Survey of Machine Learning Algorithms For Dynamic Resource Pricing In Cloud

Authors

  • Meetu Kandpal  MCA, Gujarat Technological University, Ahmedabad, Gujarat, India
  • Dr. Kalyani Patel  MSc(IT), Gujarat University, Ahmedabad, Gujarat , India

Keywords:

Cloud Computing, Cloud Pricing, Machine Learning Algorithms

Abstract

The paper provides insights of various machines learning algorithm that could be helpful in deriving the dynamic pricing of resources in cloud. Currently machine learning has impact on many IT and non IT sectors. At the same time because of great change in computing from on premise to cloud computing many big companies has opted cloud computing in which resources are provided on demand basis via internet. On the basis of resource usage machine learning algorithm help to predict the future demand and also help in deciding the price of resource at the time of request (spot request).

References

  1. https://aws.amazon.com/pricing/
  2. https://aws.amazon.com/ec2/pricing/
  3. Al-Roomi, May, et al. "Cloud computing pricing models: a survey." International Journal of Grid & Distributed Computing 6.5 (2013): 93-106.
  4. https://yourfreetemplates.com/free-machine-learning-diagram/
  5. Montgomery, Douglas C., Elizabeth A. Peck, and G.
  6. Geoffrey Vining. Introduction to linear regression analysis. Vol. 821. John Wiley & Sons, 2012.
  7. Woodside, Arch G. "Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory." (2013): 463-472.
  8. Jin, Chen, Luo De-Lin, and Mu Fen-Xiang. "An improved ID3 decision tree algorithm." Computer Science & Education, 2009. ICCSE'09. 4th International Conference on. IEEE, 2009.
  9. Wu, Xindong, et al. "Top 10 algorithms in data mining." Knowledge and information systems 14.1 (2008): 1-37.
  10. Picatoste Ruilope, Álvaro. Energy demand forecasting in smart buildings. MS thesis. Universitat Politècnica de Catalunya, 2017.
  11. J. A. Hartigan and M. A. Wong Journal of the Royal Statistical Society. Series C (Applied Statistics) Vol. 28, No. 1 (1979), pp. 100-108
  12. Tsochantaridis, Ioannis, et al. "Support vector machine learning for interdependent and structured output spaces." Proceedings of the twenty-first international conference on Machine learning. ACM, 2004.
  13. Tumilaar, Kezia, Yohanes Langi, and Altien Rindengan. "Hidden Markov Model." de CARTESIAN 4.1 (2015): 86-94.
  14. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444.
  15. Domingos, Pedro. "A few useful things to know about machine learning." Communications of the ACM 55.10 (2012): 78-87.
  16. Caruana, Rich, and Alexandru Niculescu-Mizil. "An empirical comparison of supervised learning algorithms." Proceedings of the 23rd international conference on Machine learning. ACM, 2006.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2018-01-20

Issue

Volume 4, Issue 2, January-February-2018

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Meetu Kandpal, Dr. Kalyani Patel, " Survey of Machine Learning Algorithms For Dynamic Resource Pricing In Cloud, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 2, pp.93-97, January-February-2018.