[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

A water cycle optimized wavelet neural network algorithm for demand prediction in cloud computing

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Nowadays, using cloud computing services is becoming very popular among corporations and dependent users because of its flexibility and diverse facilities. So, demand for its infrastructures is increasing exponentially and with this huge growth of demands, it is getting more challenging for cloud providers to serve all the user requests in a way that quality of service is high enough and service level agreement is also met. Along years, researchers found out that for an optimized resource allocation which provides required user quality of service it is needed to know the amount of future workload in advance so resources can be prepared. There are many methods introduced for cloud workload prediction but excessive changes in the number of requests in cloud workloads cause a reduction in their prediction accuracy. To solve this problem, we have proposed a novel hybrid wavelet neural network method which can efficiently model excessive changes in workload and predict the upcoming requests. For training the proposed method accurately, we have used two heuristic algorithms, Artificial Immune System and Water Cycle Algorithm. The two mentioned heuristic algorithms are used for finding optimized parameters (such as bias and weight) for the wavelet neural network algorithm. The proposed wavelet neural network trained with heuristic algorithms is tested on real and standard cloud workloads. At last, the accuracy of the proposed method is thoroughly surveyed statistically and also compared with other state-of-the-art prediction methods. Simulation results show that our method improves MAPE error at least 10% in comparison to other rival prediction methods.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010)

    Article  Google Scholar 

  2. Rimal, B. P., Choi, E., Lumb, I.: A taxonomy and survey of cloud computing systems. In: INC, IMS and IDC, pp. 44–51 (2009)

  3. Rittinghouse, J.W., Ransome, J.F.: Cloud computing: implementation, management, and security. CRC Press, Boca Raton (2016)

    Google Scholar 

  4. Ardagna, D., et al.: Quality-of-service in cloud computing: modeling techniques and their applications. J. Internet Serv. Appl. 5(1), 11 (2014)

    Article  MathSciNet  Google Scholar 

  5. Rashidi, S., Sharifian, S.: A hybrid heuristic queue based algorithm for task assignment in mobile cloud. Future Gener. Comput. Syst. 68, 331–345 (2017)

    Article  Google Scholar 

  6. Singh, S., Chana, I.: QoS-aware autonomic resource management in cloud computing: a systematic review. ACM Comput. Surv. 48(3), 42 (2016)

    Google Scholar 

  7. Yadav, R., Zhang, W.: MeReg: managing energy-SLA tradeoff for green mobile cloud computing. Wirel. Commun. Mob. Comput. 20, 17 (2017). https://doi.org/10.1155/2017/6741972

    Article  Google Scholar 

  8. Barati, M., Sharifian, S.: A hybrid heuristic-based tuned support vector regression model for cloud load prediction. J. Supercomput. 71(11), 4235–4259 (2015)

    Article  Google Scholar 

  9. Amiri, M., Mohammad-Khanli, L.: Survey on prediction models of applications for resources provisioning in cloud. J. Netw. Comput. Appl. 82, 93–113 (2017)

    Article  Google Scholar 

  10. Jiang, Y., Perng, C.-S., Li, T., Chang, R.N.: Cloud analytics for capacity planning and instant VM provisioning. IEEE Trans. Netw. Serv. Manag. 10(3), 312–325 (2013)

    Article  Google Scholar 

  11. Yadav, R., et al.: MuMs: energy-aware VM selection scheme for cloud data center. In: Database and Expert Systems Applications (DEXA), 2017 28th International Workshop on IEEE (2017)

  12. Calheiros, R.N., et al.: Workload prediction using ARIMA model and its impact on cloud applications’ QoS. IEEE Trans. Cloud Comput. 3(4), 449–458 (2015)

    Article  Google Scholar 

  13. Benmouiza, K., Cheknane, A.: Small-scale solar radiation forecasting using ARMA and nonlinear autoregressive neural network models. Theoret. Appl. Climatol. 124(3-4), 945–958 (2016)

    Article  Google Scholar 

  14. Yang, Z., Ce, L., Lian, L.: Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods. Appl. Energy 190, 291–305 (2017)

    Article  Google Scholar 

  15. Nourikhah, H., Akbari, M.K., Kalantari, M.: Modeling and predicting measured response time of cloud-based web services using long-memory time series. J. Supercomput. 71(2), 673–696 (2015)

