Image de couverture de livre

A Hierarchical Fractal Space NSGA-II-Based Cloud–Fog Collaborative Optimization Framework for Latency and Energy-Aware Task Offloading in Smart Manufacturing

Enregistré dans:
Publié dans:Mathematics vol. 13, no. 22 (2025), p. 3691-3720
Auteur principal: Lin, Zhiwen
Autres auteurs: Chen, Chuanhai, Chen, Jianzhou, Liu, Zhifeng
Publié:
MDPI AG
Sujets:
Energy management
Parallel processing
Collaboration
Computer architecture
Mathematical models
Task complexity
Fractals
Architecture
Multiple objective analysis
Manufacturing
Energy consumption
Intelligent manufacturing systems
Distributed processing
Business metrics
Scheduling
Edge computing
Genetic algorithms
Process controls
Digital twins
Computation offloading
Pareto optimization
Design
Energy efficiency
Optimization algorithms
Accès en ligne:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
Résumé:The growth of intelligent manufacturing systems has led to a wealth of computation-intensive tasks with complex dependencies. These tasks require an efficient offloading architecture that balances responsiveness and energy efficiency across distributed computing resources. Existing task offloading approaches have fundamental limitations when simultaneously optimizing multiple conflicting objectives while accommodating hierarchical computing architectures and heterogeneous resource capabilities. To address these challenges, this paper presents a cloud–fog hierarchical collaborative computing (CFHCC) framework that features fog cluster mechanisms. These methods enable coordinated, multi-node parallel processing while maintaining data sensitivity constraints. The optimization of task distribution across this three-tier architecture is formulated as a multi-objective problem, minimizing both system latency and energy consumption. To solve this problem, a fractal-based multi-objective optimization algorithm is proposed to efficiently explore Pareto-optimal task allocation strategies by employing recursive space partitioning aligned with the hierarchical computing structure. Simulation experiments across varying task scales demonstrate that the proposed method achieves a 20.28% latency reduction and 3.03% energy savings compared to typical and advanced methods for large-scale task scenarios, while also exhibiting superior solution consistency and convergence. A case study on a digital twin manufacturing system validated its practical effectiveness, with CFHCC outperforming traditional cloud–edge collaborative computing by 12.02% in latency and 11.55% in energy consumption, confirming its suitability for diverse intelligent manufacturing applications.
ISSN:2227-7390
DOI:10.3390/math13223691
Source:Engineering Database