Delay-Aware and Energy-Efficient Task Scheduling Using Strength Pareto Evolutionary Algorithm II in Fog-Cloud Computing Paradigm

Abstract

The exponential growth of technology and advent of the Internet of Things (IoT) paradigm have caused large volumes of data to be continuously generated from the intelligent devices. One common feature of these devices is their limited capabilities, hence, they are not able to process large volumes of generated data. However, the processing of these data in the cloud leads to high latency and high power consumption. Hence, providing services to the latency-sensitive IoT applications in the cloud is a challenging issue. Fog computing as a complement to the cloud, allows data to be processed near IoT devices. However, the resources in the fog layer are heterogeneous. Thus, the proper distribution of tasks among heterogeneous nodes while serving the task within the intended deadline is of great importance. In this paper, we have presented a task scheduling model in the fog-cloud paradigm, which formulates the task scheduling problem as a multi-objective optimization problem with the aim of minimizing service response time and the total energy consumption of the system, while considers deadline and load balancing constraints. Since the problem of task scheduling is np-hard, we have proposed a modified version of Strength Pareto Evolutionary Algorithm II (SPEA-II) with customized operators to achieve the optimal scheduling strategy. The experimental results reveal that the proposed scheme outperforms some benchmarking algorithms in terms of service response time and energy consumption. Furthermore, by optimal distribution of tasks among heterogeneous computing nodes, it leads to better resource utilization and improvement in the percentage of missed-deadline tasks.