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

  • Authors:

  • Dingde Jiang;Zhengzheng Xu;Zhenhua Chen;Yang Han;Hongwei Xu

  • Affiliations:

  • College of Information Science and Engineering, Northeastern University, Shenyang, China and State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommun ...;College of Information Science and Engineering, Northeastern University, Shenyang, China;College of Information Science and Engineering, Northeastern University, Shenyang, China;College of Information Science and Engineering, Northeastern University, Shenyang, China;College of Information Science and Engineering, Northeastern University, Shenyang, China

  • Venue:
  • Computer Networks: The International Journal of Computer and Telecommunications Networking
  • Year:
  • 2011

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Abstract

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.