SPOT PRICE PREDICTION FOR CLOUD COMPUTINGUSING NEURAL NETWORKS

Authors

  • Volodymyr Turchenko
  • Vladyslav Shults
  • Iryna Turchenko
  • Richard M. Wallace
  • Mehdi Sheikhalishahi
  • Jose Luis Vazquez-Poletti
  • Lucio Grandinetti

DOI:

https://doi.org/10.47839/ijc.12.4.615

Keywords:

Spot Market, Cloud Computing, Resource Management, Neural Networks, Prediction.

Abstract

Advances in service-oriented architectures, virtualization, high-speed networks, and cloud computing has resulted in attractive pay-as-you-go services. Job scheduling on such systems results in commodity bidding for computing time. Amazon institutionalizes this bidding for its Elastic Cloud Computing (EC2) environment. Similar bidding methods exist for other cloud-computing vendors as well as multi–cloud and cluster computing brokers such as SpotCloud. Commodity bidding for computing has resulted in complex spot price models that have ad-hoc strategies to provide demand for excess capacity. In this paper we will discuss vendors who provide spot pricing and bidding and present the predictive models for future short-term and middle-term spot price prediction based on neural networks giving users a high confidence on future prices aiding bidding on commodity computing.

References

Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. H. Katz, A. Konwinski, G. Lee, D. A. Patterson, A. Rabkin, I. Stoica, and M. Zaharia Above the clouds: A berkeley view of cloud computing, EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2009-28, Feb 2009. [Online]. Available: http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.html.

B. N. Chun, P. Buonadonna, A. Auyoung, C. Ng, D. C. Parkes, J. Shneidman, A. C. Snoeren, and A. Vahdat, Mirage: A microeconomic resource allocation system for sensor net testbeds, in Proceedings of the 2nd IEEE Workshop on Embedded Networked Sensors, 2005.

C. Weng, M. Li, X. Lu, and Q. Deng, An economic-based resource management framework in the grid context, in Proceedings of the IEEE International Symposium on Cluster Computing and the CCGrid’2005, Vol. 1, 2005, pp. 542–549.

P. Doulai and W. Cahill, Short-term price forecasting in electric energy market, in Proceedings of the International Power Engineering Conference, 17-19 May 2001, Singapore, Vol. 21, No. 2, 2001, pp. 29–29.

T. Lora, J. C. R. Santos, J. R. Santos, J. L. M. Ramos, and A. G. Exposito, Electricity market price forecasting: Neural networks versus weighted-distance k nearest neighbours, in Proceedings of the 13th International Conference on Database and Expert Systems Applications, ser. DEXA’02, Springer-Verlag, London, UK, 2002, pp. 321–330.

B. Javadi, R. K. Thulasiram, and R. Buyya, Statistical modeling of spot instance prices in public cloud environments, in Proceedings of the UCC, IEEE Computer Society, 2011, pp. 219–228.

Amazon ec2 spot price history, 2012, [Online]. http://aws.typepad.com/aws/2011/07/ec2-spot-pricing-now-specific-to-each-availability-zone.html

Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, A view of cloud computing, Communications of ACM, Vol. 53, No. 4, April 2010, pp. 50–58. [Online]. http://doi.acm.org/10.1145/1721654.1721672

J. J. Murphy, Technical Analysis of the Financial Markets: a Comprehensive Guide to Trading Methods and Applications, New York, NY: New York Institute of Finance, 1999.

G. Box and G. Jenkins, Time Series Analysis: Forecasting and Control, Hoboken, NJ: John Wiley and Sons, 1970.

S. Haykin, A Comprehensive Foundation, 2nd ed. Upper Saddle River, NJ: Prentice Hall, 1998.

E. Guresen, G. Kayakutlu, and T. U. Daim, Using artificial neural network models in stock market index prediction, Expert Systems with Applications, Vol. 38, No. 8, August 2011, pp. 10 389–10 397. [Online]. Available: http://dx.doi.org/10.1016/j.eswa.2011.02.068

Lin, Z. Yang, and Y. Song, Short-term stock price prediction based on echo state networks, Expert Systems with Applications, Vol. 36, No. 3, Part 2, 2009, pp. 7313–7317, [Online]. http://www.sciencedirect.com/science/article/pii/S0957417408006519

R. R. Lawrence, Using Neural Networks to Forecast Stock Market Prices, University of Manitoba, 1997.

S. H. Kim and S. H. Chun, Graded forecasting using an array of bipolar predictions: application of probabilistic neural networks to a stock market index, International Journal of Forecasting, Vol. 14, No. 3, 1998, pp. 323–337. [Online]. Available: http: //www.sciencedirect.com/science/article/pii/S016920709800003X

X. Zhu, H. Wang, L. Xu, and H. Li, Predicting stock index increments b neural networks: The role of trading volume under different horizons, Expert Systems with Applications, Vol. 34, No. 4, 2008, pp. 3043–3054. [Online]. http://www.sciencedirect.com/science/article/pii/S0957417407002345

Kara, M. Acar Boyacioglu, and O. K. Baykan, Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul stock exchange, Expert Systems with Applications, Vol. 38, No. 5, 2011, pp. 5311-5319. [Online]. http://dx.doi.org/10.1016/j.eswa.2010.10.027

V. A. Golovko, Neural networks: Training, models and applications, Radiotechnika, 2011.

V. Turchenko, P. Beraldi, F. De Simone, and L. Grandinetti, Short-term stock price prediction using mlp in moving simulation mode, in Proceedings of the IEEE 6th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), Vol. 2, 2011, pp. 666–671.

Golovko V., Savitsky J., Laopoulos Th., Sachenko A., and Grandinetti L. Technique of Learning Rate Estimation for Efficient Training of MLP, IEEE-INNS-ENNS International Joint Conference on Neural Networks - IJCNN'00, Como, Italy, July 2000, pp. 323-328.

Wallace R.M., Turchenko V., Sheikhalishahi M., Turchenko I., Shults V., Vazquez-Poletti J.L., Grandinetti L. Applications of Neural-based Spot Market Prediction for Cloud Computing, in Proceedings of the 7th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems IDAACS'2013, Berlin, Germany, 12-14 September 2013, Vol. 2, pp. 710-716.

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Published

2014-08-01

How to Cite

Turchenko, V., Shults, V., Turchenko, I., Wallace, R. M., Sheikhalishahi, M., Vazquez-Poletti, J. L., & Grandinetti, L. (2014). SPOT PRICE PREDICTION FOR CLOUD COMPUTINGUSING NEURAL NETWORKS. International Journal of Computing, 12(4), 348-359. https://doi.org/10.47839/ijc.12.4.615

Issue

2013, Volume 12, Issue 4

Section

Articles

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