Cost Aware Scheduling in Hybrid Cloud-Fog Environment Using Earthworm Optimization Algorithm

With the increasing complexity of tasks and dynamic resource requirements, effective scheduling becomes critical in minimizing operational costs. In this paper, we propose a novel Cost-Aware Scheduling approach in a hybrid Cloud-Fog environment using the Earthworm Optimization Algorithm (EOA). The hybrid cloud-fog paradigm, while offering reduced latency and increased efficiency, introduces challenges in balancing communication, computation, and energy costs. Our approach leverages EOA's unique foraging behavior to optimize task allocation by minimizing total cost across cloud and fog nodes. Key performance metrics such as computation cost, communication cost, and energy consumption are prioritized in the optimization process. Simulations demonstrate that the proposed algorithm outperforms existing approaches by reducing overall costs while maintaining high resource utilization and system efficiency in real-time Task Scheduling (TS) scenarios. This scheduling strategy is particularly beneficial for IoT applications, where cost efficiency is crucial for sustainable operation.