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Retroactive Packet Sampling for Traffic Receipts

机译:流量收据的追溯数据包采样

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摘要

Is it possible to design a packet-sampling algorithm that prevents the network node that performs the sampling from treating the sampled packets preferentially? We study this problem in the context of designing a "network-transparency" system. In this system, networks emit receipts for a small sample of the packets they observe, and a monitor collects these receipts to estimate each network's loss and delay performance. Sampling is a good building block for this system, because it enables a solution that is flexible and combines low resource cost with quantifiable accuracy. The challenge is cheating resistance: when a network's performance is assessed based on the conditions experienced by a small traffic sample, the network has a strong incentive to treat the sampled packets better than the rest. We contribute a sampling algorithm that is provably robust to such prioritization attacks, enables network performance estimation with quantifiable accuracy, and requires minimal resources. We confirm our analysis using real traffic traces.
机译:是否可以设计一种数据包采样算法,以防止执行采样的网络节点优先处理采样的数据包?我们在设计“网络透明”系统的背景下研究此问题。在此系统中,网络会针对他们观察到的数据包的一小部分发出收据,然后监视器会收集这些收据以估计每个网络的丢失和延迟性能。采样是该系统的一个很好的构建块,因为它可以实现灵活的解决方案,并结合了低资源成本和可量化的精度。挑战在于防欺诈性:当根据少量流量样本所经历的条件评估网络的性能时,网络有强烈的动机要比其他样本更好地处理采样的数据包。我们提供了一种采样算法,该算法可证明对此类优先级攻击具有鲁棒性,能够以可量化的准确性估算网络性能,并且需要最少的资源。我们使用实际流量跟踪来确认我们的分析。

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