    Article  Google Scholar 

  16. Deng, S., et al.: Hybrid method of multiple kernel learning and genetic algorithm for forecasting short-term foreign exchange rates. Comput. Econ. 45(1), 49–89 (2015)

    Article  Google Scholar 

  17. Messias, V.R., et al.: Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure. Neural Comput. Appl. 27(8), 2383–2406 (2016)

    Article  Google Scholar 

  18. Papageorgiou, E.I., Poczęta, K.: A two-stage model for time series prediction based on fuzzy cognitive maps and neural networks. Neurocomputing 232, 113–121 (2017)

    Article  Google Scholar 

  19. Hussain, A.J., et al.: Regularized dynamic self-organized neural network inspired by the immune algorithm for financial time series prediction. Neurocomputing 188, 23–30 (2016)

    Article  Google Scholar 

  20. Jiang, Y., et al.: Cloud analytics for capacity planning and instant VM provisioning.”. IEEE Trans. Netw. Serv. Manag. 10(3), 312–325 (2013)

    Article  Google Scholar 

  21. Gholami, R., Fakhari, N.: Support vector machine: principles, parameters, and applications. Handb. Neural Comput. (2017). https://doi.org/10.1016/B978-0-12-811318-9.00027-2

    Article  Google Scholar 

  22. Sharma, V., et al.: Short term solar irradiance forecasting using a mixed wavelet neural network. Renew. Energy 90, 481–492 (2016)

    Article  Google Scholar 

  23. Lutfy, O.: Wavelet neural network model reference adaptive control trained by a modified artificial immune algorithm to control nonlinear systems. Arab. J. Sci. Eng. 39(6), 4737–4751 (2014)

    Article  Google Scholar 

  24. Duan, F., et al.: sEMG-based identification of hand motion commands using wavelet neural network combined with discrete wavelet transform. IEEE Trans. Ind. Electron. 63(3), 1923–1934 (2016)

    Article  Google Scholar 

  25. Suryanarayana, Ch., et al.: An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India. Neurocomputing 145, 324–335 (2014)

    Article  Google Scholar 

  26. Eskandar, H., et al.: Water cycle algorithm: a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput. Struct. 110, 151–166 (2012)

    Article  Google Scholar 

  27. Yao, G., et al.: An immune system-inspired rescheduling algorithm for workflow in Cloud systems. Knowl.-Based Syst. 99, 39–50 (2016)

    Article  Google Scholar 

  28. Jordehi, A.R.: A chaotic artificial immune system optimisation algorithm for solving global continuous optimisation problems. Neural Comput. Appl. 26(4), 827–833 (2015)

    Article  Google Scholar 

  29. Karim, F., Majumdar, S., Darabi, H., Chen, S.: LSTM fully convolutional networks for time series classification. IEEE Access 6, 1662–1669 (2018)

    Article  Google Scholar 

  30. Guo, G., Wang, C., Chen, J., Ge, P., Chen, W.: Who is answering whom? Finding “Reply-To” relations in group chats with deep bidirectional LSTM networks. Clust. Comput. 1, 12 (2018). https://doi.org/10.1007/s10586-018-2031-4

    Article  Google Scholar 

  31. Song, B., et al.: Host load prediction with long short-term memory in cloud computing. J. Supercomput. 74, 6554 (2018). https://doi.org/10.1007/s11227-017-2044-4

    Article  Google Scholar 

  32. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  33. http://ita.ee.lbl.gov/html/contrib/

  34. Messias, V. R., Estrella, J. C., Ehlers, R.: Efficient resource allocation for web applications hosted in the cloud by means of weighted multi-objective linear programming. In: Proceedings of the 21st Brazilian Symposium on Multimedia and the Web. ACM (2015)

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Amirkabir University of Technology, 15914, Tehran, Iran

    Sima Jeddi & Saeed Sharifian

Authors
  1. Sima Jeddi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saeed Sharifian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saeed Sharifian.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jeddi, S., Sharifian, S. A water cycle optimized wavelet neural network algorithm for demand prediction in cloud computing. Cluster Comput 22, 1397–1412 (2019). https://doi.org/10.1007/s10586-019-02916-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-019-02916-2

Keywords

Search

Navigation