Energy Conscious Squirrel Search based Task Scheduling Mechanism in Fog Environment

The swift explosion of Internet of Things (IoT) devices in the past few years has resulted in a deluge of data that requires timely processing. However, transmitting such massive amounts of data in and out from cloud leads to high latency and network bandwidth. Consequently, the field of fog computing has turned out as a feasible platform for utilizing resources within the vicinity of IoT devices for providing services in a timely manner. The resource constrained trait of fog nodes demands judicious management of resources in satisfying the requirements of IoT applications. This makes energy efficiency a core issue in fog networks. This article addresses the issue of energy efficiency by proposing an energy conscious squirrel search algorithm (EcSSA) for scheduling tasks in fog environment. A salient feature of EcSSA lies in the novel objective function that aims to optimize the energy consumption by minimizing the cumulative active time of all nodes in the network. Simulation studies conducted as part of this work demonstrate that the proposed approach significantly improves average energy consumption, average makespan, and carbon dioxide emission rate in comparison to the existing algorithms. We propose an energy conscious task scheduling approach (EcSSA) for fog computing environments by making use of a recent nature inspired approach called squirrel search algorithm (SSA). The objective function to minimize the energy consumption was achieved by minimizing cumulative active time of all nodes in the network.