错误网络的被动网络断层扫描：网络编码方法
Passive network tomography for erroneous networks: A network coding approach
摘要：
被动网络断层扫描使用网络通信的端到端观察来表征网络，例如估计网络拓扑并定位随机或对抗性故障。在线性网络编码的设置下，这项工作提供了一个全面的研究被动网络断层扫描在网络（随机或对抗）毛刺的存在。具体而言，本研究沿着两个方向展开：1.层析成像的上限和下限（即，对于随机线性网络编码（RLNC），给出了在每个问题设置中可能进行网络断层扫描的最不利条件，以及实现该性能的相应方案（如果可能，计算效率高）。我们认为RLNC设计有共同的随机性，即，接收器知道所有节点的随机码本。(To证明这一点，我们显示了一个上限的问题，在网络中的拓扑估计使用RLNC没有共同的随机性。在这种情况下，我们提出了第一套算法，准确地描述了网络拓扑结构。该算法具有时间复杂度，其时间复杂度是网络参数的多项式。针对给定拓扑信息的网络错误定位问题，提出了第一个计算上易于处理的随机错误定位算法，并证明了对抗性错误定位是计算上难以处理的. 2.新的网络编码方案的设计，提高RLNC的层析成像性能，同时保持理想的低复杂度，吞吐量最佳，分布式线性网络编码性能的RLNC。特别是，我们设计的网络代码的基础上里德-所罗门码，使最大数量的对抗性错误可以本地化的计算效率的方式，即使没有网络拓扑结构的信息。
英文摘要：
Passive network tomography uses end-to-end observations of network communication to characterize the network, for instance to estimate the network topology and to localize random or adversarial glitches. Under the setting of linear network coding this work provides a comprehensive study of passive network tomography in the presence of network (random or adversarial) glitches. To be concrete, this work is developed along two directions: 1. Tomographic upper and lower bounds (i.e., the most adverse conditions in each problem setting under which network tomography is possible, and corresponding schemes (computationally efficient, if possible) that achieve this performance) are presented for random linear network coding (RLNC). We consider RLNC designed with common randomness, i.e., the receiver knows the random code-books all nodes. (To justify this, we show an upper bound for the problem of topology estimation in networks using RLNC without common randomness.) In this setting we present the first set of algorithms that characterize the network topology exactly. Our algorithm for topology estimation with random network errors has time complexity that is polynomial in network parameters. For the problem of network error localization given the topology information, we present the first computationally tractable algorithm to localize random errors, and prove it is computationally intractable to localize adversarial errors. 2. New network coding schemes are designed that improve the tomographic performance of RLNC while maintaining the desirable low-complexity, throughput-optimal, distributed linear network coding properties of RLNC. In particular, we design network codes based on Reed-Solomon codes so that a maximal number of adversarial errors can be localized in a computationally efficient manner even without the information of network topology.
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