Vehicular fog computing (VFC) is envisioned as an extension of cloud and mobile edge computing to utilize the rich sensing and processing resources available in vehicles. We focus on slow-moving cars that spend a significant time in urban traffic congestion as a potential pool of on-board sensors, video cameras, and processing capacity. For leveraging the dynamic network and processing resources, we utilize a stochastic mobility model to select nodes with similar mobility patterns. We then design two distributed applications that are scaled in real-time and placed as multiple instances on selected vehicular fog nodes. We handle the unstable vehicular environment by a), Using real vehicle density data to build a realistic mobility model that helps in selecting nodes for service deployment b), Using community-detection algorithms for selecting a robust vehicular cluster using the predicted mobility behavior of vehicles. The stability of the chosen cluster is validated using a graph centrality measure, and c), Graph-based placement heuristics are developed to find the optimal placement of service graphs based on a multi-objective constrained optimization problem with the objective of efficient resource utilization. The heuristic solves an important problem of processing data generated from distributed devices by balancing the trade-off between increasing the number of service instances to have enough redundancy of processing instances to increase resilience in the service in case of node or link failure, versus reducing their number to minimise resource usage. We compare our heuristic to an integer linear program solution and a first-fit heuristic. Our approach performs better than these comparable schemes in terms of resource utilization and/or has a lesser service latency, which is a crucial requirement for safety-related applications.
翻译:视觉迷雾计算(VFC)是云层和移动边缘计算的一个延伸,目的是利用车辆现有的丰富感知和处理资源。我们侧重于在城市交通拥堵中花费大量时间的慢动汽车,作为机载传感器、摄像头和处理能力的潜在集合。为利用动态网络和处理资源,我们使用随机流动模型选择具有类似流动性模式的节点。然后我们设计两个分布式应用程序,这些应用程序以实时缩放方式为基础,并被放在选定的车辆雾节点上。我们用一个处理不稳定的车辆环境,使用真实的车辆密度数据来构建一个现实的移动模式,帮助选择服务部署的节点 b),作为机载传感器、摄像摄像机和处理能力。我们所选集的集束的稳定性通过图表中心度测量来验证。基于多角度限制优化资源的利用目标,我们使用不稳定的车辆密度数据图的最佳放置方式。 使用现实性车辆密度数据流流流流的计算方法,比我们所分配的内径直线性数据流应用方法要更难解决一个重要的问题,在交易处理系统上,而处理数据流流流流流流流数据处理系统之间则无法平衡。