Journal of Network and Systems Management

, Volume 25, Issue 2, pp 290–320 | Cite as

Performance Analysis of Network Traffic Predictors in the Cloud

  • Bruno L. Dalmazo
  • João P. Vilela
  • Marilia Curado
Article

Abstract

Predicting the inherent traffic behaviour of a network is an essential task, which can be used for various purposes, such as monitoring and managing the network’s infrastructure. However, the recent surge of dynamic environments, such as Internet of Things and Cloud Computing have hampered this task. This means that the traffic on these networks is even more complex, displaying a nonlinear behaviour with specific aperiodic characteristics during daily operation. Traditional network traffic predictors are usually based on large historical data bases which are used to train algorithms. This may not be suitable for these highly volatile environments, where the strength of the force exerted in the interaction between past and current values may change quickly with time. In light of this, a taxonomy for network traffic prediction models, including the review of state of the art, is presented here. In addition, an analysis mechanism, focused on providing a standardized approach for evaluating the best candidate predictor models for these environments, is proposed. These contributions favour the analysis of the efficacy and efficiency of network traffic prediction among several prediction models in terms of accuracy, historical dependency, running time and computational overhead. An evaluation of several prediction mechanisms is performed by assessing the Normalized Mean Square Error and Mean Absolute Percent Error of the values predicted by using traces taken from two real case studies in cloud computing.

Keywords

Cloud computing Prediction models taxonomy Computational complexity Network traffic prediction analysis 

