Grid resources prediction with support vector regression and particle swarm optimization

3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice

Volumn 1, Issue , 2010, Pages 417-422

  Hu, Guosheng ^a   Hu, Liang ^a   Li, Hongwei ^a   Li, Kun ^a   Liu, Wei ^a  

^a JILIN UNIVERSITY   (China)

Author keywords

Grid resources prediction; Particle swarm optimization; Support vector regression

Indexed keywords

BACK-PROPAGATION NEURAL NETWORKS; BENCHMARK DATA; FREE PARAMETERS; GENERALIZATION ABILITY; GRID RESOURCE; GRID SCHEDULERS; HYBRID MODEL; PREDICTION PERFORMANCE; PREDICTIVE ACCURACY; REGRESSION ALGORITHMS; SUPPORT VECTOR REGRESSION; SUPPORT VECTOR REGRESSIONS;

FORECASTING; GRID COMPUTING; NEURAL NETWORKS; REGRESSION ANALYSIS; VECTORS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 77956428042     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CSO.2010.40     Document Type: Conference Paper

References (20)

1. 1. Foster, C. Kesselman, The Grid: Blueprint for a New Computing Infrastructure, Morgan Kaufmann Publishers, San Francisco, CA, 1999.
2. P. Dinda ,D. O'Hallaron.,"Host load prediction using linear models," Proc. The 8th IEEE Symposium on High Performance Distributed Computing, 1999.
3. R. Wolski, N. T. Spring, and J. Hayes, "The network weather service: A distributed resource performance forecasting service for metacomputing., " The Journal of Future Generation Computing Systems, 1999.
4. Yuanyuan Zhang, Wei Sun, Yasushi Inoguchi, "Predict task running time in grid environments based on CPU load predictions," Future Generation Computer System, 2008, pp.489-497.
5. Shigeo Kamitsuji, Ritei Shibata, "Effectiveness of stochastic neural network for prediction of fall or rise of TOPIX," Research Paper, Keio University, 2004.
6. Liu Zhixin, Guan Xinping, Long Chengnia, "Congestion control method: improved ERICA algorithm based on available bandwidth prediction using neural network", Journal of Systems Engineering and Electronics, Vo1.16, No.1, pp.15l-l54,2005.
7. Nikola Kasabov, Evolving connectionist systems, Springer-Verlag London Limited; 2003.
8. Vapnik, V., Statistical learning theory, Wiley, New York, 1998.
9. Smola A, Sch6lkopf, B., "A tutorial on support vector regression, " NeuroCOLT Technical Report NC-TR-98-1030, Royal Holloway College University of London, 1998.
10. L. P. Wang, Support Vector Machines: Theory and Application, Springer, Berlin, 2005.
11. J. Kennedy, R. Eberhart, A new optimizer using particle swarm theory in: Proc. The Sixth International Symposium on Micro Machine Human Science, IEEE press, Nagoya, Japan, 1995, pp.3943.
12. Shi, Y. Eberhart, R., A Modified Particle Swarm Optimizer, Porco International Conferenece on Evolutionary Computation, 1998.
13. Sheng-Fa Yuan, Fu-Lei Chu, "Fault diagnostics based on particle swarm optimization and support vector machines", Mechanical Systems and Signal Processing, Volume 21, Issue 4, pp. 17871798,2007.
14. Hostload data set. Available at 〈http://cs.uchicago.edu/lyang/Loadl 〉 Jul 19, 2003.
15. Kuan-Yu Chen, Cheng-Hua Wang, "Support vector regression with genetic algorithms in forecasting tourism demand," Tourism Management, pp.2l5- 226, 2007.
16. C.C. Chang, C.J. Lin, LIBSVM: a library for support vector machines, Software available from 〈http://www.csie.ntu.edu.tw/~cjlin/libsvm/ 〉,2001.
17. Yuhui Shi and Russell C. Eberhart, "Parameter selection in particle swarm optimization", Department of Electrical Engineering, Springer Berlin, Heidelberg, volume 1447,1988.
18. Hornik, K., Stinchcombe, M., & White, H., "Multilayer feedforward networks are universal approximations," Neural Networks, pp.336 359,1989.
19. Rumelhart, E., Hinton, G. E., & Williams, R. J., Learning internal representations by error propagation in parallel distributed processing, Cambridge, MA: MIT Press, 1986.
20. Lewis, C. D., International and business forecasting methods, London: Butterworths, 1982.