基于一维投影的网络层析成像
Network tomography based on 1-D projections
摘要：
网络断层扫描被认为是大规模和分散的互联网的性能评估和诊断的最有前途的方法之一。基于观测数据一维线性投影序列的边缘分布，提出了一种新的估计方法，用于求解网络层析成像中的一类反问题。我们给出了一个一般的识别结果所提出的方法和研究的设计问题，这些一维投影的统计效率。我们表明，对于一个简单的高斯层析成像模型，有一个最佳的一维投影，从这些投影得到的估计是渐近有效的极大似然估计的基础上的观测数据的联合分布。对于实际应用，我们针对两个网络层析成像实例对所提出的方法进行了仿真研究。第一种是使用高斯源-目的地流量模型，其均值和方差之间具有幂关系的流量需求断层扫描，第二种是用于网络延迟断层扫描，其中链路延迟是从端到端路径延迟估计的。我们比较从我们的方法和从使用联合分布和其他低维投影得到的估计，并表明，在这两种情况下，所提出的方法产生令人满意的结果。
英文摘要：
Network tomography has been regarded as one of the most promising methodologies for performance evaluation and diagnosis of the massive and decentralized Internet. This paper proposes a new estimation approach for solving a class of inverse problems in network tomography, based on marginal distributions of a sequence of one-dimensional linear projections of the observed data. We give a general identifiability result for the proposed method and study the design issue of these one dimensional projections in terms of statistical efficiency. We show that for a simple Gaussian tomography model, there is an optimal set of one-dimensional projections such that the estimator obtained from these projections is asymptotically as efficient as the maximum likelihood estimator based on the joint distribution of the observed data. For practical applications, we carry out simulation studies of the proposed method for two instances of network tomography. The first is for traffic demand tomography using a Gaussian Origin-Destination traffic model with a power relation between its mean and variance, and the second is for network delay tomography where the link delays are to be estimated from the end-to-end path delays. We compare estimators obtained from our method and that obtained from using the joint distribution and other lower dimensional projections, and show that in both cases, the proposed method yields satisfactory results.
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