Fog computing enhances the Quality of Service for Internet of Things applications, addressing issues arising from the significant data transfer volume of Internet of Things devices that leads to increased latency and bandwidth demands in cloud computing. The growth of the Internet of Things generates vast data for processing, straining the fog-cloud network. High data transmission rates can disrupt system operations, necessitating effective resource management. Prioritized tasks are executed through scheduling algorithms, with high-priority tasks executed at nearby fog nodes and complex tasks sent to cloud data centers. While FC reduces response time, it raises energy consumption for users, and vice versa for cloud computing. Addressing these challenges is crucial for optimal task allocation in fog-cloud computing systems. Therefore, this study proposes a hybrid multi-objective task scheduling algorithm using the jellyfish algorithm and simulated annealing algorithm to reduce energy consumption and execution time while respecting task priorities, which play a fundamental role in task distribution. The simulation results confirm the efficiency of the IJFSA algorithm when compared with advanced algorithms such as PWAO, EETSPSO, and IJFA in reducing execution time and energy consumption.
