AdaptiveFog: A Modelling and Optimization Framework for Fog Computing in Intelligent Transportation Systems
AdaptiveFog: A Modelling and Optimization Framework for Fog Computing in Intelligent Transportation Systems
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DOI：
10.1109/tmc.2021.3080397
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发表时间：
2021-06
影响因子：
7.9
通讯作者：
Yong Xiao;M. Krunz
中科院分区：
文献类型：
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作者：
Yong Xiao;M. Krunz
雾计算一直被提倡为智能互联汽车中计算密集型服务的使能技术。大多数现有的工作都侧重于分析与雾计算相关的排队和工作负载处理延迟，而忽略了无线访问延迟有时会主导整体延迟的事实。这激发了本文的工作，我们报告了一项为期五个月的测量研究，研究了联网车辆与商用LTE网络支持的雾/云计算系统之间的无线接入延迟。我们提出AdaptiveFog，这是一个实现雾/云基础设施的不同LTE网络之间自主和动态切换的新框架。AdaptiveFog的主要目标是最大化服务置信度，定义为给定服务类型的延迟低于某个阈值的概率。为了量化不同LTE网络之间的性能差距，我们引入了一种新的统计距离度量，称为加权Kantorovich-Rubinstein （K-R）距离。研究了基于短期置信度和长期置信度的有限和无限水平优化两种情景。针对每种场景，提出了一种基于加权K-R距离的简单阈值策略，并证明了该策略可以最大化智能车辆的延迟置信度。基于我们的延迟测量进行了广泛的分析和模拟。我们的结果表明，AdaptiveFog分别在雾和云延迟的置信度上实现了大约30%到50%的改进。
Fog computing has been advocated as an enabling technology for computationally intensive services in smart connected vehicles. Most existing works focus on analyzing the queueing and workload processing latencies associated with fog computing, ignoring the fact that wireless access latency can sometimes dominate the overall latency. This motivates the work in this paper, where we report on a five-month measurement study of the wireless access latency between connected vehicles and a fog/cloud computing system supported by commercially available LTE networks. We propose AdaptiveFog, a novel framework for autonomous and dynamic switching between different LTE networks that implement a fog/cloud infrastructure. AdaptiveFog's main objective is to maximize the service confidence level, defined as the probability that the latency of a given service type is below some threshold. To quantify the performance gap between different LTE networks, we introduce a novel statistical distance metric, called weighted Kantorovich-Rubinstein (K-R) distance. Two scenarios based on finite- and infinite-horizon optimization of short-term and long-term confidence are investigated. For each scenario, a simple threshold policy based on weighted K-R distance is proposed and proved to maximize the latency confidence for smart vehicles. Extensive analysis and simulations are performed based on our latency measurements. Our results show that AdaptiveFog achieves around 30 to 50 percent improvement in the confidence levels of fog and cloud latencies, respectively.