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Internet Performance Prediction Framework Based on PingER Dataset

  • Wei Zhang
  • Xiaofei Xing
  • Saqib Ali
  • Guojun Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11336)

Abstract

The Internet performance directly affects the scalability, reliability and availability of the online applications. Delay of a few millisecond may cause companies lose millions of dollars. Therefore, Internet measurements are carried out to capture the performance of the Internet links worldwide. Most of the Internet performance monitoring frameworks are active in nature i.e., they can only capture the real-time performance of the Internet links. Thus, these monitoring frameworks are unable to forecast the near future performance of the Internet links in a region. Such estimates are quite critical for the network administrators to carry out bandwidth extensive experiments between different sites, policy makers to suggest future upgrades to the Internet infrastructures or streaming service providers to enhance the quality of service to their customers. Therefore, we analyze different machine learning algorithms including Multiple Linear regression, Random Forest algorithm, Gradient Boosting, and eXtreme Gradient Boosting to predict the performance of the Internet links using PingER (Ping End-to-End Reporting) dataset for the countries like China, India and Japan. Our experimental results show that the Multiple Linear regression has improved Internet performance prediction accuracy compared with the other methods. Our work can be utilized by the Internet service providers, streaming service providers or policymakers for the design, deployment, and evaluation of next-generation Internet infrastructure.

Keywords

Multiple linear regression Internet performance Prediction PingER 

Notes

Acknowledgments

This work is supported in part by CERNET Innovation Project under Grant No. NGII20170102, Natural Science Foundation of China under Grant No. 61772007, 61632009, Guangdong Natural Science Foundation of China under Grant No. 2016A030313540, Guangzhou Science and Technology Program under Grant No. 201707010284.

