Skip to main content
SpringerLink
Log in

A Forecasting Model for Data Center Bandwidth Utilization

  • Conference paper
  • First Online:
Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 (IntelliSys 2016)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 15))

Included in the following conference series:

  • 2013 Accesses

Abstract

Bandwidth optimization and its efficient utilization is more challenging in operating data centers. Our model can assist for proper usage of resource utilization and accommodate large scale of bursty data. In this paper we propose forecast model for Data Center Bandwidth Utilization system; a forecast model for data centers to predict and estimate proper bandwidth utilization in real-world situations. Based on self-learning procedures, the proposed forecasting model will optimize the traffic and predict bandwidth more efficiently. Our approach is based on Time Series and Vector Autoregression (VAR-Model) models, it optimizes the bandwidth traffic detecting and diagnosing the future based on historical data.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aceto, G., Botta, A., Pescapé, A., D’Arienzo, M.: Unified architecture for network measurement: the case of available bandwidth. J. Netw. Comput. Appl. 35(5), 1402–1414 (2012)

    Article  Google Scholar 

  2. Agung, I.G.N.: Time Series Data Analysis Using EViews. Wiley, Hoboken (2011)

    Google Scholar 

  3. Balman, M., Chaniotakisy, E., Shoshani, A., Sim, A.: A flexible reservation algorithm for advance network provisioning. In: 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–11. IEEE (2010)

    Google Scholar 

  4. Bilal, K., Khan, S.U., Zhang, L., Li, H., Hayat, K., Madani, S.A., Min-Allah, N., Wang, L., Chen, D., Iqbal, M., et al.: Quantitative comparisons of the state-of-the-art data center architectures. Concurrency Comput. Pract. Exp. 25(12), 1771–1783 (2013)

    Article  Google Scholar 

  5. Cisco, V.N.I.: The zettabyte era: trends and analysis. Cisco Visual Networking White Paper (2014)

    Google Scholar 

  6. Cortez, P., Rio, M., Rocha, M., Sousa, P.: Multi-scale internet traffic forecasting using neural networks and time series methods. Expert Syst. 29(2), 143–155 (2012)

    Google Scholar 

  7. Eswaradass, A., Sun, X.-H., Wu, M.: Network bandwidth predictor (NBP): a system for online network performance forecasting, cluster computing and the grid. In: Sixth IEEE International Symposium on CCGRID 2006, vol. 1, 4 pp. IEEE (2006)

    Google Scholar 

  8. Farrington, N., Porter, G., Radhakrishnan, S., Bazzaz, H.H., Subramanya, V., Fainman, Y., Papen, G., Vahdat, A.: Helios: a hybrid electrical/optical switch architecture for modular data centers. ACM SIGCOMM Comput. Commun. Rev. 41(4), 339–350 (2011)

    Google Scholar 

  9. Gardner Jr., E.S., McKenzie, E.D.: Forecasting trends in time series. Manage. Sci. 31(10), 1237–1246 (1985)

    Article  MATH  Google Scholar 

  10. Greenberg, A., Hamilton, J.R., Jain, N., Kandula, S., Kim, C., Lahiri, P., Maltz, D.A., Patel, P., Sengupta, S.: Vl2: a scalable and flexible data center network. Commun. ACM 54(3), 95–104 (2011)

    Article  Google Scholar 

  11. Griffiths, W.E., Hill, R.C., Lim, G.C.: Using EViews for Principles of Econometrics (2008)

    Google Scholar 

  12. Han, M.-S.: Dynamic bandwidth allocation with high utilization for XG-PON. In: 16th International Conference on Advanced Communication Technology, pp. 994–997. IEEE (2014)

    Google Scholar 

  13. Hiemstra, C., Jones, J.D.: Testing for linear and nonlinear granger causality in the stock price-volume relation. J. Finance 49(5), 1639–1664 (1994)

    Google Scholar 

  14. Autoregressive integrated moving average. https://en.wikipedia.org/

  15. Hu, K., Choi, J., Sim, A., Jiang, J.: Best predictive generalized linear mixed model with predictive lasso for high-speed network data analysis. Int. J. Stat. Prob. 4(2), 132 (2015)

    Article  Google Scholar 

  16. Ningning, H., Peter, S.: Evaluation and characterization of available bandwidth probing techniques. IEEE J. Sel. Areas Commun. 21(6), 879–894 (2003)

    Article  Google Scholar 

  17. Hyndman, R.J., Akram, M., Archibald, B.C.: The admissible parameter space for exponential smoothing models. Ann. Inst. Stat. Math. 60(2), 407–426 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  18. Hyndman, R.J., Khandakar, Y.: Automatic time series for forecasting: the forecast package for R. Technical report (2007)

    Google Scholar 

  19. Hyndman, R.J., Koehler, A.B., Snyder, R.D., Grose, S.: A state space framework for automatic forecasting using exponential smoothing methods. Int. J. Forecast. 18(3), 439–454 (2002)

    Article  Google Scholar 

  20. Hyndman, R.J., Kostenko, A.V.: Minimum sample size requirements for seasonal forecasting models. Foresight 6, 12–15 (2007)

    Google Scholar 

  21. Cisco visual networking index: forecast and methodology. 2014–2019 white paper, Cisco (2015)

    Google Scholar 

  22. Jain, M., Dovrolis, C.: End-to-End Available Bandwidth: Measurement Methodology, Dynamics, and Relation with TCP Throughput, vol. 32. ACM (2002)

