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Real-time monitoring and forecasting technology for network traffic has played an important role in network management. Effective network traffic prediction could analyze and solve problems before overload occurs, which significantly improves network availability. In this paper, after the vulnerability of traditional nonlinear prediction method in forecasting modeling is analyzed, the relevant local (RL) forecast which is based on correlation analysis and the parameter optimization method based on pattern search (PS) is introduced. Using the correlation analysis, the optimal training subset is chosen from time-and distance-correlated training samples. On this basis, the prediction model is established by LSSVM. Finally network traffic dataset collected from wired campus networks is studied for our experiments. And the results show that the relevant local LSSVM prediction method whose training set and parameters have been automatically optimized can effectively predict the small scale traffic measurement data, and RL-LSSVM traffic forecasting algorithm exhibits significantly good prediction accuracy for the data set compared with previous algorithm.
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Keywords:
- network traffic prediction /
- chaos time series forecasting /
- least squares support vector machine /
- local prediction
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[2] Marco L, Matteo B, Paolo F 2013 IEEE Trans. on Int. Trans. Sys. 2 871
[3] Ana M, Rivalino M, Autran M, Paulo R M M, Lucio B A 2011 12th International Conference on Parallel and Distributed Computing, Applications and Technologies Gwangju, Korea, October 20-22, 2011 109
[4] Jun J, Symeon P 2006 Computer Communications 29 1627
[5] Li R, Chen J, Liu Y, Wang Z 2010 The Journal of China Universities of Posts and Telecommunications 17 88
[6] Manoel C, Young S J, Myong K J, Lee D H 2009 Expert Systems with Applications 36 6164
[7] Eleni I V, Matthew G K, John C G 2005 Transportation Research Part C 13 211
[8] Chang H, Lee Y, Yoon B, Baek S 2011 IET Intell. Transo. Syst. 6 292
[9] Tigran T T, Biswajit B, Margaret O M 2012 IEEE Trans. on Int. Trans. Sys. 13 519
[10] Bao R C, Hsiu F T 2009 Expert Systems with Applications 36 6960
[11] Sun H L, Jin Y H, Cui Y D, Cheng S D 2009 Chin. Phys. B 18 4760
[12] Liu X W, Fang X M, Qin Z H, Ye C, Miao X 2011 J. Netw. Syst. Manage 19 427
[13] Bao R C, Hsiu F T 2009 Applied Soft Computing 9 1177
[14] Chen Y H, Yang B, Meng Q F 2012 Applied Soft Computing 12 274
[15] Meng Q F, Chen Y H, Peng Y H 2009 Chin. Phys. B 18 2194
[16] Meng Q F, Chen Y H, Feng Z Q, Wang F L, Chen S S 2013 Acta Phys. Sin. 62 150509 (in Chinese) [孟庆芳, 陈月辉, 冯志全, 王枫林, 陈珊珊 2013 62 150509]
[17] Vapnik V N 1999 The Nature of Statistical Learning Theory (2nd Ed.) (New York, Springer)
[18] Sapankevych N I, Sankar R 2009 IEEE Comput. Intell. Mag. 4 24
[19] Wang X D, Ye M Y 2004 Chin. Phys. 13 454
[20] Sun J C, Zhou Y T, Luo J G 2006 Chin. Phys. 15 1208
