基于网络层析成像的光网络流量矩阵估计方法(英文) 被引量：2

An Estimation Approach to Traffic Matrix in Optical Networks Based on Network Tomography

下载PDF

导出

摘要提出一种面向光传输网络的流量矩阵估计方法.采用压缩感知理论研究光传输网络中的流量矩阵估计,根据信号稀疏表示将流量矩阵稀疏化,基于矩阵变换理论提出新的面向光传输网络的网络层析成像模型.该模型克服了已有网络层析成像模型的病态特性,并通过凸优化来获得流量矩阵的估计等式.提出了具体的估计算法,获得关于光传输网络流量矩阵的精确估计.真实网络的数据仿真表明所提出的方法是有效和可行的. A traffic matrix estimation approach for optical transportation networks was proposed. Compressive sensing theory was used to study traffic matrix estimation in optical transportation networks. According to the sparse representation of signals, traffic matrix was processed in the sparse way. Matrix transform theory was exploited to present a new network tomography model for optical transportation networks. This model can overcome the ill-posed nature of the existing network tomography. Conex optimization was used to attain the estimation equation about traffic matrix. The detailed estimation algorithm is presented. The accurate estimation about traffic matrix for optical transportation networks was obtained. The data from the real network was used to perform the simulation. Simulation results show that the proposed method is effective and feasible.

作者蒋定德秦文达唐庆怡聂来森张健

机构地区东北大学信息科学与工程学院

出处《光子学报》 EI CAS CSCD 北大核心 2014年第7期101-106,共6页 Acta Photonica Sinica

基金 The National Natural Science Foundation of China(Nos.61071124,61172051) the Specialized Research Fund for the Doctoral Program of Higher Education(No.20100042120035) the Program for New Century Excellent Talents in University(No.NCET-11-0075) the Fundamental Research Funds for the Central Universities(Nos.N120804004,N110404001)

关键词流量矩阵光网络凸优化网络层析成像压缩感知 Traffic matrix Optical network Convex optimization Network tomography Compressivesensing

分类号 TP393 [自动化与计算机技术—计算机应用技术]

引文网络
相关文献

参考文献3

1陈存康,乔耀军,纪越峰.基于正交频分复用无源光网络的动态带宽分配算法研究(英文)[J].光子学报,2011,40(5):684-689. 被引量：10
2管爱红,王波云,傅洪亮,徐寅,张海芳.OBS网络中基于优先级与突发包分割的偏射路由机制(英文)[J].光子学报,2012,41(2):127-132. 被引量：7
3JIANG Dingde,XU Zhengzheng,NIU Laisen,LIU Jindi.An Approximate Approach to End-to-End Traffic in Communication Networks[J].Chinese Journal of Electronics,2012,21(4):705-710. 被引量：4

二级参考文献28

1A. Soule, F. Silveira, H. Ringberg, C. Diot, "Challenging the supremacy of traffic matrices in anomaly detection", in Proc. of ACM IMC, 2007.
2P:. Soule, A. Iakhina, N. Taft et al., "Traffic matrices: Balanc- ing measurements, inference and modeling", in Proc. of ACM SIGMETRICS, 2005.
3A. Lakhina, K. Papagiannaki, M. Crovella, C. Diot, E. Kola- cyzk, N. Taft, "Structural analysis of network traffic flows", in Pvoc. of ACM SIGMETRICS, 20(}4.
4L. Guo, "LSSP: A novel local segment shared protection for multi-domain optical mesh networks", Computer Communica- tions, Vol.30, pp.1794-1801, June 2007.
5K. Papagiannaki, N. Taft, A. Lakhina, alstributed approach to measure traffic matrix", in Proc. of A CM IMC, 2004. (5-{-1).
6D. Jiang, Z. Xu, Z. Chen et al., "Joint time-frequency sparse estimation of large-scale network traffic", Conputer Networks Vol.55, No.10t pp.3533-3547, 2011.
7L. Guo, J. Cao, H. Yu et al., "Path-based routing provisioning with mixed shared protection in WDM mesh networks", Journal of Lightwave Technology, Vol.24, pp.1129-1141, Mar. 2006.
8Y. Zhang, M. Roughan, C. Lund, D. Donoho, "An information theoretic approach to traffic matrix estimation", in Proc. of ACM SIGCOMM, 2003.
9Y. Zhang, M. Roughan, N. Duffield, A. Greenberg, "Fast ac- curate computation of large-scale IP traffic matrices from link loads", A CM SIGMETRICS Performance Evaluation Review, Vol.31, No.3, pp.206-217, 2003.
10J. Ni, S. Tatikonda, E.M. Yeh, "A large-scale distributed traf- fic matrix estimation algorithm", in Proc. of IEEE Globecom, 2006.