CADCO: An Adaptive Dynamic Cloud-fog Computing Offloading Method for complex dependency tasks of IoT

Abstract

With the rapid development of the Internet of Things (IoT) and cloud-fog computing, efficient offloading of complex dependency tasks has become a key challenge for improving system performance, especially for real-time IoT applications. Traditional methods are inefficient in handling dynamic environments and long-range dependencies, while existing deep reinforcement learning approaches face issues such as rigid resource allocation and Q-value overestimation. To address these problems, we propose an Adaptive Dynamic Cloud-fog Computing Offloading Method for complex dependency tasks (CADCO). The method accurately models task dependencies using the multi-head attention mechanism of Transformer, optimizes computational and memory resource allocation through Hybrid Model Parallelism (HMP) technology, and designs a dynamic offloading strategy based on an improved Double Deep Q-Network (DDQN). A freshness factor is introduced to optimize the experience replay mechanism, enhancing the stability of the strategy. Experimental results show that CADCO demonstrates significant advantages in multi-user, multi-task offloading scenarios, optimizing task scheduling, improving resource utilization, and significantly enhancing QoS while reducing task latency and energy consumption. These results validate the practical application value of CADCO in complex task dependency environments, providing solid theoretical and experimental support for intelligent computing offloading optimization.