In computer networks, transmitted traffic between origin‐destination nodes has been considered a crucial factor in traffic engineering applications. To date, measuring the traffic directly in high load networks is difficult due to high computational costs. On the other hand, accurate estimation of network traffic by means of link load measurements and routing information is currently a challenging problem. In this paper, we propose a new approach to estimate end‐to‐end traffic, inspired by graph embedding. In the proposed approach, we consider a computer network as a time‐varying graph. Our model provides explicit routing information to a convolutional neural network estimator via link load measurements and network topological structure. When explicit routing information is provided, the learning model is only expected to embed the relations between link loads into a traffic estimation vector, instead of figuring out the routing paths. The experimental results showed that the proposed approach outperforms similar estimators in terms of lower estimation error and better tracking the fluctuations.

End-to-end network traffic analysis is one of the most important factors in design, development, and traffic engineering of large-scale backbone networks. Despite the importance of end-to-end traffic, it is not applicable to measure it directly using protocols such as NetFlow in large-scale networks due to high computational cost. One technique to tackle this problem is estimating endto-end traffic using easily measured neighboring link loads. Tomography model relates these two parameters with linear equations. In this way, the problem turns into an inverse problem. Since the number of link loads is usually smaller than the number of end-to-end flows in a computer network, traffic matrix estimation is considered to be an ill-posed problem. In this paper, we propose two different methods for estimation of end-to-end traffic matrix by means of link load measurements. One of the methods deploys Kalman filter and the other is developed based on Tikhonov regularization with respect to the existence of temporal autocorrelation among traffics. We also propose a best estimation model based on least squares, which explores the temporal autocorrelation in end-to-end traffics. This model is used as the best estimator and has been compared with other methods. We have evaluated the efficiency of the proposed methods using real datasets from Abilene and GÉANT backbone networks. It is worth noting that the proposed methods in this paper also can be used in any inverse problem with temporal autocorrelation

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