing with Colored Petri Nets

An Efficient Task Scheduling Approach in Cloud Computing Using Hybrid Fruit Fly and Ant Colony Optimization Techniques

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.

SPES: A Stochastic Predictive Energy-Aware Scheduling Approach for Efficient Multi-Region 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.

Hybrid fuzzy clustering with Golden Eagle Optimization Algorithm for fault tolerant load balancing in fog computing environment

Overall, HFC-GEOA offers a scalable, health-conscious, and fault-tolerant scheduler solution that fully leverages the best latency performance at scale with health-conscious energy usage and stable reliability in heterogeneous fog computing systems.

Energy-Efficient Hybrid Metaheuristic Resource Allocation Model for Dynamic Virtual Machine Scheduling in Cloud Computing Platforms

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.

Benchmarking Bio-Inspired Metaheuristics for Load-Aware Task Offloading in Hierarchical IoT-Fog-Cloud Ecosystems

The multi-objective nature of task scheduling for novel latency-sensitive applications in IoT-Fog-Cloud hierarchies presents persistent challenges, where optimizing one QoS metric often degrades another. Although bio-inspired algorithms provide adaptive solutions, existing comparative studies are constrained by narrow algorithmic scopes, limited evaluation metrics, and a notable absence of statistical validation. To bridge this gap, we introduce a novel unified benchmarking framework that systematically evaluates five prominent metaheuristics — Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Bacterial Foraging Optimization (BFO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC) — for load balancing within a three-tier architecture using the iFogSim simulator. We evaluate performance across three QoS dimensions (response time, makespan, and load imbalance degree) under three escalating workload intensities on heterogeneous infrastructure, with all experiments repeated over 10 independent runs. Statistical significance is assessed via the Wilcoxon signed-rank test against GA, selected as a widely established and computationally stable baseline. Our findings reveal no universally optimal algorithm. GA consistently maintains mean response times below 30.5 ms across all workloads, while PSO and BFO remain statistically indistinguishable from GA on makespan, highlighting their interchangeability under specific conditions. Conversely, ABC uniquely excels in distribution equity, reducing load imbalance from 2.64% to 1.32% as task density increases. ACO, however, incurs statistically significant penalties across all metrics, suffering from a pronounced cloud-bias that elevates load imbalance to 11.0% —up to eight times higher than its counterparts. Collectively, these results confirm the inherent trade-offs between latency, efficiency, and fairness. This reference benchmark delivers a reproducible, statistically validated performance baseline, offering system architects a clear empirical foundation for adaptive algorithm selection in heterogeneous Fog-Cloud environments.