Open Access Open Access  Restricted Access Subscription or Fee Access

Distributed System Architecture for Grid Resource Monitoring and Resource State Prediction

S. Sabeetha Saraswathi, S. Mohana, N. Vijayaraj, S. Imavathy, S. Mahalakshmi

Abstract


The core functions of grid computing are Resource allocation and job scheduling. These functions are based on adequate information of available resources. Timely acquiring resource status information is of great importance in ensuring overall performance of grid computing. This work aims at building a distributed system for grid resource monitoring and prediction. The system architecture for grid resource monitoring and prediction has been design. The key issues for system implementation, including machine learning-based methodologies for modeling and optimization of resource prediction models are discussed. Evaluations are performed on a prototype system. The experimental results indicate that the efficiency and accuracy of the system meet the demand of online system for grid resource monitoring and prediction.

Keywords


Grid Resource, Monitoring and Prediction, Neural Network, Support Vector Machine, Genetic Algorithm, Particle Swarm Optimization.

Full Text:

PDF

References


Online System for Grid Resource Monitoring and Machine Learning-Based Prediction Liang Hu, Xi-Long Che, Member, IEEE, and Si-Qing Zheng, Senior Member, IEEE

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.

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.

J.Duganet al., “Iperf Project,” http://iperf.sourceforge.net/,Mar. 2008.

M. Livny et al., “Condor Hawkeye Project,” Univ. of Wisconsin-Madison, http://www.cs.wisc.edu/condor/hawkeye/, Sept. 2009.

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.

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.

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.

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.

P.A. Dinda and D.R. O’Hallaron, “Host Load Prediction Using Linear Models,” Cluster Computing, vol. 3, no. 4, pp. 265-280, 2000.

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

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.

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.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.