Quantumized approach of load scheduling in fog computing environment for IoT applications

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.