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International Conference on Theory and Applications of Models of Computation

TAMC 2019: Theory and Applications of Models of Computation pp 501–517Cite as

Building Resource Auto-scaler with Functional-Link Neural Network and Adaptive Bacterial Foraging Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11436))

Abstract

In this paper, we present a novel intelligent proactive auto-scaling solution for cloud resource provisioning systems. The solution composes of an improvement variant of functional-link neural network and adaptive bacterial foraging optimization with life-cycle and social learning for proactive resource utilization forecasting as a part of our auto-scaler module. We also propose several mechanisms for processing simultaneously different resource metrics for the system. This enables our auto-scaler to explore hidden relationships between various metrics and thus help make more realistic for scaling decisions. In our system, a decision module is developed based on the cloud Service-Level Agreement (SLA) violation evaluation. We use Google trace dataset to evaluate the proposed solution well as the decision module introduced in this work. The gained experiment results demonstrate that our system is feasible to work in real situations with good performance.

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References

  1. Ali, E., Abd-Elazim, S.: Bacteria foraging optimization algorithm based load frequency controller for interconnected power system. Int. J. Electr. Power Energy Syst. 33(3), 633–638 (2011)

    Article  Google Scholar 

  2. Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUS). arXiv preprint arXiv:1511.07289 (2015)

  3. Hipel, K.W., McLeod, A.I.: Time Series Modelling of Water Resources and Environmental Systems, vol. 45. Elsevier, Amsterdam (1994)

    Book  Google Scholar 

  4. Hluchỳ, L., Nguyen, G., Astaloš, J., Tran, V., Šipková, V., Nguyen, B.M.: Effective computation resilience in high performance and distributed environments. Comput. Inform. 35(6), 1386–1415 (2017)

    MathSciNet  MATH  Google Scholar 

  5. Khandelwal, I., Satija, U., Adhikari, R.: Forecasting seasonal time series with functional link artificial neural network. In: 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 725–729. IEEE (2015)

    Google Scholar 

  6. Kim, D.H., Cho, J.H.: Adaptive tuning of PID controller for multivariable system using bacterial foraging based optimization. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds.) AWIC 2005. LNCS (LNAI), vol. 3528, pp. 231–235. Springer, Heidelberg (2005). https://doi.org/10.1007/11495772_36

    Chapter  Google Scholar 

  7. Lorido-Botrán, T., Miguel-Alonso, J., Lozano, J.A.: Auto-scaling techniques for elastic applications in cloud environments. Technical report EHU-KAT-IK-09 12, 2012, Department of Computer Architecture and Technology, University of Basque Country (2012)

    Google Scholar 

  8. Majhi, B., Rout, M., Majhi, R., Panda, G., Fleming, P.J.: New robust forecasting models for exchange rates prediction. Expert Syst. Appl. 39(16), 12658–12670 (2012)

    Article  Google Scholar 

  9. Majhi, R., Panda, G., Sahoo, G., Dash, P.K., Das, D.P.: Stock market prediction of S&P 500 and DJIA using bacterial foraging optimization technique. In: IEEE Congress on 2007 Evolutionary Computation, CEC 2007, pp. 2569–2575. IEEE (2007)

    Google Scholar 

  10. Majhi, R., Panda, G., Sahoo, G.: Development and performance evaluation of FLANN based model for forecasting of stock markets. Expert syst. Appl. 36(3), 6800–6808 (2009)

    Article  Google Scholar 

  11. Netto, M.A., Cardonha, C., Cunha, R.L., Assunçao, M.D.: Evaluating auto-scaling strategies for cloud computing environments. In: 2014 IEEE 22nd International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), pp. 187–196. IEEE (2014)

    Google Scholar 

  12. Nguyen, B.M., Tran, D., Nguyen, G.: Enhancing service capability with multiple finite capacity server queues in cloud data centers. Clust. Comput. 19(4), 1747–1767 (2016)

