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<p>The rapid growth of IoT devices demands efficient resource allocation to satisfy low-latency and high-performance requirements in fog computing. However, most existing approaches predominantly rely on CPU-based resource scheduling, failing to leverage GPU resource potential for heterogeneous workloads. This paper introduces ARAMS (Adaptive Resource Allocation with Machine Learning and Shortest Job First), a novel hybrid framework that integrates machine learning–based CPU/GPU resource prediction with a greedy SJF scheduler to achieve real-time, adaptive task distribution. The technical novelty of ARAMS lies in its predictive allocation model that dynamically maps IoT workloads to heterogeneous fog resources based (CPU/GPU) on computational intensity and latency constraints. Experimental evaluations on real-world multi-domain IoT traffic datasets demonstrate that ARAMS achieves a 99.4% allocation ratio at 900-task capacity, reduces allocation time to 0.07 seconds, maintains latency below 0.12 seconds, and improves Strict Prediction–Constrained Allocation Accuracy (<i>Strict PCAA</i>) to 0.90, outperforming Hybrid Architecture (HA) and FCFS baselines. These results confirm ARAMS’s scalability, adaptivity, and computational efficiency for heterogeneous fog environments.</p>

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Adaptive fog resource management through ML-based prediction SJF scheduling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月24日

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  • Luthfan Hadi Pramono,
  • Shan-Hsiang Shen

摘要

The rapid growth of IoT devices demands efficient resource allocation to satisfy low-latency and high-performance requirements in fog computing. However, most existing approaches predominantly rely on CPU-based resource scheduling, failing to leverage GPU resource potential for heterogeneous workloads. This paper introduces ARAMS (Adaptive Resource Allocation with Machine Learning and Shortest Job First), a novel hybrid framework that integrates machine learning–based CPU/GPU resource prediction with a greedy SJF scheduler to achieve real-time, adaptive task distribution. The technical novelty of ARAMS lies in its predictive allocation model that dynamically maps IoT workloads to heterogeneous fog resources based (CPU/GPU) on computational intensity and latency constraints. Experimental evaluations on real-world multi-domain IoT traffic datasets demonstrate that ARAMS achieves a 99.4% allocation ratio at 900-task capacity, reduces allocation time to 0.07 seconds, maintains latency below 0.12 seconds, and improves Strict Prediction–Constrained Allocation Accuracy (Strict PCAA) to 0.90, outperforming Hybrid Architecture (HA) and FCFS baselines. These results confirm ARAMS’s scalability, adaptivity, and computational efficiency for heterogeneous fog environments.