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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 1863929, 11 pages
Research Article

Adaptive Loss Inference Using Unicast End-to-End Measurements

1School of Information and Computer, Anhui Agriculture University, Hefei 230061, China
2State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received 8 July 2016; Revised 9 November 2016; Accepted 28 November 2016

Academic Editor: Mohammad D. Aliyu

Copyright © 2016 Yan Qiao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We address the problem of inferring link loss rates from unicast end-to-end measurements on the basis of network tomography. Because measurement probes will incur additional traffic overheads, most tomography-based approaches perform the inference by collecting the measurements only on selected paths to reduce the overhead. However, all previous approaches select paths offline, which will inevitably miss many potential identifiable links, whose loss rates should be unbiasedly determined. Furthermore, if element failures exist, an appreciable number of the selected paths may become unavailable. In this paper, we creatively propose an adaptive loss inference approach in which the paths are selected sequentially depending on the previous measurement results. In each round, we compute the loss rates of links that can be unbiasedly determined based on the current measurement results and remove them from the system. Meanwhile, we locate the most possible failures based on the current measurement outcomes to avoid selecting unavailable paths in subsequent rounds. In this way, all identifiable and potential identifiable links can be determined unbiasedly using only 20% of all available end-to-end measurements. Compared with a previous classical approach through extensive simulations, the results strongly confirm the promising performance of our proposed approach.