Adaptive fog resource management through ML-based prediction SJF scheduling
TL;DR
Experimental evaluations demonstrate ARAMS’s scalability, adaptivity, and computational efficiency for heterogeneous fog environments and confirm its scalability, adaptivity, and computational efficiency for heterogeneous fog environments.
Abstract
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.