Enhancement of QoE-Aware Application Allocation and Energy-Aware Module Allocation with Offloading Applications in Fog Computing

The Internet of Things (IoT) and other rapidly evolving technologies have profoundly affected daily life and created an exponential rise in the amount of data generated and processed. By extending cloud capabilities to the network edge, fog computing lowers latency and boosts the effectiveness of data processing. But it also brings with it new difficulties, especially regarding resource management and energy usage. This study starts with a thorough analysis of the current state of fog computing systems, pointing out weaknesses and areas for improvement. We suggest enhancing the current QoE-Aware application allocation algorithm, energy-aware module allocation methods, and task-offloading approaches to maximize resource efficiency in light of our research. Experiments in simulated fog computing environment are used to assess these methods, with an emphasis on performance measures including energy-aware module allocation metrics, QoE-aware application allocation enhancement, and offloading applications developing. As the result, we found that proposed QoE-aware applications allocation are having 35.34% shorter execution time, 1.47% lower power consumption and 10.56% lower network usage.