Grid performance prediction using state-space model

Concurrency and Computation: Practice and Experience

Volumn 21, Issue 9, 2009, Pages 1109-1130

  Kalantari, Mohammad ^a   Akbari, Mohammad Kazem ^a  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Grid computing; Kalman recursions; Performance prediction; Scheduling; State space model

Indexed keywords

DEFECTS; FORECASTING; IMAGE SEGMENTATION; MAXIMUM PRINCIPLE; REPAIR; SCHEDULING; STATE SPACE METHODS; TIME SERIES;

AVERAGE PREDICTION ERROR; EXPECTATION-MAXIMIZATION ALGORITHMS; FORECASTING PURPOSE; PERFORMANCE PREDICTION; RECURSIONS; SCHEDULING POLICIES; STATE - SPACE MODELS; TIME SERIES TECHNIQUES;

GRID COMPUTING;

EID: 67949111166     PISSN: 15320626     EISSN: 15320634     Source Type: Journal    
DOI: 10.1002/cpe.1375     Document Type: Article

References (22)

1. Aktaruzzaman M. Literature review and survey: Resource discovery in computational grids. Technical Report, School of Computer Science, University of Windsor, Ont., Canada, 2003.
2. Vadhiyar S, Dongarra J. A performance oriented migration framework for the grid. Proceedings of the 3rd International Symposium on Cluster Computing and the Grid (CCGrid'03), Tokyo, Japan, May 2003; 130-139.
3. Gong L, Sun X,Watson EF. Performance modeling and prediction of non-dedicated network computing. IEEE Transactions on Computers 2002; 51(9):1041-1055.
4. Sun X, Wu M. Grid harvest service: A system for long-term application-level task scheduling. Proceedings of the 2003 International Parallel and Distributed Processing Symposium (IPDPS 2003), Nice, France, April 2003; 25-32.
5. Akioka S, Muraoka Y. Extended forecast of CPU and network load on computational grid. IEEE International Symposium, on Cluster Computing and The Grid, Chicago, IL, 2004; 765-772.
6. Liang J, Nahrstedt K, Zhou Y. Adaptive multi-resource prediction in distributed resource sharing environment. 2004 IEEE International Symposium on Cluster Computing and The Grid, Chicago, IL, 2004; 1-8.
7. Takefusa A, Matsuoka S, Casanova H, Berman F. A study of deadline scheduling for client-server systems on the computational grid. Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing(HPDC-10'01), San Francisco, CA, August 2001; 405-406.
8. Dinda PA, O'Hallaron DR. Host load prediction using linear models. Journal of Cluster Computing 2000; 3(4):265-280.
9. Dinda PA. The statistical properties of host load. Technical Report, CMU, 1998.
10. Jin H, Shi X, Qiang W, Zou D. An adaptive meta-scheduler for data-intensive application. International Journal of Grid and Utility Computing 2005; 1(1):32-37.
11. Wolski R, Spring NT, Hayes J. The network weather service: A distributed resource performance forecasting service for metacomputing. The Journal of Future Generation Computer Systems 1999; 15(5-6):757-768.
12. Yang L, Foster I, Schopf JM. Homeostatic and tendency-based CPU Load prediction. Proceedings of 17th International Parallel and Distributed Processing Symposium (IPDPS 2003), 2003; 42-50.
13. Gibbons R. A historical application profiler for use by parallel schedulers. International Workshop on Job Scheduling Strategies for Parallel Processing (Lecture Notes in Computer Science, vol. 1291). Springer: Berlin, 1997; 5877.
14. Smith W, Foster I, Taylor V. Predicting application run times using historical information. International Workshop on Job Scheduling Strategies for Parallel Processing (Lecture Notes in Computer Science, vol. 1459). Springer: Berlin, 1998; 122-136.
15. Kapadia NH, Fortes JAB, Brodley CE. Predictive application performance modeling in a computational grid environment. IEEE International Symposium for High Performance Distributed Computing (HPDC), Redondo Beach, CA, 3-6 August 1999.
16. Downey AB. Predicting queue times on space sharing parallel computers. Proceedings of the 11th International Parallel Processing Symposium, 1997; 209-218.
17. Smith W, Foster I, Taylor V. Using run time predictions to estimate queue wait times and improve scheduler performance. International Workshop on Job Scheduling Strategies for Parallel Processing (Lecture Notes in Computer Science, vol. 1659). Springer: Berlin, 1999; 202-219.
18. Brockwell PJ, Davis RA. Introduction to Time Series and Forecasting. Springer: Berlin, 1996.
19. Moon TK, Stirling WC. Mathematical Methods and Algorithms for Signal Processing. Prentice-Hall: Englewood Cliffs, NJ, 1999.
20. Kalantari M. Linear models for waiting time prediction. Technical Report, Amirkabir University of Technology, 2005.
21. Gupta N, Hauser R, Johnson NF. Using Artificial Market Models to Forecast Financial Time Series. e-print physics/ 0506134 at xxx.lanl.gov, June 2005.
22. Parallel Workload Archive, http://www.cs.huji.ac.il/labs/parallel/ workload/ [May 2007].