School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, Hebei, China; Hebei Key Laboratory of Marine Perception Network and Data Processing, Qinhuangdao, 066004, Hebei, China.
School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, Hebei, China.
Neural Netw. 2024 Nov;179:106621. doi: 10.1016/j.neunet.2024.106621. Epub 2024 Aug 8.
Vehicular edge computing (VEC), a promising paradigm for the development of emerging intelligent transportation systems, can provide lower service latency for vehicular applications. However, it is still a challenge to fulfill the requirements of such applications with stringent latency requirements in the VEC system with limited resources. In addition, existing methods focus on handling the offloading task in a certain time slot with statically allocated resources, but ignore the heterogeneous tasks' different resource requirements, resulting in resource wastage. To solve the real-time task offloading and heterogeneous resource allocation problem in VEC system, we propose a decentralized solution based on the attention mechanism and recurrent neural networks (RNN) with a multi-agent distributed deep deterministic policy gradient (AR-MAD4PG). First, to address the partial observability of agents, we construct a shared agent graph and propose a periodic communication mechanism that enables edge nodes to aggregate information from other edge nodes. Second, to help agents better understand the current system state, we design an RNN-based feature extraction network to capture the historical state and resource allocation information of the VEC system. Thirdly, to tackle the challenges of excessive joint observation-action space and ineffective information interference, we adopt the multi-head attention mechanism to compress the dimension of the observation-action space of agents. Finally, we build a simulation model based on the actual vehicle trajectories, and the experimental results show that our proposed method outperforms the existing approaches.
车联网边缘计算（VEC）是新兴智能交通系统发展的一种有前途的范例，可为车辆应用提供更低的服务延迟。然而，在资源有限的 VEC 系统中，满足具有严格延迟要求的此类应用的要求仍然是一个挑战。此外，现有的方法侧重于在某个时隙内使用静态分配的资源处理卸载任务，但忽略了异构任务的不同资源要求，导致资源浪费。为了解决 VEC 系统中实时任务卸载和异构资源分配问题，我们提出了一种基于注意力机制和递归神经网络（RNN）的分散式解决方案，该解决方案具有多代理分布式深度确定性策略梯度（AR-MAD4PG）。首先，为了解决代理的部分可观察性问题，我们构建了一个共享代理图，并提出了一种定期通信机制，使边缘节点能够从其他边缘节点聚合信息。其次，为了帮助代理更好地理解当前的系统状态，我们设计了一个基于 RNN 的特征提取网络，以捕获 VEC 系统的历史状态和资源分配信息。第三，为了解决联合观察动作空间过大和信息干扰无效的挑战，我们采用多头注意力机制来压缩代理的观察动作空间的维度。最后，我们基于实际车辆轨迹建立了一个仿真模型，实验结果表明我们提出的方法优于现有方法。
