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Spatio-Temporal Kronecker Compressive Sensing for Traffic Matrix Recovery

A traffic matrix is generally used by several network management tasks in a data center network, such as traffic engineering and anomaly detection. It gives a flow-level view of the network traffic volume. Despite the explicit importance of the traffic matrix, it is significantly difficult to implement a large-scale measurement to build an absolute traffic matrix. Generally, the traffic matrix obtained by the operators is imperfect, i.e., some traffic data may be lost. Hence, we focus on the problems of recovering these missing traffic data in this paper. To recover these missing traffic data, we propose the spatio-temporal Kronecker compressive sensing method, which draws on Kronecker compressive sensing. In our method, we account for the spatial and temporal properties of the traffic matrix to construct a sparsifying basis that can sparsely represent the traffic matrix. Simultaneously, we consider the low-rank property of the traffic matrix and propose a novel recovery model. We finally assess the estimation error of the proposed method by recovering real traffic.

A power laws-based reconstruction approach to end-to-end network traffic

To obtain accurately end-to-end network traffic is a significantly difficult and challenging problem for network operators, although it is one of the most important input parameters for network traffic engineering. With the development of current network, the characteristics of networks have changed a lot. In this paper, we exploit the characteristics of origin-destination flows and thus grasp the properties of end-to-end network traffic. An important and amazing find of our work is that the sizes of origin-destination flows obey the power laws. Taking advantage of this characteristic, we propose a novel approach to select partial origin-destination flows which are to be measured directly. In terms of the known traffic information, we reconstruct all origin-destination flows via compressive sensing method. In detail, here, we combine the power laws and the constraints of compressive sensing (namely restricted isometry property) together to build measurement matrix and pick up the partial origin-destination flows. Furthermore, we build a reconstruction model from the known information corresponding to compressive sensing reconstruction algorithms. Finally, we reconstruct all origin-destination flows from the observed results by solving the reconstruction model. And we provide numerical simulation results to validate the performance of our method using real backbone network traffic data. It illustrates that our method can recover the end-to-end network traffic more accurately than previous methods.

Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks

Network traffic analysis has been one of the most crucial techniques for preserving a large-scale IP backbone network. Despite its importance, large-scale network traffic monitoring techniques suffer from some technical and mercantile issues to obtain precise network traffic data. Though the network traffic estimation method has been the most prevalent technique for acquiring network traffic, it still has a great number of problems that need solving. With the development of the scale of our networks, the level of the ill-posed property of the network traffic estimation problem is more deteriorated. Besides, the statistical features of network traffic have changed greatly in terms of current network architectures and applications. Motivated by that, in this paper, we propose a network traffic prediction and estimation method respectively. We first use a deep learning architecture to explore the dynamic properties of network traffic, and then propose a novel network traffic prediction approach based on a deep belief network. We further propose a network traffic estimation method utilizing the deep belief network via link counts and routing information. We validate the effectiveness of our methodologies by real data sets from the Abilene and GANT backbone networks.

A reconstructing approach to end-to-end network traffic based on multifractal wavelet model

This paper studies the reconstructing method of end-to-end network traffic. Due to the development of current communication networks, our networks become more complex and heterogeneous. Meanwhile, because of time-varying nature and spatio-temporal correlations of the end-to-end network traffic, to obtain it accurately is a great challenge. We propose to exploit discrete wavelet transforms and multifractal analysis to reconstruct the end-to-end network traffic from time-frequency domain. First, its time-frequency properties can be characterized in detail by discrete wavelet transforms. And then, we combine discrete wavelet transforms and multifractal analysis to reconstruct end-to-end network traffic from link loads. Furthermore, our method needs to measure end-to-end network traffic to build the statistical model named multifractal wavelet model. Finally, simulation results from the real backbone networks suggest that our method can reconstruct the end-to-end network traffic more accurately than previous methods. Copyright

End-to-End Network Traffic Reconstruction Via Network Tomography Based on Compressive Sensing

Traffic matrices (TM) represent the volumes of end-to-end network traffic between each of the origin–destination pairs. Accurate estimates of TM are used by network operators to perform network management functions and traffic engineering tasks. Despite a large number of methods devoted to the problem of traffic matrix estimation, the inference of end-to-end network traffic is still a main challenge in the large-scale IP backbone network, due to an ill-posed nature of itself. In this paper, we focus on the problem of end-to-end network traffic reconstruction. Based on the network tomography method, we propose a simple method to estimate end-to-end network traffic from the aggregated data. By analyzing, in depth, the properties of the network tomography method, compressive sensing reconstruction algorithms are put forward to overcome the ill-posed nature of the network tomography model. In this case, to satisfy the technical conditions of compressive sensing, we propose a modified network tomography model. Besides, we give a further discussion that the proposed model follows the constraints of compressive sensing. Finally, we validate our method by real data from the Abilene and GÉANT backbone networks.

