Multilevel Ensemble Model for Load Prediction on Hosts in Fog Computing Environment

Abstract

With the growing demand for various IoT applications, fog nodes frequently become overloaded. Fog computing requires effective load balancing to maximize resource utilization. It is essential to determine the load on host to obtain workload consolidation. Various random parameters, including CPU utilization, the number of CPU cores, RAM, memory allocated, memory available, disk I/O, and network I/O are employed to better comprehend host workloads. In the proposed work, the host's load status is detected using an ensemble approach into three categories: under-loaded, balanced and overloaded. Further, the proposed work considers three case studies and varying numbers of virtual machines (VMs) are executed with various parameter combinations. In each case study, a different number of VMs are executed in parallel on two different platforms. In the proposed study, we predicted the load on multiple hosts by employing a variety of advanced machine-learning models. To construct an ensemble model, we selected models with higher accuracy based on retrieved performance evaluation criteria. The ensemble method is applied to deal with the worst-case scenario of the model prediction. For a selected number of case studies, the Random Forest model, Ada Boost Classifier, Gradient Boost and Decision Tree models perform better than other models. These state-of-the-art predictive models are outperformed by our proposed ensemble model and achieves an improved accuracy of nearly 82 % by correctly classifying hosts as overloaded, underloaded and balanced.