References

  1. 1.
    Mell, P., Grance, T.: The NIST Definition of Cloud Computing. National Institute of Standards and Technology, Gaithersburg (2011)CrossRefGoogle Scholar
  2. 2.
    Owezarski, P., Lobo, J., Medhi, D.: Network and service management for cloud computing and data centers: a report on CNSM 2012. J. Netw. Syst. Manag. 21, 707–712 (2013)CrossRefGoogle Scholar
  3. 3.
    Dainotti, A., Pescape, A., Claffy, K.: Issues and future directions in traffic classification. IEEE Netw. 26, 35–40 (2012)CrossRefGoogle Scholar
  4. 4.
    Prangchumpol, D.: A network traffic prediction algorithm based on data mining technique. In: Proceedings of World Academy of Science, Engineering and Technology, vol. 79, p. 1196 (2013)Google Scholar
  5. 5.
    Rahmani, M., Tappayuthpijarn, K., Krebs, B., Steinbach, E., Bogenberger, R.: Traffic shaping for resource-efficient in-vehicle communication. IEEE Trans. Ind. Inform. 5, 414–428 (2009)CrossRefGoogle Scholar
  6. 6.
    Sang, A., Li, S.-Q.: A predictability analysis of network traffic. Comput. Netw. 39, 329–345 (2002)CrossRefGoogle Scholar
  7. 7.
    Papadopouli, M., Raftopoulos, E., Shen, H.: Evaluation of short-term traffic forecasting algorithms in wireless networks. In: 2nd Conference on Next Generation Internet Design and Engineering, 2006 (NGI’06), pp. 8–14. IEEE (2006)Google Scholar
  8. 8.
    Zhou, B., He, D., Sun, Z.: Traffic modeling and prediction using ARIMA/GARCH model. In: Modeling and Simulation Tools for Emerging Telecommunication Networks, pp. 101–121. Springer, Berlin (2006)Google Scholar
  9. 9.
    Salah, K., Elbadawi, K., Boutaba, R.: An analytical model for estimating cloud resources of elastic services. J. Netw. Syst. Manag. 1–24 (2015)Google Scholar
  10. 10.
    Ballani, H., Costa, P., Karagiannis, T., Rowstron, A.I.: Towards predictable datacenter networks. In: SIGCOMM, vol. 11, pp. 242–253 (2011)Google Scholar
  11. 11.
    Vieira, K., Schulter, A., Westphall, C., Westphall, C.: Intrusion detection for grid and cloud computing. IT Prof. 12, 38–43 (2010)CrossRefGoogle Scholar
  12. 12.
    Plonka, D., Barford, P.: Network anomaly confirmation, diagnosis and remediation. In: 47th Annual Allerton Conference on Communication, Control, and Computing, 2009, pp. 128–135. Allerton (2009). doi:10.1109/ALLERTON.2009.5394858
  13. 13.
    Xie, Y., Zhang, Y., Ye, Z.: Short-term traffic volume forecasting using Kalman filter with discrete wavelet decomposition. Comput. Aided Civil Infrastruct. Eng. 22, 326–334 (2007)CrossRefGoogle Scholar
  14. 14.
    Xiong, W., Hu, H., Xiong, N., Yang, L.T., Peng, W.-C., Wang, X., Qu, Y.: Anomaly secure detection methods by analyzing dynamic characteristics of the network traffic in cloud communications. Inf. Sci. 258, 403–415 (2013)CrossRefGoogle Scholar
  15. 15.
    Buyya, R., Broberg, J., Goscinski, A .M.: Cloud Computing: Principles and Paradigms. Wiley, New York (2010)Google Scholar
  16. 16.
    Lim, T.-S., Loh, W.-Y., Shih, Y.-S.: A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Mach. Learn. 40, 203–228 (2000)CrossRefMATHGoogle Scholar
  17. 17.
    Dalmazo, B.L., Vilela, J.P., Curado, M.: Predicting traffic in the cloud: a statistical approach. In: Third International Conference on Cloud and Green Computing (CGC’13), pp. 121–126 (2013). doi:10.1109/CGC.2013.26
  18. 18.
    Dalmazo, B.L., Vilela, J.P., Curado, M.: Online traffic prediction in the cloud: a dynamic window approach. In: The 2nd International Conference on Future Internet of Things and Cloud (FiCloud’2014), pp. 9–14 (2014). doi:10.1109/FiCloud.2014.12
  19. 19.
    Dalmazo, B.L., Vilela, J.P., Curado, M.: Online traffic prediction in the cloud. Int. J. Netw. Manag. 26, 269–285 (2016). doi:10.1002/nem.1934 CrossRefGoogle Scholar
  20. 20.
    Hoque, N., Bhuyan, M.H., Baishya, R., Bhattacharyya, D., Kalita, J.: Network attacks: taxonomy, tools and systems. J. Netw. Comput. Appl. 40, 307–324 (2014)CrossRefGoogle Scholar
  21. 21.
    Whaiduzzaman, M., Sookhak, M., Gani, A., Buyya, R.: A survey on vehicular cloud computing. J. Netw. Comput. Appl. 40, 325–344 (2014)CrossRefGoogle Scholar
  22. 22.
