Search

In recent years, large computational problems have beensolved by the distributed environment in which applications are executed in parallel. Also, lately, fog computing or edge computing as a new environment is applied to collect data from the devices and preprocessing is done before sending for main processing in cloud computing. Since one of the crucial issues in such systems is task scheduling, this issue is addressed by considering reducing energy consumption. In this study, an energy-aware method is introduced by using the Dynamic Voltage and Frequency Scaling (DVFS) technique to reduce energy consumption. In addition, in order to construct valid task sequences, a hybrid Invasive Weed Optimization and Culture (IWO-CA) evolutionary algorithm is applied. The experimental results revealed that the proposed algorithm improves some current algorithms in terms of energy consumption. • Proposing an energy-aware task scheduling approach to minimize energy consumption in fog computing. • Presenting a hybrid invasive weed optimization (IWO) along with the cultural evolution algorithm (CEA) for proposed task scheduling approach in fog computing. • Guaranteeing optimal solutions for proposed energy-aware task scheduling approach based on maximum level of QoS factors. [ABSTRACT FROM AUTHOR]