Smart Offloading Strategies by Optimizing Fog Computing Through Reinforcement Learning Strategies

With the proliferation of Internet of Things(IoT) devices and the exponential growth in data volume, fog computing has emerged as a promising paradigm to address the limitations of cloud-centric architectures by bringing computation and storage closer to the data source. In fog comp uting environments, efficient task offloading plays a crucial role in optimizing resource utilization and minimizing latency. This paper proposes a novel approach to smart offloading strategies utilizing reinforcement learning (RL) techniques. To validate the effectiveness of our approach, we conduct extensive simulations and experiments using representative fog computing scenarios. Results demonstrate significant improvements in offloading efficiency, latency reduction, and resource utilization compared to traditional static offloading methods.