论文标题
在支持MEC的车辆网络中，一种深厚的加强学习方法，用于服务迁移
A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networks
论文作者
论文摘要
多访问边缘计算（MEC）是减少车辆网络延迟的关键推动器。由于车辆流动性，应经常在不同的MEC服务器上迁移其要求的服务（例如信息娱乐服务），以确保其严格的服务质量要求。在本文中，我们研究了支持MEC的车辆网络中的服务迁移问题，以最大程度地减少服务延迟和迁移成本。此问题被公正为非线性整数程序，并线性化以帮助使用现成的求解器获得最佳解决方案。然后，为了获得有效的解决方案，它被建模为多代理马尔可夫决策过程，并通过利用深Q学习（DQL）算法来解决。提出的DQL方案执行了主动的服务迁移，同时确保其在高移动性限制下的连续性。最后，模拟结果表明，所提出的DQL方案实现了近距离的性能。
Multi-access edge computing (MEC) is a key enabler to reduce the latency of vehicular network. Due to the vehicles mobility, their requested services (e.g., infotainment services) should frequently be migrated across different MEC servers to guarantee their stringent quality of service requirements. In this paper, we study the problem of service migration in a MEC-enabled vehicular network in order to minimize the total service latency and migration cost. This problem is formulated as a nonlinear integer program and is linearized to help obtaining the optimal solution using off-the-shelf solvers. Then, to obtain an efficient solution, it is modeled as a multi-agent Markov decision process and solved by leveraging deep Q learning (DQL) algorithm. The proposed DQL scheme performs a proactive services migration while ensuring their continuity under high mobility constraints. Finally, simulations results show that the proposed DQL scheme achieves close-to-optimal performance.
