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<p>The Internet of Things (IoT) has boosted fog computing, which complements the cloud. This is critical for applications that need close user proximity. Efficient allocation of IoT applications to the fog, as well as fog device scheduling, enabling the realistic execution of IoT application deployment in the fog environment. The scheduling difficulties are multi-objective in nature, since they must handle the issues of avoiding resource waste, network latency, and maximising Quality of Service (QoS) on fog nodes. In this research, the Hybrid Multi-Objective Marine Predators Algorithm-based Clustering and Fog Picker (HMMPACFP) Technique is developed as a combinatorial model for tackling the problem of fog node allocation, with the goal of achieving dynamic scheduling using lightweight characteristics. Utilised Fog Picker to allocate IoT components to fog nodes based on QoS parameters. Simulation trials of the proposed HMMPACFP scheme utilising iMetal and iFogSim with Hypervolume (HV) and Generational Distance (IGD) demonstrated its superiority over the benchmarked methodologies utilised for evaluation. The combination of Fog Picker with the suggested HMMPACFP scheme resulted in 32.18% faster convergence, 26.92% more solution variety, and a better balance between exploration and exploitation rates.</p>

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Hybrid multi objective marine predators algorithm based clustering for lightweight resource scheduling and application placement in fog

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月7日

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  • R. Baskar,
  • E. Mohanraj

摘要

The Internet of Things (IoT) has boosted fog computing, which complements the cloud. This is critical for applications that need close user proximity. Efficient allocation of IoT applications to the fog, as well as fog device scheduling, enabling the realistic execution of IoT application deployment in the fog environment. The scheduling difficulties are multi-objective in nature, since they must handle the issues of avoiding resource waste, network latency, and maximising Quality of Service (QoS) on fog nodes. In this research, the Hybrid Multi-Objective Marine Predators Algorithm-based Clustering and Fog Picker (HMMPACFP) Technique is developed as a combinatorial model for tackling the problem of fog node allocation, with the goal of achieving dynamic scheduling using lightweight characteristics. Utilised Fog Picker to allocate IoT components to fog nodes based on QoS parameters. Simulation trials of the proposed HMMPACFP scheme utilising iMetal and iFogSim with Hypervolume (HV) and Generational Distance (IGD) demonstrated its superiority over the benchmarked methodologies utilised for evaluation. The combination of Fog Picker with the suggested HMMPACFP scheme resulted in 32.18% faster convergence, 26.92% more solution variety, and a better balance between exploration and exploitation rates.