A convex optimization-based traffic matrix estimation approach in IP-over-WDM backbone networks

With the rapid development of an IP-over-WDM network, it has become a complex and heterogeneous network. In such a case, network management is a crucial role for promoting the efficiency of the IP-over-WDM network. A traffic matrix, which provides an intrinsic and flow-level view of our networks, is required to perform network management tasks such as traffic engineering function. Unfortunately, limited by current technologies, it is significantly difficult to acquire the exact traffic matrix. Motivated by this issue, in this paper, we investigate the traffic matrix estimation problem in an IP-over-WDM backbone network. We develop a simple algorithm based on the network tomography method. In order to deal with the ill-posed property of the traditional network tomography method, we refer to the origin-destination traffic demands of the optical layer in our method. We first study the relationship between the traffic matrix and optical layer traffic demands. Thereby, we take advantage of the relationship between them to construct a linear system. Combining with the traditional network tomography model, we propose a convex and unconstrained optimization model for estimating the traffic matrix. Finally, we also provide numerical results to validate the performance of the proposed method in this literature.

[1]  El-Sayed M. El-Alfy,et al.  A Pareto-based hybrid multiobjective evolutionary approach for constrained multipath traffic engineering optimization in MPLS/GMPLS networks , 2013, J. Netw. Comput. Appl..

[2]  Walter Willinger,et al.  Spatio-Temporal Compressive Sensing and Internet Traffic Matrices (Extended Version) , 2012, IEEE/ACM Transactions on Networking.

[3]  Rong Du,et al.  VANET based traffic estimation: A matrix completion approach , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).

[4]  Admela Jukan,et al.  A novel approach to accurately compute an IP traffic matrix using optical bypass , 2013, 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013).

[5]  Mauro Dell'Orco,et al.  A metaheuristic dynamic traffic assignment model for O-D matrix estimation using aggregate data , 2012 .

[6]  Lei Guo,et al.  A power laws-based reconstruction approach to end-to-end network traffic , 2013, J. Netw. Comput. Appl..

[7]  Kensuke Fukuda,et al.  Towards Modeling of Traffic Demand of Node in Large Scale Network , 2008, 2008 IEEE International Conference on Communications.

[8]  Morteza Mardani,et al.  Robust network traffic estimation via sparsity and low rank , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  M. Murata,et al.  Optical-Layer Traffic Engineering With Link Load Estimation for Large-Scale Optical Networks , 2012, IEEE/OSA Journal of Optical Communications and Networking.

[10]  Ernesto Cipriani,et al.  An Adaptive Bi-Level Gradient Procedure for the Estimation of Dynamic Traffic Demand , 2014, IEEE Transactions on Intelligent Transportation Systems.

[11]  Peng Zhang,et al.  A transform domain-based anomaly detection approach to network-wide traffic , 2014, J. Netw. Comput. Appl..

[12]  Dingde Jiang,et al.  A compressive sensing-based reconstruction approach to network traffic , 2013, Comput. Electr. Eng..

[13]  Hongke Zhang,et al.  An approach for efficient, accurate, and timely estimation of traffic matrices , 2014, 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[14]  Jun Luo,et al.  Efficient traffic matrix estimation for data center networks , 2013, 2013 IFIP Networking Conference.

[15]  Konstantina Papagiannaki,et al.  Traffic matrices: balancing measurements, inference and modeling , 2005, SIGMETRICS '05.

[16]  Walter Willinger,et al.  Spatio-temporal compressive sensing and internet traffic matrices , 2009, SIGCOMM '09.

[17]  Ke Xu,et al.  A Model Approach to the Estimation of Peer-to-Peer Traffic Matrices , 2014, IEEE Transactions on Parallel and Distributed Systems.

[18]  Dingde Jiang,et al.  How to reconstruct end-to-end traffic based on time-frequency analysis and artificial neural network , 2014 .

[19]  Weiqiang Sun,et al.  Traffic demand estimation for hybrid switching systems , 2013, 2013 15th International Conference on Transparent Optical Networks (ICTON).

[20]  Albert G. Greenberg,et al.  Fast accurate computation of large-scale IP traffic matrices from link loads , 2003, SIGMETRICS '03.

[21]  Matthew Roughan,et al.  Computation of IP traffic from link , 2003, SIGMETRICS 2003.

[22]  Ying Chen,et al.  Energy-aware scheduling and resource allocation for periodic traffic demands , 2013, IEEE/OSA Journal of Optical Communications and Networking.

[23]  Hiroshi Hasegawa,et al.  An efficient hierarchical optical path network design algorithm based on a traffic demand expression in a cartesian product space , 2006, IEEE Journal on Selected Areas in Communications.

[24]  George Bebis,et al.  A survey of network flow applications , 2013, J. Netw. Comput. Appl..

[25]  Morteza Mardani,et al.  Recovery of Low-Rank Plus Compressed Sparse Matrices With Application to Unveiling Traffic Anomalies , 2012, IEEE Transactions on Information Theory.

[26]  Tomer Toledo,et al.  Estimation of Dynamic Origin–Destination Matrices Using Linear Assignment Matrix Approximations , 2013, IEEE Transactions on Intelligent Transportation Systems.