Adaptive random sampling for load change detection
Adaptive random sampling for load change detection
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DOI：
10.1145/511334.511376
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发表时间：
2002-06
期刊：
影响因子：
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通讯作者：
Baek-Young Choi;Jaesung Park;Zhi-Li Zhang
中科院分区：
文献类型：
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作者：
Baek-Young Choi;Jaesung Park;Zhi-Li Zhang
及时检测流量负载的变化对于启动适当的流量工程机制至关重要。流量的准确测量是必不可少的，因为变化检测的功效取决于流量估计的准确性。然而，精确的流量测量涉及检查通过链路的每个数据包，导致显著的开销，特别是在高速链路上。提出了用于流量负载估计的采样技术作为限制测量开销的一种方式。在本文中，我们解决的问题，在一个预先指定的公差范围内的抽样误差，并提出了一种自适应随机抽样技术，确定最小抽样概率自适应根据交通动态。使用真实的网络流量的痕迹，我们表明，所提出的自适应随机采样技术确实产生所需的精度，同时也产生显着减少的流量样本的量。我们还研究了采样误差对负载变化检测性能的影响。
Timely detection of changes in traffic load is critical for initiating appropriate traffic engineering mechanisms. Accurate measurement of traffic is essential since the efficacy of change detection depends on the accuracy of traffic estimation. However, precise traffic measurement involves inspecting every packet traversing a link, resulting in significant overhead, particularly on high speed links. Sampling techniques for traffic load estimation are proposed as a way to limit the measurement overhead. In this paper, we address the problem of bounding sampling error within a pre-specified tolerance level and propose an adaptive random sampling technique that determines the minimum sampling probability adaptively according to traffic dynamics. Using real network traffic traces, we show that the proposed adaptive random sampling technique indeed produces the desired accuracy, while also yielding significant reduction in the amount of traffic samples. We also investigate the impact of sampling errors on the performance of load change detection.