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.

近年来，通过并行执行应用程序的分布式环境解决了大的计算问题。而且，最近，雾计算或边缘计算作为一种新环境被应用于从设备收集数据并进行预处理在发送以进行云计算的主要处理之前完成。由于此类系统中的关键问题之一是任务调度，因此可以通过考虑减少能耗来解决此问题。在这项研究中，通过使用动态电压和频率缩放（DVFS）技术来引入能量感知方法来减少能耗。此外，为了构建有效的任务序列，应用了一种混合杂草优化和培养（IWO-CA）进化算法。实验结果表明，该算法在能耗方面对现有算法进行了改进。