Optimizing task offloading with metaheuristic algorithms across cloud, fog, and edge computing networks: A comprehensive survey and state-of-the-art schemes

Abstract

The Internet of Things (IoT) significantly impacts various industries, enabling better connectivity and real-time data exchange for applications ranging from smart cities to healthcare. Integrating cloud, fog, and edge computing is essential for managing increased data and processing needs as IoT networks become complex. Cloud computing provides extensive storage and powerful computing capabilities but can experience delays due to the distance data must travel. Fog computing addresses these delays by processing data closer to its source, while edge computing reduces them even further by processing data directly on IoT devices. Effective management of these computing layers requires strategic task offloading, which involves moving tasks to the most appropriate computing layer to balance latency, energy consumption, and operational efficiency. Several strategies have been developed to optimize network communication and task offloading, with metaheuristic algorithms emerging as promising approaches. Inspired by natural processes, these algorithms are skilled at searching complex spaces to find near-optimal solutions for efficient and dynamic task offloading. This review provides a detailed analysis of how metaheuristic algorithms optimize task offloading. It evaluates their effectiveness in improving system performance, managing resources, and reducing costs. The review also identifies the current challenges in this area and suggests future research directions to advance this field.