[21] Liu H, Liu D, Deng L F 2006 Chin. Phys. 15 1196
[22] Tang Z J, Ren F, Peng T, Wang W B 2014 Acta Phys. Sin. 63 050505 (in Chinese) [唐舟进, 任峰, 彭涛, 王文博 2014 63 050505]
[23] Farmer J D, Sidorowich J J 1987 Phys. Rev. Lett. 59 845
[24] Jawad N, Keem S Y, Farrukh N, Sieh K T, Syed K A 2011 Applied Soft Computing 11 4774
[25] Cai C Z, Fei J F, Wen Y F, Zhu X J, Xiao T T 2009 Acta Phys. Sin. 58 S008 (in Chinese) [蔡从中, 裴军芳, 温玉锋, 朱星键, 肖婷婷 2009 58 S008]
[26] Huang T Y 2008 Chinese Journal Of Computers 31 1200 (in Chinese) [黄天云 2008 计算机学报 31 1200]
[27] Ligang Z, Kin K L, Lean Y 2009 Soft Comput. 13 149
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[1] Man C T, Wong S C, Jian M X, Zhan R G, Peng Z 2009 IEEE Trans. on Int. Trans. Sys. 10 60
[2] Marco L, Matteo B, Paolo F 2013 IEEE Trans. on Int. Trans. Sys. 2 871
[3] Ana M, Rivalino M, Autran M, Paulo R M M, Lucio B A 2011 12th International Conference on Parallel and Distributed Computing, Applications and Technologies Gwangju, Korea, October 20-22, 2011 109
[4] Jun J, Symeon P 2006 Computer Communications 29 1627
[5] Li R, Chen J, Liu Y, Wang Z 2010 The Journal of China Universities of Posts and Telecommunications 17 88
[6] Manoel C, Young S J, Myong K J, Lee D H 2009 Expert Systems with Applications 36 6164
[7] Eleni I V, Matthew G K, John C G 2005 Transportation Research Part C 13 211
[8] Chang H, Lee Y, Yoon B, Baek S 2011 IET Intell. Transo. Syst. 6 292
[9] Tigran T T, Biswajit B, Margaret O M 2012 IEEE Trans. on Int. Trans. Sys. 13 519
[10] Bao R C, Hsiu F T 2009 Expert Systems with Applications 36 6960
[11] Sun H L, Jin Y H, Cui Y D, Cheng S D 2009 Chin. Phys. B 18 4760
[12] Liu X W, Fang X M, Qin Z H, Ye C, Miao X 2011 J. Netw. Syst. Manage 19 427
[13] Bao R C, Hsiu F T 2009 Applied Soft Computing 9 1177
[14] Chen Y H, Yang B, Meng Q F 2012 Applied Soft Computing 12 274
[15] Meng Q F, Chen Y H, Peng Y H 2009 Chin. Phys. B 18 2194
[16] Meng Q F, Chen Y H, Feng Z Q, Wang F L, Chen S S 2013 Acta Phys. Sin. 62 150509 (in Chinese) [孟庆芳, 陈月辉, 冯志全, 王枫林, 陈珊珊 2013 62 150509]
[17] Vapnik V N 1999 The Nature of Statistical Learning Theory (2nd Ed.) (New York, Springer)
[18] Sapankevych N I, Sankar R 2009 IEEE Comput. Intell. Mag. 4 24
[19] Wang X D, Ye M Y 2004 Chin. Phys. 13 454
[20] Sun J C, Zhou Y T, Luo J G 2006 Chin. Phys. 15 1208
[21] Liu H, Liu D, Deng L F 2006 Chin. Phys. 15 1196
[22] Tang Z J, Ren F, Peng T, Wang W B 2014 Acta Phys. Sin. 63 050505 (in Chinese) [唐舟进, 任峰, 彭涛, 王文博 2014 63 050505]
[23] Farmer J D, Sidorowich J J 1987 Phys. Rev. Lett. 59 845
[24] Jawad N, Keem S Y, Farrukh N, Sieh K T, Syed K A 2011 Applied Soft Computing 11 4774
[25] Cai C Z, Fei J F, Wen Y F, Zhu X J, Xiao T T 2009 Acta Phys. Sin. 58 S008 (in Chinese) [蔡从中, 裴军芳, 温玉锋, 朱星键, 肖婷婷 2009 58 S008]
[26] Huang T Y 2008 Chinese Journal Of Computers 31 1200 (in Chinese) [黄天云 2008 计算机学报 31 1200]
[27] Ligang Z, Kin K L, Lean Y 2009 Soft Comput. 13 149
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