    Lu, X., Yu, Z., Guo, B., Zhou, X.: Predicting the content dissemination trends by repost behavior modeling in mobile social networks. J. Netw. Comput. Appl. 42, 197–207 (2014)CrossRefGoogle Scholar
  23. 23.
    Cortez, P., Rio, M., Rocha, M., Sousa, P.: Internet traffic forecasting using neural networks. In: International Joint Conference on Neural Networks, 2006 (IJCNN’06), pp. 2635–2642 (2006). doi:10.1109/IJCNN.2006.247142
  24. 24.
    Dalmazo, B., Cordeiro, W., Rabelo, L., Wickboldt, J., Lunardi, R., dos Santos, R., Gaspary, L., Granville, L., Bartolini, C., Hickey, M.: Leveraging it project lifecycle data to predict support costs. In: IFIP/IEEE International Symposium on Integrated Network Management (IM’2011), pp. 249–256 (2011). doi:10.1109/INM.2011.5990698
  25. 25.
    Dainotti, A., De Donato, W., Pescape, A., Salvo Rossi, P.: Classification of network traffic via packet-level hidden markov models. In: Global Telecommunications Conference, 2008. IEEE GLOBECOM 2008, New Orleans, Louisiana, pp. 1–5 (2008)Google Scholar
  26. 26.
    Erman, J., Mahanti, A., Arlitt, M.: Qrp05-4: internet traffic identification using machine learning. In: Global Telecommunications Conference, 2006 (GLOBECOM’06), pp. 1–6. IEEE (2006). doi:10.1109/GLOCOM.2006.443
  27. 27.
    Nguyen, T., Armitage, G.: A survey of techniques for internet traffic classification using machine learning. IEEE Commun. Surv. Tutor. 10, 56–76 (2008)CrossRefGoogle Scholar
  28. 28.
    Chen, Y., Yang, B., Meng, Q.: Small-time scale network traffic prediction based on flexible neural tree. Appl. Soft Comput. 12, 274–279 (2012)CrossRefGoogle Scholar
  29. 29.
    Jin, H., Li, L.: Dynamic network traffic flow prediction model based on modified quantum-behaved particle swarm optimization. J. Netw. 8(10), 2332–2339 (2013)Google Scholar
  30. 30.
    Auld, T., Moore, A., Gull, S.: Bayesian neural networks for internet traffic classification. IEEE Trans. Neural Netw. 18, 223–239 (2007)CrossRefGoogle Scholar
  31. 31.
    Bermolen, P., Rossi, D.: support vector regression for link load prediction. Comput. Netw. 53, 191–201 (2009). (QoS aspects in next-generation networks)CrossRefGoogle Scholar
  32. 32.
    Freedman, D.A.: Statistical Models: Theory and Practice. Cambridge University Press, Cambridge (2009)CrossRefMATHGoogle Scholar
  33. 33.
    Torres, J.L., Garca, A., De Blas, M., De Francisco, A.: Forecast of hourly average wind speed with ARMA models in navarre (Spain). Sol. Energy 79, 65–77 (2005)CrossRefGoogle Scholar
  34. 34.
    Song, H., Li, G.: Tourism demand modelling and forecastinga review of recent research. Tour. Manag. 29, 203–220 (2008)CrossRefGoogle Scholar
  35. 35.
    Zare Moayedi, H., Masnadi-Shirazi, M.: Arima model for network traffic prediction and anomaly detection. In: International Symposium on Information Technology, 2008 (ITSim 2008), vol. 4, pp. 1–6 (2008). doi:10.1109/ITSIM.2008.4631947
  36. 36.
    Zhao, H.: Multiscale analysis and prediction of network traffic. In: IEEE 28th International on Performance Computing and Communications Conference (IPCCC), pp. 388–393 (2009)Google Scholar
  37. 37.
    Zhang, G., Qi, M.: Neural network forecasting for seasonal and trend time series. Eur. J. Oper. Res. 160, 501–514 (2005). (Decision support systems in the internet age)CrossRefMATHGoogle Scholar
  38. 38.
    Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput. 9, 3–12 (2005)CrossRefGoogle Scholar
  39. 39.
    Lee, W., Chen, C., Chen, K., Chen, T., Liu, C.: A comparative study on the forecast of fresh food sales using logistic regression, moving average and bpnn methods. J. Mar. Sci. Technol. 20, 142–152 (2012)Google Scholar
  40. 40.
    Li, A., Han, Y., Zhou, B., Han, W., Jia, Y.: Detecting hidden anomalies using sketch for high-speed network data stream monitoring. Appl. Math. 6, 759–765 (2012)Google Scholar
  41. 41.
    Klinker, F.: Exponential moving average versus moving exponential average. Math. Semesterber. 58, 97–107 (2011)CrossRefMATHGoogle Scholar
  42. 42.
    Wilamowski, B.: Neural network architectures and learning algorithms. IEEE Ind. Electron. Mag. 3, 56–63 (2009)CrossRefGoogle Scholar
  43. 43.
