Edge assisted, forecast integrated ensemble learning based service management scheme for delay minimization in smart cities applications

Abstract

As the Internet of Things (IoT) is maturing as a technology, innovative and cross-domain IoT applications have seen smart cities being conceived and designed across the globe, though with data and resource management challenges, Quality of Service (QoS) fulfilment challenges among others. These could also be addressed by means of context-aware fog computing at the edge of the network and also by incorporating intelligence at the network edge. Since workload at fog nodes can anytime see sudden changes in demand, hence load migration among fog nodes becomes viable. However, improper migration can lead to further migrations, eventually decreasing performance. In this paper, we present a multi-channel queuing model based smart distributed service management approach and an intelligent resource-aware forecasting technique to predict the required context and resource management. The scheme accomplishes live migration by a resource-aware ensemble forecast method that used current and predicted resource utilization and their context availabilities to address the delay requirement for cross-domain IoT applications. The proposed management algorithms are simulated using CloudSim simulator and the efficacy of the obtained results confirm the superiority of the proposed methods.