Abstract:
Fog computing has emerged as a revolutionary paradigm to serve the massive data generated in the Internet of Things (IoT) environments. It can be considered a derivative of cloud computing that provides cloud-like services at the edge of the network. As such, it helps address the, often significant, issue of delays encountered when using cloud systems for the IoT. According to the literature, inefficient scheduling of user tasks in fog computing can actually result in higher delays than cloud computing. Hence, the real benefits of fog computing can only be obtained by applying effective job scheduling strategies. In fact, task scheduling is an NP-hard problem and requires optimal and efficient techniques to address issues of latency, response time, and the efficient resource utilization of resources available at the edge of the network. Given this, we propose a novel bio-inspired hybrid algorithm (NBIHA) which is a hybrid of modified particle swarm optimization (MPSO) and modified cat swarm optimization (MCSO). In the proposed scheme, the MPSO is used to schedule the tasks among fog devices and the hybrid of the MPSO and MCSO is used to manage resources at the fog device level. In the proposed approach, the resources are assigned and managed on the basis of the demand of incoming requests. The main objective of the proposed work is to reduce the average response time and to optimize resource utilization by efficiently scheduling the tasks and managing the fog resources available. The simulations are performed using iFogSim. The evaluation results show that the proposed approach (NBIHA) shows promising results in terms of energy consumption, execution time, and average response time in comparison to the state-of-the-art scheduling techniques.
