Task Offloading and Resource Allocation in Cloud-Edge Collaborative System Based on GA and DDPG

With the rapid development of the Internet of Things (IoT) and communication technologies, for the latency-sensitive and high-computing-load applications of terminals, the cloud servers in traditional cloud computing are located far away from the terminal devices, resulting in high communication delays and difficulty in meeting the requirements of low-latency tasks. While the edge node resources in traditional edge computing are limited, making it difficult to meet the growing high-computing-load requirements of terminals. This paper proposes a cloud-edge collaborative system architecture to solve the problem of task offloading and computing resource allocation including communication model and latency model, and proposes the problem formulation by minimizing the average task execution delay and maximizing the task success rate. Furthermore, this paper proposes a task offloading and resource allocation scheme based on Genetic Algorithm (GA) and Deep Deterministic Policy Gradient (DDPG). The global optimal learning rates of the Actor and Critic network of DDPG are obtained through GA pre-training. The global optimal learning rate of the Actor and Critic networks of DDPG is obtained through GA pre-training. Then, the optimal task offloading and resource allocation strategy is obtained through DDPG. Lastly, the effectiveness and superiority of the proposed scheme are verified through simulation experiments.