Network traffic prediction method based on improved ABC algorithm optimized EM-ELM

doi:10.19682/j.cnki.1005-8885.2018.0014

中国邮电高校学报(英文) ›› 2018, Vol. 25 ›› Issue (3): 33-44.doi: 10.19682/j.cnki.1005-8885.2018.0014

Network traffic prediction method based on improved ABC algorithm optimized EM-ELM

田中大¹,李树江¹,王艳红¹,王向东²

1. 沈阳工业大学
2. 沈阳工业大学信息科学与工程学院

收稿日期:2017-08-29 修回日期:2018-01-14 出版日期:2018-06-29 发布日期:2018-06-30
通讯作者: 田中大 E-mail:tianzhongda@126.com
基金资助:
辽宁省自然科学基金;辽宁省教育厅科学研究项目

Network traffic prediction method based on improved ABC algorithm optimized EM-ELM

Received:2017-08-29 Revised:2018-01-14 Online:2018-06-29 Published:2018-06-30

摘要/Abstract

摘要： In order to overcome the poor generalization ability and low accuracy of traditional network traffic prediction methods, a prediction method based on improved artificial bee colony (ABC) algorithm optimized error minimized extreme learning machine (EM-ELM) is proposed. EM-ELM has good generalization ability. But many useless neurons in EM-ELM have little influences on the final network output, and reduce the efficiency of the algorithm. Based on the EM-ELM, an improved ABC algorithm is introduced to optimize the parameters of the hidden layer nodes, decrease the number of useless neurons. Network complexity is reduced. The efficiency of the algorithm is improved. The stability and convergence property of the proposed prediction method are proved. The proposed prediction method is used in the prediction of network traffic. In the simulation, the actual collected network traffic is used as the research object. Compared with other prediction methods, the simulation results show that the proposed prediction method reduces the training time of the prediction model, decreases the number of hidden layer nodes. The proposed prediction method has higher prediction accuracy and reliable performance. At the same time, the performance indicators are improved.

关键词: error minimized extreme learning machine, improved artificial bee colony algorithm, network traffic, prediction

Abstract: In order to overcome the poor generalization ability and low accuracy of traditional network traffic prediction methods, a prediction method based on improved artificial bee colony (ABC) algorithm optimized error minimized extreme learning machine (EM-ELM) is proposed. EM-ELM has good generalization ability. But many useless neurons in EM-ELM have little influences on the final network output, and reduce the efficiency of the algorithm. Based on the EM-ELM, an improved ABC algorithm is introduced to optimize the parameters of the hidden layer nodes, decrease the number of useless neurons. Network complexity is reduced. The efficiency of the algorithm is improved. The stability and convergence property of the proposed prediction method are proved. The proposed prediction method is used in the prediction of network traffic. In the simulation, the actual collected network traffic is used as the research object. Compared with other prediction methods, the simulation results show that the proposed prediction method reduces the training time of the prediction model, decreases the number of hidden layer nodes. The proposed prediction method has higher prediction accuracy and reliable performance. At the same time, the performance indicators are improved.

Key words: error minimized extreme learning machine, improved artificial bee colony algorithm, network traffic, prediction

参考文献

1. Tso F P, Jouet S, Pezaros D P. Network and server resource management strategies for data centre infrastructures: A survey. Computer Networks, 2016, 106: 209-225

2. Duan H Q, Tang B H. Prediction of data flow in computer network based on linear multi-scale model. Journal of Shenyang University of Technology, 2017, 39(3): 322-327 (in Chinese)

3. Tian Z D, Li S J. A network traffic prediction method based on IFS algorithm optimised LSSVM. International Journal of Engineering Systems Modelling and Simulation, 2017, 9(4): 200-213

4. Oliveira T P, Barbar J S, Soares A S. Computer network traffic prediction: A comparison between traditional and deep learning neural networks. International Journal of Big Data Intelligence, 2016, 3(1): 28-37

5. Lai Y X, Chen Y N, Liu Z H, et al. On monitoring and predicting mobile network traffic abnormality. Simulation Modelling Practice and Theory, 2015, 50: 176-188

6. Yu Q, Jibin L, Jiang L R. An improved ARIMA-based traffic anomaly detection algorithm for wireless sensor networks. International Journal of Distributed Sensor Networks, 2016: Article 28/1-9

7. Wang J. A process level network traffic prediction algorithm based on ARIMA model in smart substation. Proceedings of the 2013 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC'13), Aug 5-8, 2013, Kunming, China. Piscataway, NJ, USA: IEEE, 2013: 5p

8. Yu Y H, Song M N, Fu Y, et al. Traffic prediction in 3G mobile networks based on multifractal exploration. Tsinghua Science and Technology, 2013, 18(4): 398-405

9. Liang Y L, Qiu L R. Network traffic prediction based on SVR improved by chaos theory and ant colony optimization. International Journal of Future Generation Communication and Networking, 2015, 8(1): 69-78

