Load scheduling has been a major challenge in distributed fog computing environments for meeting the demands of decision-making in real-time. This research proposes an quantumized approach for scheduling heterogeneous tasks in fog computing-based applications. Specifically, a node-specific metric is defined in terms of Node Computing Index for estimating the computational capacity of fog computing nodes. Moreover, QCI-Neural Network Model is proposed for predicting the optimal fog node for handling the heterogeneous task in real-time. In order to validate the proposed approach, experimental simulations were performed in different cases using 5, 10, 15, 20 fog nodes to schedule heterogeneous tasks obtained from online Google Job datasets. A comparative analysis was performed with state-of-the-art scheduling models like Heterogeneous Earliest Finish Time, Min–Max, and Round Robin were used for comparative analysis to determine performance enhancement. Better performance was acquired for the proposed approach with execution delay of 30.01s for 20 nodes. In addition to this, high values of statistical estimators like specificity (90.99%), sensitivity (89.76%), precision (91.15%) and coverage (94.56%) were registered to depict the enhancement in overall system performance.

中文翻译：

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雾计算环境下物联网应用负载调度的量化方法

负载调度一直是分布式雾计算环境中满足实时决策需求的主要挑战。这项研究提出了一种量子化方法，用于在基于雾计算的应用程序中调度异构任务。具体来说，根据节点计算指数定义了一个节点特定的度量标准，用于估计雾计算节点的计算能力。此外，提出了 QCI-神经网络模型，用于预测实时处理异构任务的最佳雾节点。为了验证所提出的方法，使用 5、10、15、20 个雾节点在不同情况下进行了实验模拟，以调度从在线 Google 工作数据集获得的异构任务。使用最先进的调度模型（如异构最早完成时间、最小-最大和循环）进行了比较分析，用于比较分析以确定性能增强。所提出的方法获得了更好的性能，20 个节点的执行延迟为 30.01 秒。除此之外，还记录了统计估计量的高值，如特异性 (90.99%)、灵敏度 (89.76%)、精确度 (91.15%) 和覆盖率 (94.56%)，以描述整体系统性能的增强。