Dependent Task Offloading for End-Edge-Cloud Collaborative Computing Based on Deep Reinforcement Learning

The rapid expansion of the Internet of Things (IoT) and communication technology has significantly increased the volume of complex data generated by terminal devices. This has created a demand for more efficient terminal devices with limited computing resources and battery energy. As a result, there is a need to investigate effective task offloading for many tasks with complex dependencies. This paper proposes a task offloading algorithm based on deep reinforcement learning and task offloading sequence to address the issue of dependent task offloading in end-edge-cloud collaboration scenarios. The algorithm introduces the topological structure of a directed acyclic graph (DAG) to represent task dependencies and determine task execution order based on task priorities. It uses a Markov Decision Process (MDP) to optimize and minimize latency and energy consumption for all terminal devices. Experimental results indicate that the proposed algorithm demonstrates better convergence and performance compared to baseline algorithms across various scenarios. It effectively reduces latency and energy consumption for all terminal devices, achieving cost reductions of 13.81%, 67.33%, and 81.04% compared to the three baseline algorithms, respectively. These results illustrate that our algorithm significantly outperforms the baselines under the specified conditions.