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A Predictive Method for Workload Forecasting in the Cloud Environment

  • Yao-Chung Chang
  • Ruay-Shiung Chang
  • Feng-Wei Chuang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 260)

Abstract

Cloud computing provides powerful computing capabilities, and supplies users with a flexible pay mechanism, which makes the cloud more convenient. People are getting more and more usage of the cloud environment due to a steady increase of data. In order to improve the performance and energy saving of the cloud computing, the efficiency of resource allocation has become an important issue. In this study, a neural network model with learning algorithm is applied to predict the workload of the cloud server. The resource manager deployed on the cloud server provides the service of managing the jobs with a resource allocation algorithm. With this prediction mechanism, cloud service providers can forecast the following time workload of cloud servers in advance. The experimental results show that resources can be allocated efficiently and become load balanced by proposed mechanism. Therefore, the cloud server can avoid the problem of inadequate resources.

Keywords

Cloud computing Predictive workload Neural network Learning algorithm 

Notes

Acknowledgments

The authors would like to thank the National Science Council of the Republic of China, Taiwan for financially/partially supporting this research under Contract No. NSC101-2221-E-143-005-, NSC101-2221-E-259-003- and NSC101-2221-E-259-005-MY2.

References

  1. 1.
    Zhang Q, Cherkasova L et al (2007) A regression-based analytic model for dynamic resource provisioning of multi-tier applications. IEEE Int Conf Auton Comput (ICAC)Google Scholar
  2. 2.
    Tirando JM, Higuero D, Isaila F, Carretero J (2011) Predictive data grouping and placement for cloud-based elastic server infrastructures. IEEE/ACM international conference on cluster, cloud and grid computing, pp 285–294Google Scholar
  3. 3.
    Mell P, Grance T (2009) The NIST definition of cloud computing. Nat Inst Stand Technol 53(6):50Google Scholar
  4. 4.
    Zhang Z, Wang H, Xiao L, Ruan L (2011) A statistical based resource allocation scheme in cloud. Cloud and service computing (CSC), 2011 international conference, pp 266–273Google Scholar
  5. 5.
    Gallant S (1993) Neural network learning and expert systems. MIT Press, CambridgeMATHGoogle Scholar
  6. 6.
    Peterson C, Södeberg B (1993) Artificial neural networks. Modern heuristic techniques for combinatorial problems. In: Reeves CR (ed) Advanced topics in computer science, Oxford Scientific Publications, New York, pp 197–242Google Scholar
  7. 7.
    Abramson D, Buyya R, Giddy J (2002) A computational economy for grid computing and its implementation in the Nimrod-G resource broker. Future Gener Comput Syst 18(8):1061–1074CrossRefMATHGoogle Scholar
  8. 8.
    Picht SW (1994) Steepest descent algorithms for neural network controllers and filters. IEEE Trans Neural Networks 198–212Google Scholar
  9. 9.
    Haykin S (2008) Neural networks and learning machines: a comprehensive foundation, 3rd ed. Prentice HallGoogle Scholar
  10. 10.
  11. 11.
    Borthakur D (2009) The Hadoop distributed file system: architecture and design. http://hadoop.apache.org/common/docs/current/hdfs-design.html

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Yao-Chung Chang
    • 1
  • Ruay-Shiung Chang
    • 2
  • Feng-Wei Chuang
    • 2
  1. 1.Department of Computer Science and Information EngineeringNational Taitung UniversityTaitungTaiwan, Republic of China
  2. 2.Department of Computer Science and Information EngineeringNational Dong Hwa UniversityHualienTaiwan, Republic of China

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