10. Liu X W, Li H, Chen L, et al. An improved forecasting algorithm for wireless network traffic. IEICE Electronics Express, 2011, 8(12): 916-922

11. Peng T, Tang Z J. A small scale forecasting algorithm for network traffic based on relevant local least squares support vector machine regression model. Applied Mathematics and Information Sciences, 2015, 9(2): 653-659

12. Tian Z D, Li S J, Wang Y H, et al. IFS-LSSVM and its application in time-delay series prediction. Electric Machines and Control, 2015, 19(11): 104-110 (in Chinese)

13. Rutka G. Neural network models for Internet traffic prediction. Electronics and Electrical Engineering (Elektronika ir Elektrotechnika), 2006, (4): 55-58

14. Singh R, Kumar H, Singla R K. An intrusion detection system using network traffic profiling and online sequential extreme learning machine. Expert Systems with Applications, 2015, 42(22): 8609-8624

15. Park D C. Structure optimization of BiLinear recurrent neural networks and its application to Ethernet network traffic prediction. Information Sciences, 2013, 237(13): 18-28

16. Wei D F. Network traffic prediction based on RBF neural network optimized by improved gravitation search algorithm. Neural Computing and Applications, 2017, 28: 2303-2312

17. Sun H L, Jin Y H, Cui Y D, et al. Large-time scale network traffic short-term prediction by grey model. Journal of Beijing University of Posts and Telecommunications, 2010, 33(1): 7-11 (in Chinese)

18. Chen Y H, Yang B, Meng Q F. Small-time scale network traffic prediction based on flexible neural tree. Applied Soft Computing, 2012, 12(1): 274-279

19. Tian Z D, Gao X W, Li S J, et al. Prediction method for network traffic based on genetic algorithm optimized echo state network. Journal of Computer Research and Development, 2015, 52(5): 1137-1145 (in Chinese)

20. Tian Z D, Li S J, Wang Y H, et al. A network traffic hybrid prediction model optimized by improved harmony search algorithm. Neural Network World, 2015, 25(6): 669-685

21. Tian Z D, Li S J, Wang Y H, et al. Network traffic multi-step prediction based on chaos theory and improved echo state network. Journal on Communications, 2016, 37(3): 55-70 (in Chinese)

22. Sun G. Network traffic prediction based on the wavelet analysis and Hopfield neural network. International Journal of Future Computer and Communication, 2013, 2(2): 101-105

23. Katris C, Daskalaki S. Comparing forecasting approaches for Internet traffic. Expert Systems with Applications, 2015, 42(21): 8172-8183

24. Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications. Neurocomputing, 2006, 70(1/2/3): 489-501

25. Lan Y, Soh Y C, Huang G B. Two-stage extreme learning machine for regression. Neurocomputing, 2010, 73(16/17/18): 3028-3038

26. Huang G B, Zhou H M, Ding X J, et al. Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics , Part B: Cybernetics, 2012, 42(2): 513-529

27. Wang D D, Wang R, Yan H. Fast prediction of protein-protein interaction sites based on extreme learning machines. Neurocomputing, 2014, 128: 258-266

28. Ertam F, Avci E. Network traffic classification via kernel based extreme learning machine. International Journal of Intelligent Systems and Applications in Engineering, 2016, 4: 109-113

29. Tian Z D, Li S J, Wang Y H, et al. Network traffic prediction method based on extreme learning machine with ARIMA compensation. Information and Control, 2014, 43(6): 705-710 (in Chinese)

30. Huang G B, Chen L, Siew C K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Network, 2006, 17(4): 879-892

31. Huang G B, Chen L. Enhanced random search based incremental extreme machine. Neurocomputing, 2008, 71(16/17/18): 3460-3468

32. Wang W, Zhang R. Improved convex incremental extreme learning machine based on enhanced random search. Unifying Electrical Engineering and Electronics Engineering: Proceedings of the 2012 International Conference on Electrical and Electronics Engineering (ICEEE’12), Jul 4-6, 2012, London, UK. LNEE 238. New York, NY, USA: Springer-Verlag, 2014: 2033-2040

33. Feng G R, Huang G B, Lin Q P, et al. Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Transactions on Neural Networks, 2009, 20(8):1352-1357

34. Yang Y M, Wang Y N, Yuan X F, Bidirectional extreme learning machine for regression problem and its learning effectiveness. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(9): 1498-1505

35. Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 2007, 39(3): 459-471

36. Karaboga D, Gorkemli B, Ozturk C, et al. A comprehensive survey: Artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 2012, 42(1): 21-57

37. Zhang Y X, Tian X M, Deng X G. Blind source separation based on modified artificial bee colony algorithm. Acta Electronica Sinica, 2012, 40(10): 2026-2030 (in Chinese)

Network traffic prediction method based on improved ABC algorithm optimized EM-ELM

Network traffic prediction method based on improved ABC algorithm optimized EM-ELM

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价