Abstract: In the Internet of Vehicles (IoV), the limited computational capabilities of vehicles, the dynamic computational resources of edge servers, and the remote deployment locations of cloud servers pose significant challenges in designing computation offloading and resource allocation schemes. This paper proposes a deep reinforcement learning-based joint computation offloading and resource allocation algorithm, aiming at minimizing the weighted sum of latency and energy consumption for processing computational tasks. Specifically, to enable collaborative processing of computational tasks between edge and cloud servers, a software-defined networking (SDN) based edge-cloud collaborative network architecture is first designed, along with a metric for task priority. Subsequently, computational models for cloud-edge-end device tasks are established separately. Then, an objective function is designed to optimize system latency and energy consumption, which is transformed into a system utility function. Finally, a deep reinforcement learning algorithm is utilized to determine computation offloading and resource allocation strategies based on the system utility. Experimental results demonstrate that, compared to existing algorithms, the proposed algorithm significantly outperforms benchmark algorithms in reducing system latency and energy consumption, as well as improving the success rate of task computation.

Key words: computation offloading, resource allocation, cloud computing, mobile edge computing, deep reinforcement learning