Method Of Traffic Matrix Estimation Research Articles

Estimating SDN traffic matrix based on online adaptive information gain maximization method

Traffic Matrix (TM) estimation is important for network management and traffic engineering. However, current estimation methods are insufficient in estimation accuracy and measurement cost. In this paper, by using the flow measurement capability in Software Defined Networks (SDN), we propose an Online Information Gain Maximization based SDN traffic matrix estimation method IGME. IGME uses the information gain metric to determine which flows are most informative, and then constructs the measurement flow set iteratively until the accuracy requirement is satisfied or the measurement resource constraint is reached. The experiment results on three Internet measurement datasets show that IGME can improve the estimation accuracy only by consuming a small amount of measurement resource. Besides, the iteration feature of IGME provides a means to dynamically adjust the measurement flow selection choice, so as to adapt to the time-varying characteristics of the network traffic.

Probability-model based network traffic matrix estimation

Traffic matrix is of great help in many network applications. However, it is very difficult to estimate the traffic matrix for a large-scale network. This is because the estimation problem from limited link measurements is highly underconstrained. We propose a simple probability model for a large-scale practical network. The probability model is then generalized to a general model by including random traffic data. Traffic matrix estimation is then conducted under these two models by two minimization methods. It is shown that the Normalized Root Mean Square Errors of these estimates under our model assumption are very small. For a large-scale network, the traffic matrix estimation methods also perform well. The comparison of two minimization methods shown in the simulation results complies with the analysis.

An approximation method of origin–destination flow traffic from link load counts

Traffic matrix (TM) is a key input of traffic engineering and network management. However, it is significantly difficult to attain TM directly, and so TM estimation is so far an interesting topic. Though many methods of TM estimation are proposed, TM is generally unavailable in the large-scale IP backbone networks and is difficult to be estimated accurately. This paper proposes a novel method of TM estimation in large-scale IP backbone networks, which is based on the generalized regression neural network (GRNN), called GRNN TM estimation (GRNNTME) method. Firstly, building on top of GRNN, we present a multi-input and multi-output model of large-scale TM estimation. Because of the powerful capability of learning and generalizing of GRNN, the output of our model can sufficiently capture the spatio-temporal correlations of TM. This ensures that the estimation of TM can accurately be attained. And then GRNNTME uses the procedure of data posttreating further to make the output of our model closer to real value. Finally, we use the real data from the Abilene Network to validate GRNNTME. Simulation results show that GRNNTME can perform well the accurate and fast estimation of TM, track its dynamics, and holds the stronger robustness and lower estimation errors.

Gradually Reconfiguring Virtual Network Topologies Based on Estimated Traffic Matrices

Traffic matrix is essential to traffic engineering (TE) methods. Because it is difficult to monitor traffic matrices directly, several methods for estimating them from link loads have been proposed. However, estimated traffic matrix includes estimation errors which degrade the performance of TE significantly. In this paper, we propose a method that reduces estimation errors while reconfiguring the virtual network topology (VNT) by cooperating with the VNT reconfiguration. In our method, the VNT reconfiguration is divided into multiple stages instead of reconfiguring the suitable VNT at once. By dividing the VNT reconfiguration into multiple stages, our traffic matrix estimation method calibrates and reduces the estimation errors in each stage by using information monitored in prior stages. We also investigate the effectiveness of our proposal using simulations. The results show that our method can improve the accuracy of the traffic matrix estimation and achieve an adequate VNT as is the case with the reconfiguration using the actual traffic matrices.

Theoretical mean-variance relationship of IP network traffic based on ON/OFF model

Mean-variance relationship (MVR), nowadays agreed in power law form, is an important function. It is currently used by traffic matrix estimation as a basic statistical assumption. Because all the existing papers obtain MVR only through empirical ways, they cannot provide theoretical support to power law MVR or the definition of its power exponent. Furthermore, because of the lack of theoretical model, all traffic matrix estimation methods based on MVR have not been theoretically supported yet. By observing both our laboratory and campus network for more than one year, we find that such an empirical MVR is not sufficient to describe actual network traffic. In this paper, we derive a theoretical MVR from ON/OFF model. Then we prove that current empirical power law MVR is generally reasonable by the fact that it is an approximate form of theoretical MVR under specific precondition, which can theoretically support those traffic matrix estimation algorithms of using MVR. Through verifying our MVR by actual observation and public DECPKT traces, we verify that our theoretical MVR is valid and more capable of describing actual network traffic than power lawMVR.