Adaptive fog resource management through ML-based prediction SJF scheduling

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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.