网络测量中自适应数据采集方法

抽样方法广泛地应用于网络测量与其他领域对被测总体的指标进行估计.研究表明,多种网络指标呈现重尾分布或自相似的特征.这些特性为准确估计总体指标带来了诸多困难.但同时,对被测网络指标进行建模也有着重要的应用.然而,建立精确网络模型是困难的.从时间序列拟合角度出发,提出了一种基于拟合的自适应抽样方法,对被测指标进行基于测量的建模.工作主要体现在: (1) 采用分段线性函数对被测指标进行逼近,建立基于测量的模型; (2) 与常用的抽样方法相比,在相同的样本数情况下,由拟合模型对指标进行的估计更准确、更稳定;通过对两个测量记录的分析表明,在与常用抽样方法保持相同的拟合误差时,自适应抽样方法明显地减少了所需采集的样本数量; (3) 与其他概率抽样方法相比,自适应抽样最终抽取的样本数更稳定、更可靠,并给出了最终样本数的概率分布.

Adaptive Sampling Methodology in Network Measurements

Sampling methodologies are widely used in network measurements and other related fields. Most applications mainly focus on parent population statistical metrics estimation of interest. Recent researches reveal that many aspects of network characters present heavy-tailed distribution or self-similarity. These properties might cause a heavy passive effect on the estimation accuracy. In other circumstances, there exist demands on modeling the characteristics of a network in network operation. To develop an accurate model for network character is much difficult. From a broader view, these applications are treated as special cases of fitting problems of planar data set or time series in applied mathematics. In the paper, a Fitting-based adaptive sampling methodology (FASM) is developed for reconstructing the evolution of some network characteristics (model). The contributions of the paper include: (1) Adopting a Piecewise Linear Function Approximation scheme to provide a more accurate approximation of the true character. (2) The statistical metric derived from the FASM provides a much more stable and accurate estimation than other popular methodologies under the same sampling size. Experiments based on two measurement traces show that the FASM can dramatically reduce the number of samples while retaining the same approximating residual error as others. (3) The variance of sampling size is more stable than those of other probability sampling schemes.