Randomized Error Removal for Online Spread Estimation in High-Speed Networks
Randomized Error Removal for Online Spread Estimation in High-Speed Networks
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DOI：
10.1109/tnet.2022.3197968
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Haibo Wang;Chaoyi Ma;Olufemi O. Odegbile;Shigang Chen;J. Peir
中科院分区：
文献类型：
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作者：
Haibo Wang;Chaoyi Ma;Olufemi O. Odegbile;Shigang Chen;J. Peir
流扩散测量提供了基本的统计数据，可以帮助网络运营商更好地了解流量特征和流量模式，并在流量工程，网络安全和服务质量中应用。在过去的几十年里，单流扩散估计的性能得到了巨大的改善。然而，当处理数据包流中的大量数据流时，在减少内存占用的同时准确测量每个流的扩展仍然是一个重大挑战。本文的目标是引入新的多流传播估计设计，产生更小的处理开销和查询开销比最先进的，但实现显着的精度提高传播估计。我们正式分析这些新设计的性能。我们在硬件和软件中实现它们，并使用真实世界的数据跟踪，以评估其性能与最先进的比较。实验结果表明，我们最好的草图显着提高了现有的最佳工作的估计精度，数据包处理吞吐量和在线查询吞吐量。
Flow spread measurement provides fundamental statistics that can help network operators better understand flow characteristics and traffic patterns with applications in traffic engineering, cybersecurity and quality of service. Past decades have witnessed tremendous performance improvement for single-flow spread estimation. However, when dealing with numerous flows in a packet stream, it remains a significant challenge to measure per-flow spread accurately while reducing memory footprint. The goal of this paper is to introduce new multi-flow spread estimation designs that incur much smaller processing overhead and query overhead than the state of the art, yet achieves significant accuracy improvement in spread estimation. We formally analyze the performance of these new designs. We implement them in both hardware and software, and use real-world data traces to evaluate their performance in comparison with the state of the art. The experimental results show that our best sketch significantly improves over the best existing work in terms of estimation accuracy, packet processing throughput, and online query throughput.