Abstract
In this study, we address the problem of selecting the optimal end-to-end paths for link loss inference in order to improve the performance of network tomography applications, which infer the link loss rates from the path loss rates. Measuring the path loss rates using end-to-end probing packets may incur additional traffic overheads for networks, so it is important to select the minimum path set carefully while maximizing their performance. The usual approach is to select the maximum independent paths from the candidates simultaneously, while the other paths can be replaced by linear combinations of them. However, this approach ignores the fact that many paths always exist that do not lose any packets, and thus it is easy to determine that all of the links of these paths also have 0 loss rates. Not considering these good paths will inevitably lead to inefficiency and high probing costs. Thus, we propose an adaptive path selection method that selects paths sequentially based on the loss rates of previously selected paths. We also propose a theorem as well as a graph construction and decomposition approach to efficiently find the most valuable path during each round of selection. Our new method significantly outperforms the classical path selection method based on simulations in terms of the probing cost, number of accurate links determined, and the running speed.
Highlights
The robustness of communication networks is extremely important for both users and network service providers
In the “Observations” section, we consider some characteristics of the paths and links in network tomography applications, and we propose some fundamental concepts motivated by the observed characteristics in the “Fundamentals of Path Selection” section
[12][13] and [14] do not select paths before they perform tomography, while our algorithm focuses on the path selection stage; [15][16] and [17] select monitoring paths to detect or locate the failures, but our algorithm aims to determine the loss rates of links; [9] selects paths before the tomography stage under the condition that there are link failures in the network, which is outside the scope of this paper
Summary
The robustness of communication networks is extremely important for both users and network service providers. As the network grows in terms of size and diversity, it becomes increasingly difficult to monitor the characteristics of the network interior, such as the link loss rates and packet latency. The main problems are as follows [1]: i) general organizations have administrative access to only a small fraction of the network’s internal nodes, whereas commercial factors often prevent organizations from sharing internal performance data; and ii) the servers and routers in the network are usually operated by businesses, which may be unwilling or unable to cooperate with the collection of network traffic measurements for network. Adaptive Path Selection for Link Loss Inference http://www.nsfc.gov.cn/. YR contributes to the design of the experiments and the data collection. JJ receives The National Natural Science Foundations of China (No 31671589), http://www.
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