Many vehicles spend a significant amount of time in urban traffic congestion. Due to the evolution of autonomous vehicles, driver assistance systems, and in-vehicle entertainment, these vehicles have plentiful computational and communication capacity. How can we deploy data collection and processing tasks on these (slowly) moving vehicles to productively use any spare resources? To answer this question, we study the efficient placement of distributed services on a moving vehicle cluster. We present a macroscopic flow model for an intersection in Dublin, Ireland, using real vehicle density data. We show that such aggregate flows are highly predictable (even though the paths of individual vehicles are not known in advance), making it viable to deploy services harnessing vehicles’ sensing capabilities. After studying the feasibility of using these vehicle clusters as infrastructure, we introduce a detailed mathematical specification for a task-based, distributed service placement model. The distributed service scales according to the resource requirements and is robust to the changes caused by the mobility of the cluster. We formulate this as a constrained optimization problem, with the objective of minimizing overall processing and communication costs. Our results show that jointly scaling tasks and finding a mobility-aware, optimal placement results in reduced processing and communication costs compared to the two schemes in the literature. We compare our approach to an autonomous vehicular edge computing-based naive solution and a clustering-based solution.

中文翻译：

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在城市环境中的车辆集群上扩展和放置分布式服务


许多车辆在城市交通拥堵中花费大量时间。由于自动驾驶汽车、驾驶员辅助系统和车载娱乐系统的发展，这些车辆具有丰富的计算和通信能力。我们如何在这些（缓慢）移动的车辆上部署数据收集和处理任务，以有效地利用任何闲置资源？为了回答这个问题，我们研究了移动车辆集群上分布式服务的有效放置。我们使用真实的车辆密度数据，提出了爱尔兰都柏林十字路口的宏观流量模型。我们表明，这种总流量是高度可预测的（即使事先不知道各个车辆的路径），使得部署利用车辆传感功能的服务变得可行。在研究了使用这些车辆集群作为基础设施的可行性之后，我们为基于任务的分布式服务放置模型引入了详细的数学规范。分布式服务根据资源需求进行扩展，并且对于集群移动性引起的变化具有鲁棒性。我们将其表述为一个约束优化问题，其目标是最小化总体处理和通信成本。我们的结果表明，与文献中的两种方案相比，联合扩展任务并找到移动感知的最佳放置可以降低处理和通信成本。我们将我们的方法与基于自主车辆边缘计算的朴素解决方案和基于集群的解决方案进行比较。