Adaptive multi-criteria-based load balancing technique for resource allocation in fog-cloud environments
<|reference_start|>Adaptive multi-criteria-based load balancing technique for resource allocation in fog-cloud environments: Recently, to deliver services directly to the network edge, fog computing, an emerging and developing technology, acts as a layer between the cloud and the IoT worlds. The cloud or fog computing nodes could be selected by IoTs applications to meet their resource needs. Due to the scarce resources of fog devices that are available, as well as the need to meet user demands for low latency and quick reaction times, resource allocation in the fog-cloud environment becomes a difficult problem. In this problem, the load balancing between several fog devices is the most important element in achieving resource efficiency and preventing overload on fog devices. In this paper, a new adaptive resource allocation technique for load balancing in a fog-cloud environment is proposed. The proposed technique ranks each fog device using hybrid multi-criteria decision-making approaches Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS), then selects the most effective fog device based on the resulting ranking set. The simulation results show that the proposed technique outperforms existing techniques in terms of load balancing, response time, resource utilization, and energy consumption. The proposed technique decreases the number of fog nodes by 11%, load balancing variance by 69% and increases resource utilization to 90% which is comparatively higher than the comparable methods.<|reference_end|>
@article{gad-elrab2024adaptive,
    title={Adaptive multi-criteria-based load balancing technique for resource
  allocation in fog-cloud environments},
    author={Ahmed A. A. Gad-Elrab, Almohammady S. Alsharkawy, Mahmoud E. Embabi,
  Ahmed Sobhi, Farouk A. Emara},
    journal={arXiv preprint arXiv:2402.01326},
    year={2024},
    doi={10.5121/ijcnc.2024.16107},
    archivePrefix={arXiv},
    eprint={2402.01326},
    primaryClass={cs.DC}
}