Abstract
In the carrier network, the network noise is large, which leads to the low performance of the link packet loss rate inference algorithm. To solve this problem, this paper proposes a link packet loss rate reasoning algorithm based on network characteristics in carrier networks. The algorithm includes five steps: building a routing matrix, simplifying the routing matrix, calculating link characteristics according to network characteristics, building a column full-rank matrix, and solving equations to obtain the value of the packet loss rate of each link. In order to construct a full-rank matrix, the pass rate of each link is evaluated from the two dimensions of the pass rate estimation and the importance estimation of each link based on the network characteristics. In the experimental part, it is verified that compared with the existing algorithms, the algorithm in this paper improves the accuracy of the detection rate and pass rate of congested links, and reduces the misjudgment rate of congested links.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Vardi, Y.: Network tomography: estimating source-destination traffic intensities from link data. J. Am. Stat. Assoc. 91(433), 365–377 (1996)
Fan, X., Li, X.: Minimizing probing CostWith mRMR feature selection in network monitoring. IEEE Commun. Lett. 21(11), 2400–2403 (2017)
Xie, K., Li, X., Wang, X., et al.: Fast tensor factorization for accurate internet anomaly detection. IEEE/ACM Trans. Networking 25(6), 3794–3807 (2017)
Chen, Y., Bindel, D., Song, H.: Network tomography: identifiability and fourier domain estimation. In: Proceedings of the IEEE International Conference on Computer Communications, pp. 1875–1883. Barcelona, Spain, May (2007)
Li, Y., Miao, R., Kim, C., et al.: Lossradar: fast detection of lost packets in data center networks. In: Proceedings of the 12th International on Conference on Emerging Networking Experiments and Technologies, pp. 481–495. ACM, New York, NY, USA (2016)
Qiao, Y., Jiao, J., Cui, X., Rao, Y.: Robust loss inference in the presence of noisy measurements and hidden fault diagnosis. IEEE/ACM Trans. Networking 28(1), 43–56 (2020)
Qiao, Y., Qiu, X., Meng, L., Gu, R.: Efficient loss inference algorithm using unicast end-to-end measurements. J. Netw. Syst. Manage. 21(2), 169–193 (2013)
Padmanabhan, V.N., Qiu, L., Wang, H.J.: Server-based inference of Internet performance. In: Proceedings of the IEEE INFOCOM, vol. 1, pp. 145–155. San Diego, CA, USA (2003)
Acknowledgments
This work was supported by the science and technology project of Guangdong Power Grid (036000KK52190008(GDKJXM20198131)).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shi, Z., Zeng, Y., Liang, Y., Gong, W. (2022). Link Packet Loss Rate Inference Algorithm Based on Network Characteristics in Carrier Network. In: Liu, Q., Liu, X., Chen, B., Zhang, Y., Peng, J. (eds) Proceedings of the 11th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-16-6554-7_154
Download citation
DOI: https://doi.org/10.1007/978-981-16-6554-7_154
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6553-0
Online ISBN: 978-981-16-6554-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)