Zhengzheng Xu

Joint time-frequency sparse estimation of large-scale network traffic

When 3G, WiFi, and WiMax technologies are successfully applied to access networks, current communication networks become more and more complex, more and more heterogeneous, and more difficult to manage. Moreover, network traffic exhibits the increasing diversities and concurrently shows many new characteristics. The real-time end-to-end demand urges network operators to learn and grasp traffic matrix covering their networks. However, unfortunately traffic matrix is significantly difficult directly to attain. Despite many studies made previously about traffic matrix estimation problem, it is a significant challenging to obtain its reliable and accurate solution. Here we propose a novel approach to solve this problem, based on joint time-frequency analysis in transform domain. Different from previous methods, we analyze the time-frequency characteristics about traffic matrix and build the time-frequency model describing it. Generally, traffic matrix can be divided into tendency terms and fluctuation terms. We find that traffic matrix in time-frequency domain owns the more obvious sparsity than in time domain. Obviously, its tendency terms and fluctuation terms also have the lower dimensions in time-frequency domain. This brings us into the field of compressive sensing that is a generic technique for data reconstruction. Additionally, we take into account updating time-frequency model presented with link loads to make our model adaptive. Finally, comparative analysis in two real backbone networks confirms that the accuracy, stability, and effectiveness of our approach.

Network coding-based energy-efficient multicast routing algorithm for multi-hop wireless networks

We study energy-efficient multicast communication in multi-hop wireless networks.We present energy-efficient network model for multi-hop wireless networks.We exploit network coding idea to improve energy efficiency of networks.We propose the corresponding algorithm and perform detailed simulation analysis. Multi-hop multicast routing can provide better communication performance in multi-hop wireless networks. However, existing multi-hop multicast routing hardly take into account energy efficiency of networks. This paper studies the energy-efficient multicast communication aiming at multi-hop wireless networks. Firstly, we analyze energy metric and energy efficiency metric of multi-hop networks. Then the corresponding models are given. Secondly, network coding is used to improve network throughput. Different from previous methods, we here consider that network nodes are satisfied with a certain random distribution. In such a case, it is a challenge to construct the network structure that network coding requires. For the above random network topology, we propose three basic structures of network coding to overcome this problem. Thirdly, we present a flexible energy-efficient multicast routing algorithm for multi-hop wireless networks to extensively exploit the network structure proposed above to maximize network throughput and decrease network energy consumption. Finally, we perform numerical experiments by network simulation. Simulation results indicate that our approach is significantly promising.

A collaborative multi-hop routing algorithm for maximum achievable rate

This paper studies collaborative multi-hop communication technology in next generation wireless communications. We propose a collaborative multi-hop routing algorithm combined with clustering to improve network performance. To build the multi-hop routing with maximum achievable rate, a relation matrix is exploited to describe the possible coverage of network nodes. A clustering-based path strategy is presented to create the effective next-hop link. A collaboration strategy is proposed to construct collaborative matrix. And then by clustering and collaboration, a multi-hop routing with maximum achievable rate is successfully built. The effectiveness and the feasibility of the proposed methods are verified by simulation results.

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.

A novel hybrid prediction algorithm to network traffic

Network traffic describes the characteristics and users’ behaviors of communication networks. It is a crucial input parameter of network management and network traffic engineering. This paper proposes a new prediction algorithm to network traffic in the large-scale communication network. First, we use signal analysis theory to transform network traffic from time domain to time-frequency domain. In the time-frequency domain, the network traffic signal is decomposed into the low-frequency and high-frequency components. Second, the gray model is exploited to model the low-frequency component of network traffic. The white Gaussian noise model is utilized to describe its high-frequency component. This is reasonable because the low-frequency and high-frequency components, respectively, represent the trend and fluctuation properties of network traffic, while the gray model and white Gaussian noise model can well capture the characteristics. Third, the prediction models of low-frequency and high-frequency components are built. The hybrid prediction algorithm is proposed to overcome the problem of network traffic prediction in the communication network. Finally, network traffic data from the real network is used to validate our approach. Simulation results indicate that our algorithm holds much lower prediction error than previous methods.

Topology control‐based collaborative multicast routing algorithm with minimum energy consumption

SUMMARY This paper studies the multicast routing problem in the multi-hop wireless network. We exploit topology control to put forward a multicast routing algorithm with minimum energy consumption. First, network nodes are classified as different clusters. Then, the end-to-end multicast routing is appropriately built by using the cooperation among clusters and in each cluster and by minimizing the energy consumption. Unlike previous methods, we employ the appropriate cooperation among/in clusters and the optimal cross-layer design to attain the information from the different layers and the different nodes. In a result, on the basis of the information, the needed clusters of nodes are correctly created. This is helpful to avoid clustering blindly network nodes and to reduce computational overheads. Simulation results show that the proposed algorithm is promising and effective. Copyright

A compressive sensing-based reconstruction approach to network traffic

Traffic matrix in a network describes the end-to-end network traffic which embodies the network-level status of communication networks from origin to destination nodes. It is an important input parameter of network traffic engineering and is very crucial for network operators. However, it is significantly difficult to obtain the accurate end-to-end network traffic. And thus obtaining traffic matrix precisely is a challenge for operators and researchers. This paper studies the reconstruction method of the end-to-end network traffic based on compressing sensing. A detailed method is proposed to select a set of origin-destination flows to measure at first. Then a reconstruction model is built via these measured origin-destination flows. And a purely data-driven reconstruction algorithm is presented. Finally, we use traffic data from the real backbone network to verify our approach proposed in this paper.

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

An accurate method to estimate traffic matrices from link loads for QoS provision

Effective traffic matrix estimation is the basis of efficient traffic engineering, and therefore, quality of service provision support in IP networks. In this study, traffic matrix estimation is investigated in IP networks and an Elman neural network-based traffic matrix inference (ENNTMI) method is proposed. In ENNTMI, the conventional Elman neural network is modified to capture the spatiotemporal correlations and the time-varying property, and certain side information is introduced to help estimate traffic matrix in a network accurately. The regular parameter is further introduced into the optimal equation. Thus, the highly ill-posed nature of traffic matrix estimation is overcome effectively and efficiently.

An Optimal Estimation of Origin-Destination Traffic in Large-Scale Backbone Network

This paper proposes a constrained iterative optimal approach to estimate traffic matrix, namely all origin-destination traffic, in a large-scale backbone network. Based on the modified principal component analysis method, we denote traffic matrix estimation problem into an iterative optimal process under the constraints followed by it. In each iterative step, the covariance matrix of traffic matrix is used to capture its spatio-temporal correlation in order to make the more accurate estimation. Furthermore, we present an iterative adjustment method to find the optimal solution in accordance with link load deviation yielded by traffic matrix estimation. Thus this will gradually overcome the highly ill-posed nature of this problem and obtain the accurate estimation. Finally, we use the real data from a backbone network to validate our method. Simulation results show that our method is effective and practical.