Nowadays vehicle fleets are launched to perform business or scientific tasks, with new features supported by the emerging multi-access edge computing (MEC) platform. In the presence of high vehicle mobility, however, it is challenging to precisely provision resources among distributed edge clouds so that i) the QoS of vehicular service is guaranteed and meanwhile ii) the provisioning cost is minimized. We systematically investigate the QoS guaranteed optimal resource provisioning problem for the connected vehicle fleet in the MEC environment. Based on stochastic traffic analysis, we propose an optimization framework to minimize the cost of resource provisioning, while the service blocking probability is guaranteed to be smaller than a predefined threshold. We then present a lightweight two-phase algorithm based on bracketing and binary searching to solve the problem efficiently. To evaluate our method, we use two large real-world datasets collected by an online taxi service platform and validate the QoS with our resource provisioning strategy. The results demonstrate that our method can save the total provision cost up to $\text{40}{\%}$, compared with the naïve resource provisioning strategy, and meanwhile can provide reliable QoS guarantee, compared with the mobility estimation-based approach.

中文翻译：

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为车队提供 QoS 保证的边缘云资源供应

如今，车队开始执行商业或科学任务，新功能得到新兴的多接入边缘计算 (MEC) 平台的支持。然而，在车辆高机动性的情况下，在分布式边缘云之间精确配置资源以确保 i) 车辆服务的 QoS，同时 ii) 最小化配置成本是一项挑战。我们系统地研究了 MEC 环境中联网车队的 QoS 保证最优资源供应问题。基于随机流量分析，我们提出了一个优化框架，以最小化资源提供的成本，同时保证服务阻塞概率小于预定义的阈值。然后，我们提出了一种基于括号法和二进制搜索的轻量级两阶段算法来有效地解决该问题。为了评估我们的方法，我们使用在线出租车服务平台收集的两个大型真实世界数据集，并使用我们的资源配置策略验证 QoS。结果表明，与原始资源配置策略相比，我们的方法可以节省高达 $\text{40}{\%}$ 的总配置成本，同时与基于移动性估计的方法相比，可以提供可靠的 QoS 保证.