Large-scale IP Traffic Matrix Research Articles

Tomofanout: a novel approach for large-scale IP traffic matrix estimation with excellent accuracy

Traffic matrix (TM) plays an important role in many network engineering and management tasks. However, the accurate TM estimation is still a challenge because the problem is highly under-constrained. In this paper, we propose a considerably accurate approach, termed as Tomofanout, for the estimation of TM in large-scale IP network using the available link load data, routing matrix, and partial direct measurement data of TM. Firstly, we propose an edge link fanout model which defines each edge link’s fanout, i.e., each edge link’s fractions of traffic emitting from that edge link to other edge links. Secondly, benefited from the edge link fanout’s diurnal pattern and stability, we are able to compute the edge link baseline fanout to estimate the TM at the following days by multiplying it by the edge link loads at the corresponding time intervals. In such way, an initial link-to-link TM estimation result is calculated by the edge link fanout model. Further, by making the corresponding transformation to the link-to-link TM, the router-to-router TM estimation result is thus obtained. Thirdly, the solution is then refined by the basic model of the Tomography method to keep consistent with both the edge and the interior link loads for further improvement of accuracy in estimation. In particular, the expectation maximization (EM) iteration of the basic model of Tomography method is used for further refinement. As the iteration is running on, the edge link fanout model solution is gradually approaching to the final estimation result, which is compatible with both the edge and the interior link loads. Fourthly, a general algorithm is proposed for computing the edge link baseline fanout and the estimation of the TM. Finally, the Tomofanout approach is validated by simulation studies using the real data from the Abilene Network. The simulation results demonstrate that Tomofanout achieves extremely high accuracy: its spatial relative error (SRE) is less than one half of Tomogravity’s, while its temporal relative error (TRE) is less than one half of Fanout’s and is only one third of Tomogravity’s.

Accurate estimation of large-scale IP traffic matrix

Traffic matrix (TM) estimation, which is an interesting and important research topic at present, is used to conduct network management, traffic detecting, provisioning and so on. However, because of inherent characteristics in the IP network, especially in the large-scale IP network, TM estimation itself is highly under-constrained, and so it is an very ill-posed problem. how fast and accurately to attain large-scale IP TM estimation is a challenge. Based on back-propagation neural network (BPNN), this paper proposes a novel method for large-scale IP TM estimation, called BPNN TM estimation (BPTME). In contrast to previous methods, BPTME can easily avoid the complex mathematical computation so that we can quickly estimate the TM. The model of large-scale IP TM estimation built on top of BPNN, whose outputs can sufficiently represent TM's spatial-temporal correlations, ensures that we can attain an accurate estimation result. Finally, we use the real data from the Abilene Network to validate and evaluate BPTME. Simulation results show that BPTME not only improves remarkably and holds better robustness, but it can also make more accurate estimation of large-scale IP TM and track quickly its dynamics.

Mahalanobis distance-based traffic matrix estimation

This letter studies large-scale IP traffic matrix (TM) estimation problem and proposes a novel method called the Mahalanobis distance-based regressive inference (MDRI). By using Mahalanobis distance as an optimal metric, we can get rid of the highly ill-posed nature of this problem. We describe the TM estimation into an optimal process, and then by optimising the regularised equation about this problem, TM's estimation can accurately obtained. Testing results are shown to be promising. Copyright © 2010 John Wiley & Sons, Ltd.

An optimization method of large-scale IP traffic matrix estimation

This letter studies the large-scale IP traffic matrix (TM) estimation problem and proposes a novel method called the simulated annealing and generalized inference (SAGI). The major challenge of large-scale IP TM estimation is its highly ill-posed nature. We describe the TM estimation into a modified simulated annealing process, and then by using the generalized inference we can easily overcome its ill-posed nature. Simulation results show that SAGI is promising.

GARCH model-based large-scale IP traffic matrix estimation

This letter proposes a novel method to estimate large-scale IP traffic matrix (TM). By using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) to model the Origin-Destination (OD) flows, we can easily get rid of the ill-posed problem of large-scale IP TM. Compared with previous methods, our method does not only hold the lower estimation errors but also is more robust to the noise.

An Accurate Approach to Large-Scale IP Traffic Matrix Estimation

This letter proposes a novel method of large-scale IP traffic matrix (TM) estimation, called algebraic reconstruction technique inference (ARTI), which is based on the partial flow measurement and Fratar model. In contrast to previous methods, ARTI can accurately capture the spatio-temporal correlations of TM. Moreover, ARTI is computationally simple since it uses the algebraic reconstruction technique. We use the real data from the Abilene network to validate ARTI. Simulation results show that ARTI can accurately estimate large-scale IP TM and track its dynamics.

An Accurate Approach of Large-Scale IP Traffic Matrix Estimation

This letter proposes a novel method of large-scale IP traffic matrix estimation which is based on Partial Flow Measurement and Fratar Model (PFMFM). Firstly, we model OD flows as Fratar model and introduce the constrained relations between traffic matrix and link loads. By combining partial flow measurement, we can get a good prior value of network tomography. Then a good estimation of traffic matrix is attained with the modified network tomography method. Finally, we use the real data [8] from network Abilene to validate our method. In contrast to TomoGravity [1], the results show that our method improves remarkably and the estimation of traffic matrix is closer to real data, and especially when the flow is small and changes dramatically, the estimation is better.