References

  1. 1.
    Singla, A., Chandrasekaran, B., Godfrey, P.B., Maggs, B.: The Internet at the speed of light. In: Proceedings of the 13th ACM Workshop on Hot Topics in Networks - HotNets-XIII, pp. 1–7 (2014)Google Scholar
  2. 2.
    Ali, S., Cottrell, R.L., Nveed, A.: Pinger Malaysia-internet performance measuring project: A case study (No. SLAC-PUB-16462). SLAC National Accelerator Lab., Menlo Park, CA, United States (2016)Google Scholar
  3. 3.
    Samknows Homepage. https://www.samknows.com/. Accessed 30 May 2018
  4. 4.
    Sundaresan, S., Burnett, S., Feamster, N., de Donato, W.: BISmark: A testbed for deploying measurements and applications in broadband access networks. In: Proceedings 2014 USENIX Annual Technical Conference (USENIX ATC 2014), pp. 383–394 (2014)Google Scholar
  5. 5.
    Sánchez, M., Otto, J.: Dasu: Pushing Experiments to the internet’s edge. In: Proceedings of USENIX Association, pp. 487–499 (2013)Google Scholar
  6. 6.
    Sonntag, S., Manner, J., Schulte, L.: Netradar – Measuring the wireless world. In: Wireless Network Measurements, pp. 29–34 (2013)Google Scholar
  7. 7.
    Faggiani, A., Gregori, E., Lenzini, L., Luconi, V., Vecchio, A.: Smartphone-based crowdsourcing for network monitoring: opportunities, challenges, and a case study. IEEE Commun. Mag. 52, 106–113 (2014)CrossRefGoogle Scholar
  8. 8.
    Bajpai, V., Eravuchira, S.J., Schönwälder, J.: Lessons learned from using the RIPE atlas platform for measurement research. ACM SIGCOMM Comput. Commun. Rev. 45, 35–42 (2015)CrossRefGoogle Scholar
  9. 9.
    Hanemann, A., et al.: PerfSONAR: A service oriented architecture for multi-domain network monitoring. In: Benatallah, B., Casati, F., Traverso, P. (eds.) ICSOC 2005. LNCS, vol. 3826, pp. 241–254. Springer, Heidelberg (2005).  https://doi.org/10.1007/11596141_19CrossRefGoogle Scholar
  10. 10.
    Matthews, W., Coffrell, L.: The PingER project: active Internet performance monitoring for the HENP community. IEEE Commun. Mag. 38, 130–136 (2000)CrossRefGoogle Scholar
  11. 11.
    Paxson, V.: End-to-end Internet packet dynamics. IEEE/ACM Trans. Netw. 7, 277–292 (1999)CrossRefGoogle Scholar
  12. 12.
    Wu, C.L., Chau, K.W., Li, Y.S.: Methods to improve neural network performance in daily flows prediction. J. Hydrol. 372, 80–93 (2009)CrossRefGoogle Scholar
  13. 13.
    Zhou, D., Chen, S., Dong, S.: Network Traffic Prediction Based on ARFIMA Model. arXiv Prepr. arXiv1302.6324, vol. 9, pp. 106–111 (2013)Google Scholar
  14. 14.
    Shang, P., Li, X., Kamae, S.: Nonlinear analysis of traffic time series at different temporal scales. Phys. Lett. Sect. A Gen. At. Solid State Phys. 357, 314–318 (2006)zbMATHGoogle Scholar
  15. 15.
    Nury, A.H., Hasan, K., Alam, M.J.: Bin: Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh. J. King Saud Univ. Sci. 29, 47–61 (2017)CrossRefGoogle Scholar
  16. 16.
    Yin, H., Lin, C., Sebastien, B., Li, B., Min, G.: Network traffic prediction based on a new time series model. Int. J. Commun Syst 18, 711–729 (2005)CrossRefGoogle Scholar
  17. 17.
    Karunasinghe, D.S.K., Liong, S.Y.: Chaotic time series prediction with a global model: artificial neural network. J. Hydrol. 323, 92–105 (2006)CrossRefGoogle Scholar
  18. 18.
    Weiyong, Z., Guangli, F.: Network traffic combination forecasting based on encompassing tests and support vector machine. Comput. Eng. Appl. 15, 84–87 (2013)Google Scholar
  19. 19.
    Hongxing, C.: Network traffic prediction based on extreme learning machine and least square support vector machine. Comput. Eng. Appl. 51(24), 73–77 (2015)Google Scholar
  20. 20.
    Hammami, C., Jemili, I., Gazdar, A., Belghith, A.: Hybrid live P2P streaming protocol. Procedia Comput. Sci. 32, 158–165 (2014)CrossRefGoogle Scholar
  21. 21.
    Hodge, V.J., Austin, J.: A Survey of outlier detection methodoligies. Artif. Intell. Rev. 22, 85–126 (2004)CrossRefGoogle Scholar
  22. 22.
    Cui, F.: Study of traffic flow prediction based on BP neural network. In: 2010 2nd International Workshop on Intelligent Systems and Applications, pp. 1–4 (2010)Google Scholar
  23. 23.
    Breiman, L.: Random forest. Mach. Learn. 45, 5–32 (2001)CrossRefGoogle Scholar
  24. 24.
    Guelman, L.: Gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Syst. Appl. 39, 3659–3667 (2012)CrossRefGoogle Scholar
  25. 25.
    Parker, C., Fern, A., Tadepalli, P.: Gradient boosting for sequence alignment. In: Proceedings of the National Conference on Artificial Intelligence, vol. 21, no. 1, p. 452. (2006). AAAI Press, Menlo Park. MIT Press, Cambridge, London (1999)Google Scholar
  26. 26.
    Chen, T., He, T.: XGBoost: eXtreme Gradient Boosting. R Packag. version 0.4-2, pp. 1–4 (2015)Google Scholar
  27. 27.
    Chen, T., Guestrin, C.: XGboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)Google Scholar
  28. 28.
    Ali, S., Wang, G., Cottrell, R.L., Masood, S.: Internet performance analysis of south asian countries using end-to-end internet performance measurements. In: 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications, 2017 IEEE International Conference on Ubiquitous Computing and Communications, pp. 1319–1326 (2017)Google Scholar
  29. 29.
    Ali, S., Wang, G., Cottrell, R.L., Anwar, T.: Detecting anomalies from end-to-end internet performance measurements (PingER) using cluster based local outlier factor. In: 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications, 2017 IEEE International Conference on Ubiquitous Computing and Communications, pp. 982–989 (2017)Google Scholar
  30. 30.
    Mal, A., Sabitha, A.S., Bansal, A., White, B., Cottrell, L.: Analysis and clustering of PingER network data. In: Proceedings of 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence), pp. 268–273 (2016)Google Scholar
  31. 31.
    Ali, S., Wang, G., White, B., Cottrell, R.L.: A Blockchain-based decentralized data storage and access framework for PingER. In: 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/12th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), pp. 1303–1308. IEEE (2018)Google Scholar
  32. 32.
    Ali, S., Wang, G., Xing, X., Cottrell, R.L.: Substituting missing values in end-to-end Internet performance measurements using k-Nearest neighbors. In: 2018 IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, 16th International Conference on Pervasive Intelligence and Computing, 4th International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp. 919–926. IEEE (2018)Google Scholar
  33. 33.
    Internet Word Stats Homepage. https://www.Internetworldstats.com/stats3.htm. Accessed 11 June 2018
  34. 34.
    Guo, H., Wang, X., Gao, Z.: Uncertain linear regression model and it’s application. J. Intell. Manuf. 28, 559–564 (2017)CrossRefGoogle Scholar
  35. 35.
    Sun, H., Liu, H., Xiao, H., He, R., Ran, B.: Short term traffic forecasting using the local linear regression model. Transp. Res. Rec., 143–150 (2003)Google Scholar
  36. 36.
    Kumar, S., Gangwar, S.S.: Intuitionistic fuzzy time series: an approach for handling nondeterminism in time series forecasting. IEEE Trans. Fuzzy Syst. 24, 1270–1281 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Wei Zhang
    • 1
  • Xiaofei Xing
    • 1
  • Saqib Ali
    • 1
  • Guojun Wang
    • 1
  1. 1.School of Computer Science and TechnologyGuangzhou UniversityGuangzhouPeople’s Republic of China

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