Skip to main content
SpringerLink
Log in

Performance Analysis of Network Traffic Predictors in the Cloud

  • Published:
Journal of Network and Systems Management Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Mell, P., Grance, T.: The NIST Definition of Cloud Computing. National Institute of Standards and Technology, Gaithersburg (2011)

    Book  Google Scholar 

  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)

    Article  Google Scholar 

  3. Dainotti, A., Pescape, A., Claffy, K.: Issues and future directions in traffic classification. IEEE Netw. 26, 35–40 (2012)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  6. Sang, A., Li, S.-Q.: A predictability analysis of network traffic. Comput. Netw. 39, 329–345 (2002)

    Article  Google Scholar 

  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)

  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)

  9. Salah, K., Elbadawi, K., Boutaba, R.: An analytical model for estimating cloud resources of elastic services. J. Netw. Syst. Manag. 1–24 (2015)

  10. Ballani, H., Costa, P., Karagiannis, T., Rowstron, A.I.: Towards predictable datacenter networks. In: SIGCOMM, vol. 11, pp. 242–253 (2011)

  11. Vieira, K., Schulter, A., Westphall, C., Westphall, C.: Intrusion detection for grid and cloud computing. IT Prof. 12, 38–43 (2010)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  15. Buyya, R., Broberg, J., Goscinski, A .M.: Cloud Computing: Principles and Paradigms. Wiley, New York (2010)

    Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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. 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. 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

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  21. Whaiduzzaman, M., Sookhak, M., Gani, A., Buyya, R.: A survey on vehicular cloud computing. J. Netw. Comput. Appl. 40, 325–344 (2014)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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)

  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. Nguyen, T., Armitage, G.: A survey of techniques for internet traffic classification using machine learning. IEEE Commun. Surv. Tutor. 10, 56–76 (2008)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Auld, T., Moore, A., Gull, S.: Bayesian neural networks for internet traffic classification. IEEE Trans. Neural Netw. 18, 223–239 (2007)

    Article  Google Scholar 

  31. Bermolen, P., Rossi, D.: support vector regression for link load prediction. Comput. Netw. 53, 191–201 (2009). (QoS aspects in next-generation networks)

    Article  Google Scholar 

  32. Freedman, D.A.: Statistical Models: Theory and Practice. Cambridge University Press, Cambridge (2009)

    Book  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  34. Song, H., Li, G.: Tourism demand modelling and forecastinga review of recent research. Tour. Manag. 29, 203–220 (2008)

    Article  Google Scholar 

  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. Zhao, H.: Multiscale analysis and prediction of network traffic. In: IEEE 28th International on Performance Computing and Communications Conference (IPCCC), pp. 388–393 (2009)

  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)

    Article  MATH  Google Scholar 

  38. Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput. 9, 3–12 (2005)

    Article  Google Scholar 

  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. 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. Klinker, F.: Exponential moving average versus moving exponential average. Math. Semesterber. 58, 97–107 (2011)

    Article  MATH  Google Scholar 

  42. Wilamowski, B.: Neural network architectures and learning algorithms. IEEE Ind. Electron. Mag. 3, 56–63 (2009)

    Article  Google Scholar 

  43. Akesson, B.M., Toivonen, H.T.: A neural network model predictive controller. J. Process Control 16, 937–946 (2006)

    Article  Google Scholar 

  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)

  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. Chunlin, L., Layuan, L.: Multi-layer resource management in cloud computing. J. Netw. Syst. Manag. 22, 100–120 (2014)

    Article  Google Scholar 

  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. Gardiner, C.W., et al.: Handbook of Stochastic Methods, vol. 3. Springer, Berlin (1985)

    Google Scholar 

  49. Wei, W .W.-S.: Time Series Analysis. Addison-Wesley publ, Reading (1994)

    Google Scholar 

  50. Joshi, M., Hadi, T.H.: A Review of Network Traffic Analysis and Prediction Techniques (2015). arXiv:1507.05722

  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. Makridakis, S., Wheelwright, S.C., Hyndman, R.J.: Forecasting Methods and Applications. Wiley, New York (2008)

    Google Scholar 

  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)

    Article  Google Scholar 

  54. Cormen, T .H., Leiserson, C .E., Rivest, R .L.: Introduction to Algorithms. MIT Press, Cambridge (2001)

    MATH  Google Scholar 

  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)

  56. Monahan, J.F.: Some algorithms for the conditional mean vector and covariance matrix. J. Stat. Softw. 16 (2005)

  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)

    Article  Google Scholar 

  58. Garey, M.R., Johnson, D.S.: Computers and Intractability. Freeman, San Francisco (1979)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  60. Aceto, G., Botta, A., de Donato, W., Pescap, A.: Cloud monitoring: a survey. Comput. Netw. 57, 2093–2115 (2013)

    Article  Google Scholar 

  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)

  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. 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)

    Article  Google Scholar 

  64. McLeod, A., Zhang, Y.: Faster ARMA maximum likelihood estimation. Comput. Stat. Data Anal. 52, 2166–2176 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  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. R.C. Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, R.C. Team, Vienna (2012)

    Google Scholar 

Download references

Acknowledgments

This work was funded by CAPES and CNPq (Brazil) through the Ciência sem Fronteiras Program/2016.

Author information

Authors and Affiliations

  1. CISUC, Department of Informatics Engineering, University of Coimbra, Pólo II - Pinhal de Marrocos, Coimbra, Portugal

    Bruno L. Dalmazo, João P. Vilela & Marilia Curado

Authors
  1. Bruno L. Dalmazo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. João P. Vilela
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marilia Curado
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bruno L. Dalmazo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dalmazo, B.L., Vilela, J.P. & Curado, M. Performance Analysis of Network Traffic Predictors in the Cloud. J Netw Syst Manage 25, 290–320 (2017). https://doi.org/10.1007/s10922-016-9392-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10922-016-9392-x

Keywords

Search

Navigation