    Article  Google Scholar 

  13. Nguyen, T., Tran, N., Nguyen, B.M., Nguyen, G.: A resource usage prediction system using functional-link and genetic algorithm neural network for multivariate cloud metrics. In: 2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA), pp. 49–56. IEEE (2018)

    Google Scholar 

  14. Pao, Y.H.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)

    MATH  Google Scholar 

  15. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22(3), 52–67 (2002)

    Article  MathSciNet  Google Scholar 

  16. Reig, G., Alonso, J., Guitart, J.: Prediction of job resource requirements for deadline schedulers to manage high-level SLAs on the cloud. In: 2010 9th IEEE International Symposium on Network Computing and Applications (NCA), pp. 162–167. IEEE (2010)

    Google Scholar 

  17. Reiss, C., Tumanov, A., Ganger, G.R., Katz, R.H., Kozuch, M.A.: Heterogeneity and dynamicity of clouds at scale: Google trace analysis. In: Proceedings of the Third ACM Symposium on Cloud Computing, p. 7. ACM (2012)

    Google Scholar 

  18. Reiss, C., Wilkes, J., Hellerstein, J.L.: Google cluster-usage traces: format + schema. White Paper, pp. 1–14. Google Inc. (2011)

    Google Scholar 

  19. Sahoo, D.M., Chakraverty, S.: Functional link neural network learning for response prediction of tall shear buildings with respect to earthquake data. IEEE Trans. Syst. Man Cybern. Syst. 48(1), 1–10 (2018)

    Article  Google Scholar 

  20. Souza, A.A.D., Netto, M.A.: Using application data for sla-aware auto-scaling in cloud environments. In: 2015 IEEE 23rd International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), pp. 252–255. IEEE (2015)

    Google Scholar 

  21. Tran, D., Tran, N., Nguyen, G., Nguyen, B.M.: A proactive cloud scaling model based on fuzzy time series and SLA awareness. Procedia Comput. Sci. 108, 365–374 (2017)

    Article  Google Scholar 

  22. Vazquez, C., Krishnan, R., John, E.: Time series forecasting of cloud data center workloads for dynamic resource provisioning. JoWUA 6(3), 87–110 (2015)

    Google Scholar 

  23. Yan, X., Zhu, Y., Zhang, H., Chen, H., Niu, B.: An adaptive bacterial foraging optimization algorithm with lifecycle and social learning. Discrete Dyn. Nat. Soc. 2012, 20 pp. (2012)

    Google Scholar 

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Acknowledgements

This research is supported by Vietnamese MOETs project “Research on developing software framework to integrate IoT gateways for fog computing deployed on multi-cloud environment” No. B2017-BKA-32, Slovak APVV-17-0619 “Urgent Computing for Exascale Data”, and EU H2020-777536 EOSC-hub “Integrating and managing services for the European Open Science Cloud”.

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Authors and Affiliations

  1. School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam

    Thieu Nguyen & Binh Minh Nguyen

  2. Institute of Informatics, Slovak Academy of Sciences, Bratislava, Slovakia

    Giang Nguyen

Authors
  1. Thieu Nguyen
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  2. Binh Minh Nguyen
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  3. Giang Nguyen
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Corresponding author

Correspondence to Binh Minh Nguyen .

Editor information

Editors and Affiliations

  1. Anna University, Chennai, India

    T.V. Gopal

  2. Waseda University, Kitakyushu, Japan

    Junzo Watada

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Nguyen, T., Nguyen, B.M., Nguyen, G. (2019). Building Resource Auto-scaler with Functional-Link Neural Network and Adaptive Bacterial Foraging Optimization. In: Gopal, T., Watada, J. (eds) Theory and Applications of Models of Computation. TAMC 2019. Lecture Notes in Computer Science(), vol 11436. Springer, Cham. https://doi.org/10.1007/978-3-030-14812-6_31

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  • DOI: https://doi.org/10.1007/978-3-030-14812-6_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14811-9

  • Online ISBN: 978-3-030-14812-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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