Mobile-edge computing provisions computing and storage resources by deploying edge servers (ESs) at the edge of the network to support ultralow delay and high bandwidth services. To ensure QoS of latency-sensitive services in vehicular networks, service migration is required to migrate data of the ongoing services to the closest ES seamlessly when users move across different ESs. To achieve seamless service migration, path selection is proposed to obtain one or more paths (consisting of several switches and ESs) to transfer service data. We focus on the following problems about path selection: 1) where to implement path selection? 2) how to coordinate interests of mobile users (i.e., vehicles) and network providers since they have conflicting interests during path selection? and 3) how to ensure seamless service migration during the migration of vehicles? To address the above problems, this article investigates path selection for seamless service migration. We propose a path-selection algorithm to jointly optimize both interests of the network plane (i.e., the cost for network providers) and service plane (i.e., QoE of users). We first formulate it as a multiobjective optimization problem and further prove theoretically that the proposed algorithm can give a weakly Pareto-optimal solution . Moreover, to improve the scalability of the proposed algorithm, a distance-based filter strategy is designed to eliminate undesired switches in advance. We conduct experiments on two synthesized data sets and the results validate the effectiveness of the proposed algorithm.

中文翻译：

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车辆边缘计算中无缝服务迁移的路径选择

移动边缘计算通过在网络边缘部署边缘服务器（ES）来提供计算和存储资源，以支持超低延迟和高带宽服务。为了确保车载网络中对延迟敏感的服务的QoS，需要进行服务迁移，以在用户跨不同ES移动时将正在进行的服务的数据无缝迁移到最近的ES。为了实现无缝服务迁移，提出了路径选择以获得一条或多条路径（由多个交换机和ES组成）以传输服务数据。我们关注与路径选择有关的以下问题：1）在哪里实现路径选择？2）如何协调移动用户的利益（即 车辆和网络提供商，因为它们在路径选择过程中利益冲突？3）如何在车辆迁移过程中确保无缝的服务迁移？为了解决上述问题，本文研究了无缝服务迁移的路径选择。我们提出了一种路径选择算法，以共同优化网络平面（即网络提供商的成本）和服务平面（即用户的QoE）的利益。我们首先将其表述为一个多目标优化问题，然后在理论上进一步证明该算法可以给出一个 用户的QoE）。我们首先将其表述为一个多目标优化问题，然后在理论上进一步证明该算法可以给出一个 用户的QoE）。我们首先将其表述为一个多目标优化问题，然后在理论上进一步证明该算法可以给出一个弱帕累托最优解 。此外，为了提高所提出算法的可扩展性，设计了一种基于距离的滤波策略，以预先消除不希望的开关。我们对两个综合数据集进行了实验，结果验证了该算法的有效性。