基于车边云协同的服务迁移优化策略研究

Research on Service Migration Optimization Strategy Based on Vehicle-Edge-Cloud Collaboration

摘要: 随着车联网的发展和智能交通系统的完善，移动边缘计算和边缘缓存技术不断发展，在车联网服务迁移问题上发挥了巨大作用。为了更加合理利用边缘缓存资源、减小时延，本文提出了一种联合优化时延和缓存策略的“车–边–云”协同服务迁移方案。在该架构下，网络中的车载终端、路侧单元和云服务器可以协同进行任务计算，进行动态服务迁移。通过使用基于Hawkes过程的方法，根据历史请求信息更新不同内容类型的流行度，选择合适的边缘计算节点和缓存内容。本文以最大化系统的长期效益为目标，降低时延和提高命中率，形式化服务迁移问题。这是一个具有挑战性的非凸问题。通过将该问题建模为马尔科夫决策过程，并引入深度强化学习求解最优问题，提出了一种结合长短期记忆网络和近端策略优化算法的服务迁移算法。仿真实验结果表明，提出的策略优化方法比其他策略有更好的性能。

Abstract: With the development of the Internet of Vehicles (IoV) and the improvement of intelligent transportation system, mobile edge computing (MEC) and edge cache technology are constantly developing, which play a huge role in the service migration of the Internet of vehicles. In order to make more reasonable use of edge cache resources and reduce the delay, this paper proposes a “Vehicle-Edge-Cloud” collaborative service migration scheme which jointly optimizes the delay and cache strategy. In this architecture, vehicles, Roadside Units (RSUs) and cloud server in the network can collaborate on task computation and dynamic service migration. By using a Hawkes process-based approach, the popularity of different content types is updated based on historical request information, and appropriate edge compute nodes and cached content are selected. To maximize the long-term benefits of the system, reduce the delay and improve the hit rate, formalize the problem of service migration. This is a challenging non-convex problem. By modeling the problem as Markov Decision Process (MDP) and introducing deep reinforcement learning (DRL) to solve the optimal problem, a service migration algorithm combining Long Short-Term Memory (LSTM) and Proximal Policy Optimization (PPO) algorithm is proposed. Simulation results show that the proposed strategy optimization method has better performance than other strategies.