Aug 2026· Scientific Reports· Vol 16· 0 citations· 39 references
Medicine
TL;DR
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.
Abstract
Fog Computing (FC), when integrated with emerging 5G technologies, provides significant potential to reduce latency and enhance Quality of Service (QoS). Nevertheless, current scheduling strategies tend to lack the in-depth fault-tolerance and load balancing provisions as virtual machine (VM) health indicators, including CPU utilization, memory status, and reliability are not specifically taken into account during task assignment. To overcome these difficulties, this paper will propose a Hybrid Fuzzy Clustering with Golden Eagle Optimization Algorithm (HFC-GEOA). The framework incorporates capacity aware VM clustering into three levels (HCC, MCC, LCC), fuzzy-based task prioritization and localized metaheuristic optimization, which allows efficient mapping of tasks to VM under resource constraints and adaptive inter-cluster migration. Extensive scaling simulations in iFogSim2 using five task-VM scenarios (1,000-20,000 tasks; 20-1,500 VMs) of 30 independent runs demonstrate that HFC-GEOA is a strong and consistent scale performer. In large scale scenarios HFC-GEOA is used to reduce the average turnaround time by up to 78% over FGELB, 71% over ACO-LWC, and 85% over FCLB. The greatest scenario shows improvement of up to 87% of the average wait time over FGELB and 81% improvement over ACO-LWC and 91% improvement over FCLB. The consumption of energy has been competitive as it has remained 11 to 16% less than ACO-LWC over S3-S5. The stably maintained system reliability at 0.71-0.74 across all scenarios more than twice that of EWOA (0.31-0.33) and the failure rates are kept in control under 2.88 to 3.81%. The fault tolerance score increases progressively when compared to and exceeding EWOA (0.77) and FCLB (0.71) at large scale, and as the system resilience to scale improves. 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.
Ensuring reliability and real-time performance of task offloading in fog computing remains a critical challenge. To address this, this article considers a dual-objective optimization problem of reliability and execution time for task offloading in energy-constrained fog computing. We first propose a more realistic fog computing system model that incorporates Rayleigh fading. Second, we introduce a reliability and time balanced Pareto ant colony optimization algorithm (RTPACO) based on the Pareto ant colony optimization (PACO algorithm. This algorithm is specifically designed for task offloading scenarios in fog computing. Lastly, we compared RTPACO with other multiobjective optimization algorithms using several metrics, including convergence and diversity (evenness and spread). To evaluate the performance of the algorithms, we employed the widely-used Hypervolume (HV) metric. The experimental results demonstrate that RTPACO consistently achieves a superior Pareto front, with HV improvements ranging from 17.2% to 50.2% compared to existing algorithms.
Xiaochuan Guo, Jia Wei, Wufei Wu et al.· IEEE Transactions on Reliabi...· 0 citations
The results empirically quantify the necessary tradeoff between aggressive hardware consolidation and Service Level Agreement preservation, establishing Fuzzy-SLW as a scalable solution for power-constrained hyper-scale environments.
Nidhi Chauhan, Navneet Kaur, Jawad Khan et al.· Computers, Materials & C...· 0 citations
The intelligible Virtual Machine (VM) migration in federated cloud systems is important for achieving
energy conservation, workload balancing and Service Level Agreement (SLA) compliance. Traditional heuristics
and hybrid metaheuristics though effective fail to address qualms and hesitation in workload assessment, leading
to needless migrations and SLA violations. To these limitations this study makes known to IF-FLAME
(Intuitionistic Fuzzy Firefly Lion Advanced Migration using Exploration and Exploitation) an improved hybrid
optimization framework that fit in intuitionistic fuzzy logic with Firefly and Lion metaheuristics. Different
conservative fuzzy systems the intuitionistic fuzzy model books for truth, falsity and hesitation degrees in task
prioritization, safeguarding more robust migration results under hesitation. Experimental assessment in CloudSim
demonstrates that IF-FLAME attains significant developments over F-FLAME as well as abridged SLA violation
rate (from 90% to 8%), minimalized migration energy cost, better quality throughput, and better deadline
adherence. These consequences found IF-FLAME as a maintainable, SLA-compliant, and energy-aware migration
model for federated clouds.
Karunya N, D. Devi· International Journal of Dru...· 0 citations
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.
Lamia Oualili, Mohamed El Ghmary, Hassan Echoukairi· International Journal of Adv...· 0 citations
This 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
This research proposes a cost-aware, genetic-based task scheduling algorithm tailored for fog-cloud environments, which seeks to improve cost efficiency for real-time applications with strict deadlines, and demonstrates that the proposed algorithm surpasses existing techniques like Round-Robin and Trade-off algorithms.
Youssef Oukissou, Hamza Elhaou, Driss Ait Omar et al.· 1 citation
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