CLEMO: Cost, load, energy, and makespan-based optimized scheduler for internet of things applications in cloud-fog environment

The rapid expansion of Internet of Things (IoT) devices has led to a substantial increase in data that needs to be processed efficiently. However, IoT devices face numerous challenges like constrained computational power, limited storage capacity, and finite battery life that hinder their ability to process extensive data efficiently. To address these issues, IoT devices are using the advantages of cloud and fog computing to process large tasks. However, task scheduling in cloud fog is another challenge as it is an NP-hard problem. In this study, we have introduced Cost, Load, Energy and Makespan based Optimized task scheduler (CLEMO) that assigns tasks to different cloud and fog nodes considering the cost, load, energy usage, and makespan involved in processing the dependent tasks. The CLEMO aims to identify optimal solutions for task scheduling in cloud fog by harnessing the inherent capabilities of genetic algorithms. To assess the performance of CLEMO, we conducted various exhaustive experiments and compared the results with other state-of-the-art methods. The outcomes demonstrate that the CLEMO outperforms other methods in terms of cost, load distribution, energy efficiency, and makespan. That indicated that the proposed method can make IoT applications more cost-efficient, conserve energy effectively with better execution time, and optimally utilize available resources in a cloud-fog environment.