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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
Agung, I.G.N.: Time Series Data Analysis Using EViews. Wiley, Hoboken (2011)
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)
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)
Cisco, V.N.I.: The zettabyte era: trends and analysis. Cisco Visual Networking White Paper (2014)
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)
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)
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)
Gardner Jr., E.S., McKenzie, E.D.: Forecasting trends in time series. Manage. Sci. 31(10), 1237–1246 (1985)
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)
Griffiths, W.E., Hill, R.C., Lim, G.C.: Using EViews for Principles of Econometrics (2008)
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)
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)
Autoregressive integrated moving average. https://en.wikipedia.org/
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)
Ningning, H., Peter, S.: Evaluation and characterization of available bandwidth probing techniques. IEEE J. Sel. Areas Commun. 21(6), 879–894 (2003)
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)
Hyndman, R.J., Khandakar, Y.: Automatic time series for forecasting: the forecast package for R. Technical report (2007)
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)
Hyndman, R.J., Kostenko, A.V.: Minimum sample size requirements for seasonal forecasting models. Foresight 6, 12–15 (2007)
Cisco visual networking index: forecast and methodology. 2014–2019 white paper, Cisco (2015)
Jain, M., Dovrolis, C.: End-to-End Available Bandwidth: Measurement Methodology, Dynamics, and Relation with TCP Throughput, vol. 32. ACM (2002)
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)
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)
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)
Margulies, M., Egholm, M.: Genome sequencing in microfabricated high-density picolitre reactors. Nature 437(7057), 376–380 (2005)
Mirahmadi, M., Shami, A.: Traffic-prediction-assisted dynamic bandwidth assignment for hybrid optical wireless networks. Comput. Netw. 56(1), 244–259 (2012)
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)
Cisco visual networking: the zettabyte era-trends and analysis. Cisco white paper (2013)
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)
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)
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)
Ribeino, V., Baraniuk, R.R., Pathchimp, R.: Efficient available bandwidth estimation for network paths. In: PAM (2003)
Sang, A., Li, S.-Q.: A predictability analysis of network traffic. Comput. Netw. 39(4), 329–345 (2002)
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)
Tukey, J.W.: Exploratory Data Analysis (1977)
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)
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)
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)
Zucchini, W., Nenadic, O.: Time series analysis with R-part I. Document de cours (2011)
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
Corresponding author
Editor information
Editors and Affiliations
Rights 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)