Online system for grid resource monitoring and machine learning-based prediction

IEEE Transactions on Parallel and Distributed Systems

Volumn 23, Issue 1, 2012, Pages 134-145

  Hu, Liang ^a   Che, Xi Long ^a   Zheng, Si Qing ^b  

^a JILIN UNIVERSITY   (China)

^b The University of Texas at Dallas   (United States)

Author keywords

genetic algorithm; Grid resource; monitoring and prediction; neural network; particle swarm optimization; support vector machine

Indexed keywords

CORE FUNCTIONS; DISTRIBUTED SYSTEMS; GRID RESOURCE; GRID RESOURCE MONITORING AND PREDICTIONS; JOB SCHEDULING; MODELING AND OPTIMIZATION; PARTICLE SWARM; PROTOTYPE SYSTEM; RESOURCE PREDICTION; SUPPORT VECTOR; SYSTEM ARCHITECTURES; SYSTEM IMPLEMENTATION;

FORECASTING; FUNCTION EVALUATION; GENETIC ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; MONITORING; NEURAL NETWORKS; OPTIMIZATION;

GRID COMPUTING;

EID: 82555168375     PISSN: 10459219     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPDS.2011.108     Document Type: Article

References (33)

1. L. F. Bittencourt and E. R. M. Madeira, "A Performance-Oriented Adaptive Scheduler for Dependent Tasks on Grids," Concurrency and Computation: Practice and Experience, vol. 20, no. 9, pp. 1029-1049, June 2008.
2. F. Wolf and B. Mohr, "Hardware-Counter Based Automatic Performance Analysis of Parallel Programs," Proc. Conf. Parallel Computing (ParCo'03), pp. 753-760, Sept. 2003.
3. J. Dugan et al., "Iperf Project," http://iperf.sourceforge.net/ , Mar. 2008.
4. M. Livny et al., "Condor Hawkeye Project," Univ. of Wisconsin-Madison, http://www.cs.wisc.edu/condor/hawkeye/, Sept. 2009.
5. M. L. Massie, B. N. Chun, and D. E. Culler, "The Ganglia Distributed Monitoring System: Design, Implementation, and Experience," Parallel Computing, vol. 30, no. 7, pp. 817-840, July 2004.
6. A. Waheed et al., "An Infrastructure for Monitoring and Management in Computational Grids," Proc. Fifth Int'l Workshop Languages, Compilers and Run-Time Systems for Scalable Computers, vol. 1915, pp. 235-245, Mar. 2000.
7. J. S. Vetter and D. A. Reed, "Real-Time Performance Monitoring, Adaptive Control, and Interactive Steering of Computational Grids," Int'l J. High Performance Computing Applications, vol. 14, no. 4, pp. 357-366, 2000. (Pubitemid 32024203)
8. D. M. Swany and R. Wolski, "Multivariate Resource Performance Forecasting in the Network Weather Service," Proc. ACM/IEEE Conf. Supercomputing, pp. 1-10, Nov. 2002.
9. P. A. Dinda and D. R. O'Hallaron, "Host Load Prediction Using Linear Models," Cluster Computing, vol. 3, no. 4, pp. 265-280, 2000.
10. E. Caron, A. Chis, F. Desprez, and A. Su, "Design of Plug-in Schedulers for a GRIDRPC Environment," Future Generation Computer Systems, vol. 24, no. 1, pp. 46-57, 2008. (Pubitemid 47337127)
11. P. A. Dinda, "Design, Implementation, and Performance of an Extensible Toolkit for Resource Prediction in Distributed Systems," IEEE Trans. Parallel and Distributed Systems, vol. 17, no. 2, pp. 160-173, Feb. 2006. (Pubitemid 46444800)
12. A. C. Sodan, G. Gupta, L. Han, L. Liu, and B. Lafreniere, "Time and Space Adaptation for Computational Grids with the ATOPGrid Middleware," Future Generation Computer Systems, vol. 24, no. 6, pp. 561-581, 2008. (Pubitemid 351470016)
13. M. Wu and X. H. Sun, "Grid Harvest Service: A Performance System of Grid Computing," J. Parallel and Distributed Computing, vol. 66, no. 10, pp. 1322-1337, 2006. (Pubitemid 44302303)
14. L. T. Lee, D. F. Tao, and C. Tsao, "An Adaptive Scheme for Predicting the Usage of Grid Resources," Computing Computers and Electrical Eng., vol. 33, no. 1, pp. 1-11, 2007. (Pubitemid 44794342)
15. A. Eswaradass, X. H. Sun, and M. Wu, "A Neural Network Based Predictive Mechanism for Available Bandwidth," Proc. 19th IEEE Int'l Parallel and Distributed Processing Symp. (IPDPS'05), p. 33a, 2005.
16. V. N. Vapnik, The Nature of Statistical Learning Theory, second ed. Springer-Verlag, 1999.
17. H. Prem and N. R. S. Raghavan, "A Support Vector Machine Based Approach for Forecasting of Network Weather Services," J. Grid Computing, vol. 4, no. 1, pp. 89-114, 2006. (Pubitemid 43559864)
18. J. H. Holland, Adaptation in Natural and Artificial Systems. MIT Press, 1975.
19. J. Kennedy and R. C. Eberhart, "Particle Swarm Optimization," Proc. IEEE Int'l Conf. Neural Networks, pp. 1942-1948, 1995.
20. L. Fausett, Fundamentals of Neural Networks. Prentice-Hall, 1994.
21. S. Haykin, Neural Networks: A Comprehensive Foundation. Macmillan Publishing, 1994.
22. D. Patterson, Artificial Neural Networks. Prentice-Hall, 1996.
23. A. J. Smola and B. Schölkopf, "A Tutorial on Support Vector Regression," Statistics and Computing, vol. 14, no. 33, pp. 199-222, 2004. (Pubitemid 39063488)
24. Bandwidth Data Set, http://www.slac.stanford.edu/comp/net/iepm-bw.slac. stanford.edu/combinedata/, Feb. 2009.
25. Host Load Data Set, http://people.cs.uchicago.edu/~lyang/Load/, Feb. 2009.
26. Y. Shi and R. C. Eberhart, "A Modified Particle Swarm Optimizer," Proc. IEEE Int'l Conf. Evolutionary Computation, pp. 69-73, 2000.
27. C. W. Hsu, C. C. Chang, and C. J. Lin, "A Practical Guide to Support Vector Classification," http://www.csie.ntu.edu.tw/~cjlin/papers/guide/ guide.pdf Dept. of Computer Science and Information Eng., Nat'l Taiwan Univ., 2003.
28. M. W. Browne, "Cross-Validation Methods," J. Math. Psychology, vol. 44, no. 1, pp. 108-132, 2000.
29. J. Kennedy and R. C. Eberhart, "A Discrete Binary Version of the Particle Swarm Optimization," Proc. IEEE Int'l Conf. Neural Networks, pp. 4104-4108, 1997.
30. Globus Alliance "Globus Project," http://www.globus.org/ toolkit/downloads/4.2.0/, 2008.
31. C. C. Chang and C. J. Lin, "LIBSVM: a Library for Support Vector Machines," http://www.csie.ntu.edu.tw/~cjlin/libsvm/, May 2008.
32. S. Borja, "The Globus Toolkit 4 Programmer's Tutorial," http://gdp. globus.org/gt4-tutorial/, Nov. 2005.
33. M. Livny et al., "Condor Project," Univ. of Wisconsin-Madison, http://www.cs.wisc.edu/condor/, Sept. 2009.