    Google Scholar 

  23. Kandula, S., Sengupta, S., Greenberg, A., Patel, P., Chaiken, R.: The nature of data center traffic: measurements & analysis. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference, pp. 202–208. ACM (2009)

    Google Scholar 

  24. Kliazovich, D., Bouvry, P., Khan, S.U.: Greencloud: a packet-level simulator of energy-aware cloud computing data centers. J. Supercomput. 62(3), 1263–1283 (2012)

    Article  Google Scholar 

  25. Krithikaivasan, B., Zeng, Y., Deka, K., Medhi, D.: Arch-based traffic forecasting and dynamic bandwidth provisioning for periodically measured nonstationary traffic. IEEE/ACM Trans. Netw. (TON) 15(3), 683–696 (2007)

    Article  Google Scholar 

  26. Margulies, M., Egholm, M.: Genome sequencing in microfabricated high-density picolitre reactors. Nature 437(7057), 376–380 (2005)

    Google Scholar 

  27. Mirahmadi, M., Shami, A.: Traffic-prediction-assisted dynamic bandwidth assignment for hybrid optical wireless networks. Comput. Netw. 56(1), 244–259 (2012)

    Article  Google Scholar 

  28. Moussas, V.C., Daglis, M., Kolega, E.: Network traffic modeling and prediction using multiplicative seasonal arima models. In: Proceedings of the 1st International Conference on Experiments/Process/System Modeling/Simulation/Optimization, Athens, pp. 6–9 (2005)

    Google Scholar 

  29. Cisco visual networking: the zettabyte era-trends and analysis. Cisco white paper (2013)

    Google Scholar 

  30. Mysore, R.N., Pamboris, A., Farrington, N., Huang, N., Miri, P., Radhakrishnan, S., Subramanya, V., Vahdat, A.: Portland: a scalable fault-tolerant layer 2 data center network fabric. In: ACM SIGCOMM Computer Communication Review, vol. 39, pp. 39–50. ACM (2009)

    Google Scholar 

  31. Papagiannaki, K., Taft, N., Zhang, Z.-L., Diot, C.: Long-term forecasting of internet backbone traffic. IEEE Trans. Neural Netw. 16(5), 1110–1124 (2005)

    Article  Google Scholar 

  32. Qiao, Y., Skicewicz, J., Dinda, P.: An empirical study of the multiscale predictability of network traffic. In: 13th IEEE International Symposium on High performance Distributed Computing, Proceedings, pp. 66–76. IEEE (2004)

    Google Scholar 

  33. Ribeino, V., Baraniuk, R.R., Pathchimp, R.: Efficient available bandwidth estimation for network paths. In: PAM (2003)

    Google Scholar 

  34. Sang, A., Li, S.-Q.: A predictability analysis of network traffic. Comput. Netw. 39(4), 329–345 (2002)

    Article  Google Scholar 

  35. Strauss, J., Katabi, D., Kaashoek, F.: A measurement study of available bandwidth estimation tools. In: Proceedings of the 3rd ACM SIGCOMM Conference on Internet Measurement, pp. 39–44. ACM (2003)

    Google Scholar 

  36. Tukey, J.W.: Exploratory Data Analysis (1977)

    Google Scholar 

  37. Wang, G., Andersen, D.G., Kaminsky, M., Papagiannaki, K., Ng, T.S., Kozuch, M., Ryan, M.: c-Through: part-time optics in data centers. ACM SIGCOMM Comput. Commun. Rev. 40, 327–338. ACM(2010)

    Google Scholar 

  38. Yoo, W., Sim, A.: Network bandwidth utilization forecast model on high bandwidth networks. In: 2015 International Conference on Computing, Networking and Communications (ICNC), pp. 494–498. IEEE (2015)

    Google Scholar 

  39. Yu, Y., Aung, K.M.M., Tong, E.K.K., Foh, C.H.: Dynamic load balancing multipathing in data center ethernet. In: 2010 IEEE International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), pp. 403–406. IEEE (2010)

    Google Scholar 

  40. Zucchini, W., Nenadic, O.: Time series analysis with R-part I. Document de cours (2011)

    Google Scholar 

Download references

Acknowledgement

I take this opportunity to express gratitude to all unknown reviewers for their feedback and make me able to participate for this conference. This research was supported by Sukkur Institute of Business Administration, this prestigious institute allowed me to mentioned the name to acknowledge. I would like to express my sincere gratitude to my supervisor Prof. M-Tahar Kechadi, who is second author of this paper; this study is nothing with the exception of his continuous support and motivation. My sincere thanks to my ex-colleague Mr. Fahad Rahim Qasmi for providing the partial data and excess of data center.

Author information

Authors and Affiliations

  1. School of Computer Science, University College Dublin, Belfield, Dublin, Ireland

    Samar Raza Talpur & Tahar Kechadi

Authors
  1. Samar Raza Talpur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tahar Kechadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samar Raza Talpur .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Engineering, School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Talpur, S.R., Kechadi, T. (2018). A Forecasting Model for Data Center Bandwidth Utilization. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-56994-9_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-56994-9_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56993-2

  • Online ISBN: 978-3-319-56994-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation