Advertisement

PANP-GM: A Periodic Adaptive Neighbor Workload Prediction Model Based on Grey Forecasting for Cloud Resource Provisioning

  • Yazhou HuEmail author
  • Bo Deng
  • Fuyang Peng
  • Dongxia Wang
  • Yu Yang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 201)

Abstract

Cloud computing platforms provide on-demand service to meet users’ need by adding or removing cloud resources dynamically. The cloud resource provisioning is often based on the feedback model, which causes time delay and resource wasters. Workload prediction methods can make the resource provisioning more instantaneous and reduce resource and power consumption, to meet service level objectives (SLOs) and improve quality of service (QoS) of cloud platform. In this paper, we propose a periodic adaptive neighbor workload prediction model based on grey forecasting (PANP-GM) for cloud resource provisioning. Firstly, the model analyzes the growth rate and evaluates the periodicity of workload. Secondly, this model uses the growth rate of previous neighbor periodicity to predicate the trend of upcoming workload. To adapt to dynamic changes and emergencies, the grey forecasting model is applied for automatic error correction and improving prediction accuracy. Experimental results demonstrate that PANP-GM can achieve better resource prediction accuracy than basic and general approaches. Furthermore, this model can effectively improve the QoS of cloud platform and reduce SLO violations.

Keywords

Workload prediction Periodicity Grey forecasting model Resource provisioning 

Notes

Acknowledgments

This work is supported by the National High-Technology Research and Development Program of China (863 Program) (No. 2013AA01A215) and the National Natural Science Foundation of China (No. 61271252).

References

  1. 1.
    Jiang, Y., Perng, C., Li, T., et al.: Asap: a self-adaptive prediction system for instant cloud resource demand provisioning. In: 2011 IEEE 11th International Conference on Data Mining (ICDM), pp. 1104–1109. IEEE (2011)Google Scholar
  2. 2.
    Hoffmann, G., Trivedi, K.S., Malek, M.: A best practice guide to resource predicting for computing systems. IEEE Trans. Reliab. 56(4), 615–628 (2007)CrossRefGoogle Scholar
  3. 3.
    Almeida Morais, F.J., Vilar Brasileiro, F., Vigolvino Lopes, R., et al.: Autoflex: service agnostic auto-scaling framework for iaas deployment models. In: 2013 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 42–49. IEEE (2013)Google Scholar
  4. 4.
    Khan, A., Yan, X., Tao, S., et al.: Workload characterization and prediction in the cloud: a multiple time series approach. In: 2012 IEEE Network Operations and Management Symposium (NOMS), pp. 1287–1294. IEEE (2012)Google Scholar
  5. 5.
    Roy, N., Dubey, A., Gokhale, A.: Efficient autoscaling in the cloud using predictive models for workload predicting. In: 2011 IEEE International Conference on Cloud Computing (CLOUD), pp. 500–507. IEEE (2011)Google Scholar
  6. 6.
    Reig, G., Guitart, J.: On the anticipation of resource demands to fulfill the QoS of saas web applications. In: Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing, pp. 147–154. IEEE Computer Society (2012)Google Scholar
  7. 7.
    Xu, W., Zhu, X., Singhal, S., et al.: Predictive control for dynamic resource allocation in enterprise data centers. In: 10th IEEE/IFIP Network Operations and Management Symposium, NOMS 2006, pp. 115–126. IEEE (2006)Google Scholar
  8. 8.
    Bankole, A.A., Ajila, S.A.: Cloud client prediction models for cloud resource provisioning in a multitier web application environment. In: 2013 IEEE 7th International Symposium on Service Oriented System Engineering (SOSE), pp. 156–161. IEEE (2013)Google Scholar
  9. 9.
    Imam, M.T., Miskhat, S.F., Rahman, R.M., et al.: Neural network and regression based processor load prediction for efficient scaling of Grid and Cloud resources. In: 2011 14th International Conference on Computer and Information Technology (ICCIT), pp. 333–338. IEEE (2011)Google Scholar
  10. 10.
    Islam, S., Keung, J., Lee, K., et al.: Empirical prediction models for adaptive resource provisioning in the cloud. Future Gener. Comput. Syst. 28(1), 155–162 (2012)CrossRefGoogle Scholar
  11. 11.
    Calheiros, R.N., Ranjan, R., Buyya, R.: Virtual machine provisioning based on analytical performance and QoS in cloud computing environments. In: 2011 International Conference on Parallel Processing (ICPP), pp. 295–304. IEEE (2011)Google Scholar
  12. 12.
    Jheng, J.J., Tseng, F.H., Chao, H.C., et al.: A novel VM workload prediction using Grey Predicting model in cloud data center. In: 2014 International Conference on Information Networking (ICOIN), pp. 40–45. IEEE (2014)Google Scholar
  13. 13.
    Saripalli, P., Kiran, G.V.R., Shankar, R.R., et al.: Load prediction and hot spot detection models for autonomic cloud computing. In: 2011 Fourth IEEE International Conference on Utility and Cloud Computing (UCC), pp. 397–402. IEEE (2011)Google Scholar
  14. 14.
    Julong, D.: Introduction to grey system theory. J. Grey Syst. 1(1), 1–24 (1989)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Wang, Y., Gao, X., Wu, S., et al.: Periodicity detection method of periodic pattern mining in time series. Comput. Eng. 22, 014 (2009)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Yazhou Hu
    • 1
    Email author
  • Bo Deng
    • 1
  • Fuyang Peng
    • 1
  • Dongxia Wang
    • 2
  • Yu Yang
    • 1
  1. 1.Beijing Institute of System EngineeringBeijingChina
  2. 2.National Key Laboratory of Science and Technology on Information System SecurityBeijingChina

Personalised recommendations