Latency Prediction for Delay-sensitive V2X Applications in Mobile Cloud/Edge Computing Systems
Latency Prediction for Delay-sensitive V2X Applications in Mobile Cloud/Edge Computing Systems
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DOI：
10.1109/globecom42002.2020.9348104
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Wenhan Zhang;Mingjie Feng;M. Krunz;H. Volos
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文献类型：
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作者：
Wenhan Zhang;Mingjie Feng;M. Krunz;H. Volos
移动的边缘计算（MEC）是延迟敏感型车联网（V2X）应用的关键推动因素。确定在哪里执行任务需要准确估计卸载延迟。在本文中，我们提出了一个集成机器学习和统计方法的延迟预测框架。在驾驶过程中收集的大量延迟测量的帮助下，我们首先对数据进行预处理，并将其分为两个部分：一个部分随着时间的推移遵循可跟踪的趋势，另一个部分表现得像随机噪声。然后，我们开发了一个长短期记忆（LSTM）网络来预测第一个组件。这个LSTM网络捕捉了延迟随时间的变化趋势。我们进一步提高了这种技术的预测精度，通过采用k-中心点分类方法。对于第二个组成部分，我们提出了一种统计方法，使用Epanechnikov内核和移动平均函数的组合。实验结果表明，所提出的预测方法将预测误差降低到原始数据的标准差（STD）的一半。
Mobile edge computing (MEC) is a key enabler of delay-sensitive vehicle-to-everything (V2X) applications. Determining where to execute a task necessitates accurate estimation of the offloading latency. In this paper, we propose a latency prediction framework that integrates machine learning and statistical approaches. Aided by extensive latency measurements collected during driving, we first preprocess the data and divide it into two components: one that follows a trackable trend over time and the other that behaves like random noise. We then develop a Long Short-Term Memory (LSTM) network to predict the first component. This LSTM network captures the trend in latency over time. We further enhance the prediction accuracy of this technique by employing a k-medoids classification method. For the second component, we propose a statistical approach using a combination of Epanechnikov Kernel and moving average functions. Experimental results show that the proposed prediction approach reduces the prediction error to half of a standard deviation (STD) of the raw data.