论文标题
通过边缘采样的有效传输和重建相关数据流
Efficient Transmission and Reconstruction of Dependent Data Streams via Edge Sampling
论文作者
论文摘要
由于智能设备的普遍性和对实时分析的需求，数据流处理是一个越来越重要的主题。地理分布的流媒体系统，基于云的查询利用来自多个分布式设备的数据流，由于广阔区域网络（WAN）带宽通常很少或昂贵，因此面临挑战。边缘计算使我们能够利用靠近设备的资源来解决这些带宽成本，例如要对传入的数据流进行采样，该数据流可以交易下游查询准确性，以降低总体传输成本。在本文中，我们利用了一个事实，即数据流之间的相关性可能在同一地理区域的设备之间存在。使用此见解，我们开发了一个混合边缘云系统，该系统在边缘的采样与云中缺少值的估计以减少WAN上的流量之间进行系统交易。我们提出了一个优化框架，该框架计算边缘处的样本大小，并系统地界定我们可以在云中估算的样本数量，并且考虑到流之间的相关性的强度。我们使用三个现实世界数据集的评估表明，与现有的采样技术相比，我们的系统可以在多个聚合查询中提供可比的错误率，同时将WAN流量降低27-42％。
Data stream processing is an increasingly important topic due to the prevalence of smart devices and the demand for real-time analytics. Geo-distributed streaming systems, where cloud-based queries utilize data streams from multiple distributed devices, face challenges since wide-area network (WAN) bandwidth is often scarce or expensive. Edge computing allows us to address these bandwidth costs by utilizing resources close to the devices, e.g. to perform sampling over the incoming data streams, which trades downstream query accuracy to reduce the overall transmission cost. In this paper, we leverage the fact that correlations between data streams may exist across devices located in the same geographical region. Using this insight, we develop a hybrid edge-cloud system which systematically trades off between sampling at the edge and estimation of missing values in the cloud to reduce traffic over the WAN. We present an optimization framework which computes sample sizes at the edge and systematically bounds the number of samples we can estimate in the cloud given the strength of the correlation between streams. Our evaluation with three real-world datasets shows that compared to existing sampling techniques, our system could provide comparable error rates over multiple aggregate queries while reducing WAN traffic by 27-42%.