Traffic Matrix Prediction and Estimation Based on Deep Learning for Data Center Networks

Network traffic analysis is a crucial technique for systematically operating a data center network. Many network management functions rely on exact network traffic information. Although a great number of works to obtain network traffic have been carried out in traditional ISP networks, they cannot be employed effectively in data center networks. Motivated by that, we focus on the problem of network traffic prediction and estimation in data center networks. We involve deep learning techniques in the network traffic prediction and estimation fields, and propose two deep architectures for network traffic prediction and estimation, respectively. We first use a deep architecture to explore the time-varying property of network traffic in a data center network, and then propose a novel network traffic prediction approach based on a deep belief network and a logistic regression model. Meanwhile, to deal with the highly ill-pose property of network traffic estimation, we further propose a network traffic estimation method using the deep belief network trained by link counts. We validate the effectiveness of our methodologies by real traffic data.

Modeling network traffic for traffic matrix estimation and anomaly detection based on Bayesian network in cloud computing networks

With the rapid development of a cloud computing network, the network security has been a terrible problem when it provides much more services and applications. Network traffic modeling and analysis is significantly crucial to detect some lawless activities such as DDoS, virus and worms, and so on. Meanwhile, it is a common approach for acquiring a traffic matrix, which can be used by network operators to carry out network management and planning. Although a great number of methods have been proposed to model and analyze the network traffic, it is still a remarkable challenge since the network traffic characterization has been tremendously changed, in particular, for a cloud computing network. Motivated by that, we analyze and model the statistical features of network traffic based on the Bayesian network in this paper. Furthermore, we propose an accurate network traffic estimation approach and an efficient anomaly detection approach, respectively. In detail, we design a Bayesian network structure to model the causal relationships between network traffic entries. Based on this Bayesian network model, we obtain a joint probability distribution of network traffic by the maximum a posteriori approach. Then, we estimate the network traffic in terms of a regularized optimization model. Meanwhile, we also perform anomaly detection based on the proposed Bayesian network structure. We finally discuss the effectiveness of the proposed method for traffic matrix estimation and anomaly detection by applying it to the Abilene and GÉANT networks.

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.

A compressive sensing-based network tomography approach to estimating origin–destination flow traffic in large-scale backbone networks

Summary A traffic matrix can exhibit the volume of network traffic from origin nodes to destination nodes. It is a critical input parameter to network management and traffic engineering, and thus it is necessary to obtain accurate traffic matrix estimates. Network tomography method is widely used to reconstruct end-to-end network traffic from link loads and routing matrix in a large-scale Internet protocol backbone networks. However, it is a significant challenge because solving network tomography model is an ill-posed and under-constrained inverse problem. Compressive sensing reconstruction algorithms have been well known as efficient and precise approaches to deal with the under-constrained inference problem. Hence, in this paper, we propose a compressive sensing-based network traffic reconstruction algorithm. Taking into account the constraints in compressive sensing theory, we propose an approach for constructing a novel network tomography model that obeys the constraints of compressive sensing. In the proposed network tomography model, a framework of measurement matrix according to routing matrix is proposed. To obtain optimal traffic matrix estimates, we propose an iteration algorithm to solve the proposed model. Numerical results demonstrate that our method is able to pursuit the trace of each origin–destination flow faithfully. Copyright

Network Traffic Prediction Based on Deep Belief Network in Wireless Mesh Backbone Networks

Wireless mesh network is prevalent for providing a decentralized access for users. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep belief network and a Gaussian model. The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself. Then a prediction model is built by learning a deep belief network from the extracted low-pass component. Otherwise, for the rest high-pass component that expresses the gusty and irregular fluctuations of network traffic, a Gaussian model is used to model it. We estimate the parameters of the Gaussian model by the maximum likelihood method. Then we predict the high-pass component by the built model. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.