A Traffic Matrix Recovery Algorithm via Low-Dimension Nature in Smart Grid

Abstract:

In this literature, we explore the solution of network traffic recovery in smart grid. Taking account of dimensionality of network traffic in smart grid, we propose a novel reconstruction model via network tomography. In our algorithm, we use the low-dimension nature of traffic matrix and the greedy adaptive dictionary algorithm to convert the network tomography into the problem of sparse reconstruction at first. Then we solve network traffic by an iterative greedy algorithm. Simulation results indicate that proposed algorithm exhibits noticeably improvement in estimation error comparing with previous work.

• Periodical: Applied Mechanics and Materials (Volume 392)
• Pages: 593-597
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.392.593
• Online since: September 2013
• Authors: Fan Bo Meng, Hong Hao Zhao, Qing Qi Zhao, Wei Zhe Ma, Zhi Gang Qi, Jin You Su
• Keywords: Greedy Algorithm, Network Traffic Estimation, Smart Grid (SG), Traffic Matrix

References

[1] X. Fang, S. Misra, G. Xue, and D. Yang, Smart grid-the new and improved power grid: a survey, IEEE Communications Surveys and Tutorials vol. 14, pp.944-980, (2012).

DOI: 10.1109/surv.2011.101911.00087

[2] D. Jiang, G. Hu. GARCH model-based large-scale IP traffic matrix estimation. IEEE Communications Letters. 2009, 13(1): 52-54.

DOI: 10.1109/lcomm.2008.081271

[3] D. Jiang, Z. Xu, L. Nie, et al. An approximate approach to end-to-end traffic in communication networks, Chinese Journal of Electronics, Oct. 2012, 21(4): 705-710.

[4] Y. Yan, Q. Yi, H. Sharif, and D. Tripper, A survey on smart grid communicaiton infrastructures: motivations, requirements and challenges, IEEE Communications Surveys & Tutorials, vol. 15. no. 1, 2012, 21(4): 705-710.

DOI: 10.1109/surv.2012.021312.00034

[5] D. Jiang, Z. Xu, Z. Chen, et al. Joint time-frequency sparse estimation of large-scale network traffic, Computer Networks, Oct. 2011, 55(10): 3533-3547.

DOI: 10.1016/j.comnet.2011.06.027

[6] D. Jiang, Z. Xu, H. Xu, et al. An approximation method of origin- destination flow traffic from link load counts, Computers and Electrical Engineering, Nov. 2011, 37(6): 1106-1121.

DOI: 10.1016/j.compeleceng.2011.06.009

[7] Y. Zhang, M. Roughan, N. Duffield, and A. Greenberg, Fast accurate computation of large-scale IP traffic matrices from link loads, ACM SIGMETRICS Performance Evaluation Review, vol. 31, no. 3, pp.206-207, (2003).

DOI: 10.1145/885651.781053

[8] A. Soule, A. Lakhina, N. Taft, K. Papagiannaki, K. Salamatian, A. Nucci, M. Crovella, and C. Diot, Traffic matrices: balancing Nucci, M. Crovella, and C. Diot, , Traffic matrices: balancing Banff, Canada, June (2005).

DOI: 10.1145/1064212.1064259

[9] H. Zang and A. Nucci, Traffic monitor deployment in IP networks, Computer Networks, vol. 53, no. 14, pp.2491-2501, (2009).

DOI: 10.1016/j.comnet.2009.05.004

[10] G. Liang, N. Taft, and B. Yu, A fast lightweight approach to origin–destination IP traffic estimation using partial measurements, IEEE Transactions on Information Theory, vol. 52, no. 6, pp.2634-2648, (2006).

DOI: 10.1109/tit.2006.874412

[11] T. Adelani and A. Alfa, Hybrid techniques for large-scale IP traffic matrix estimation, in Proc. ICC 2010, pp.1-6, (2010).

DOI: 10.1109/icc.2010.5501843

[12] Y. Zhang, M. Roughan, W. Willinger, and L. Qiu, Spatio-temporal compressive sensing and Internet traffic matrices, in Proc. SIGCOMM 2009, pp.267-278, (2009).

DOI: 10.1145/1594977.1592600

[13] M. Jafari and M. Plumbley, Fast dictionary learning for sparse representations of speech signals, IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 5, pp.1025-1031, (2011).

DOI: 10.1109/jstsp.2011.2157892

[14] J. Tropp, Greed is good: algorithmic results for sparse approximation, IEEE Transactions on Information Theory, vol. 50, no. 10, pp.2231-2242, (2004).

DOI: 10.1109/tit.2004.834793