    Akesson, B.M., Toivonen, H.T.: A neural network model predictive controller. J. Process Control 16, 937–946 (2006)CrossRefGoogle Scholar
  44. 44.
    Li, W., Moore, A.W.: A machine learning approach for efficient traffic classification. In: 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, 2007 (MASCOTS’07). IEEE, pp. 310–317 (2007)Google Scholar
  45. 45.
    Zhani, M.F., Elbiaze, H., Kamoun, F.: Analysis and prediction of real network traffic. J. Netw. 4(9), 855–865 (2009)Google Scholar
  46. 46.
    Chunlin, L., Layuan, L.: Multi-layer resource management in cloud computing. J. Netw. Syst. Manag. 22, 100–120 (2014)CrossRefGoogle Scholar
  47. 47.
    Chatfield, C., Yar, M.: Holt–winters forecasting: some practical issues. J. Roy. Stat. Soc. D-Sta. 37(2), 129–140 (1988)Google Scholar
  48. 48.
    Gardiner, C.W., et al.: Handbook of Stochastic Methods, vol. 3. Springer, Berlin (1985)Google Scholar
  49. 49.
    Wei, W .W.-S.: Time Series Analysis. Addison-Wesley publ, Reading (1994)Google Scholar
  50. 50.
    Joshi, M., Hadi, T.H.: A Review of Network Traffic Analysis and Prediction Techniques (2015). arXiv:1507.05722
  51. 51.
    Weigend, A.S., Gershenfeld, N.A. (eds.): Time Series Prediction: Forecasting the Future and Understanding the Past. Westview Press, Boulder (1994)Google Scholar
  52. 52.
    Makridakis, S., Wheelwright, S.C., Hyndman, R.J.: Forecasting Methods and Applications. Wiley, New York (2008)Google Scholar
  53. 53.
    Yin, H., Chuang, L., Berton, S., Bo, L., Geyong, M.: Network traffic prediction based on a new time series model. Int. J. Commun. Syst. 18(8), 711–729 (2005)CrossRefGoogle Scholar
  54. 54.
    Cormen, T .H., Leiserson, C .E., Rivest, R .L.: Introduction to Algorithms. MIT Press, Cambridge (2001)MATHGoogle Scholar
  55. 55.
    Feng, H., Shu, Y.: Study on network traffic prediction techniques. In: Proceedings of the International Conference on Wireless Communications, Networking and Mobile Computing, 2005, vol. 2, pp. 1041–1044. IEEE (2005)Google Scholar
  56. 56.
    Monahan, J.F.: Some algorithms for the conditional mean vector and covariance matrix. J. Stat. Softw. 16 (2005)Google Scholar
  57. 57.
    Krunz, M., Makowski, A.: Modeling video traffic using M/G/\(\infty\) input processes: a compromise between markovian and LRD models. IEEE J. Sel. Areas Commun. 16, 733–748 (1998)CrossRefGoogle Scholar
  58. 58.
    Garey, M.R., Johnson, D.S.: Computers and Intractability. Freeman, San Francisco (1979)MATHGoogle Scholar
  59. 59.
    Weingrtner, R., Brscher, G.B., Westphall, C.B.: Cloud resource management: a survey on forecasting and profiling models. J. Netw. Comput. Appl. 47, 99–106 (2015)CrossRefGoogle Scholar
  60. 60.
    Aceto, G., Botta, A., de Donato, W., Pescap, A.: Cloud monitoring: a survey. Comput. Netw. 57, 2093–2115 (2013)CrossRefGoogle Scholar
  61. 61.
    Drago, I., Mellia, M., Munafò, M.M., Sperotto, A., Sadre, R., Pras, A.: Inside dropbox: understanding personal cloud storage services. In: Proceedings of the 12th ACM SIGCOMM Conference on Internet Measurement (IMC’12), Berlin, Germany, pp. 481–494 (2012)Google Scholar
  62. 62.
    Benson, T., Akella, A., Maltz, D.A.: Network traffic characteristics of data centers in the wild. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement (IMC’10), pp. 267–280. ACM, New York, NY, USA (2010). doi:10.1145/1879141.1879175
  63. 63.
    Modi, C., Patel, D., Borisaniya, B., Patel, H., Patel, A., Rajarajan, M.: A survey of intrusion detection techniques in cloud. J. Netw. Comput. Appl. 36, 42–57 (2013)CrossRefGoogle Scholar
  64. 64.
    McLeod, A., Zhang, Y.: Faster ARMA maximum likelihood estimation. Comput. Stat. Data Anal. 52, 2166–2176 (2008)MathSciNetCrossRefMATHGoogle Scholar
  65. 65.
    Hyndman, R.J., Khandakar, Y.: Automatic time series forecasting: the forecast package for R. J. Stat. Softw. 26, 1–22 (2008)Google Scholar
  66. 66.
    R.C. Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, R.C. Team, Vienna (2012)Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Bruno L. Dalmazo
    • 1
  • João P. Vilela
    • 1
  • Marilia Curado
    • 1
  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal

Personalised recommendations