基于指数平滑可用性预测的自适应分组采样方法

Jingfu Wang, Ruiying Li, Wuyue Ren
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引用次数: 1

摘要

为了提高网络可用性测试的采样精度,减少网络可用性测试的资源消耗,提出了一种新的自适应采样方法。它根据网络状况随时间调整采样概率。其基本抽样方法是简单随机抽样。基于指数平滑法对网络可用性进行预测。将可用性的实测值与预测值进行比较,就可以判断现在使用的采样参数是否适合网络状况。提出了一个线性规则来决定如何随时间调整采样概率。以战术网络为例,与简单随机抽样的效率进行了比较。结果表明,在样本大小相同的情况下,该抽样方法的准确率高于单纯随机抽样,而在发生流量突发时,效果更为明显。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive packet sampling method based on the prediction of availability using exponential smoothing
In order to improve the accuracy of sampling and reduce resource consumption in network availability testing, a new kind of adaptive sampling method is proposed in this paper. It adjusts sampling probability over time according to network condition. Its basic sampling method is simple random sampling. The network availability is predicted based on exponential smoothing method. Comparing the measured value of availability with the predicted one, we can decide whether the sampling parameter now used is suitable for the network condition. A liner rule is advanced to decide how to adjust the sampling probability over time. Taking the tactical Internet as an example, efficiency of this sampling method is compared with that of simply random sampling. The result shows that the accuracy of the sampling method is higher than simply random sampling when the size of sample is equal, and the result would be more obvious when traffic burst occurs.
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