Advertisement

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

  • Thieu Nguyen
  • Binh Minh NguyenEmail author
  • Giang Nguyen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Proactive auto-scaling Functional-link neural network Adaptive bacterial foraging optimization Multivariate time series data Cloud computing Google trace dataset 

Notes

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”.

References

  1. 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)CrossRefGoogle Scholar
  2. 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. 3.
    Hipel, K.W., McLeod, A.I.: Time Series Modelling of Water Resources and Environmental Systems, vol. 45. Elsevier, Amsterdam (1994)CrossRefGoogle Scholar
  4. 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)MathSciNetzbMATHGoogle Scholar
  5. 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. 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_36CrossRefGoogle Scholar
  7. 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. 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)CrossRefGoogle Scholar
  9. 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. 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)CrossRefGoogle Scholar
  11. 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. 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)CrossRefGoogle Scholar
  13. 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. 14.
    Pao, Y.H.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)zbMATHGoogle Scholar
  15. 15.
    Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22(3), 52–67 (2002)MathSciNetCrossRefGoogle Scholar
  16. 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. 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. 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. 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)CrossRefGoogle Scholar
  20. 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. 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)CrossRefGoogle Scholar
  22. 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. 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

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Information and Communication TechnologyHanoi University of Science and TechnologyHanoiVietnam
  2. 2.Institute of InformaticsSlovak Academy of SciencesBratislavaSlovakia

Personalised recommendations