Grid-HPA: Predicting Resource Requirements of a Job in the Grid Computing Environment
For complete support of Quality of Service, it is better that environment itself predicts resource requirements of a job by using special methods in the Grid computing. The exact and correct prediction causes exact matching of required resources with available resources. After the execution of each job, the used resources will be saved in the active database named "History". At first some of the attributes will be exploit from the main job and according to a defined similarity algorithm the most similar executed job will be exploited from "History" using statistic terms such as linear regression or average, resource requirements will be predicted. The new idea in this research is based on active database and centralized history maintenance. Implementation and testing of the proposed architecture results in accuracy percentage of 96.68% to predict CPU usage of jobs and 91.29% of memory usage and 89.80% of the band width usage.
[1] I. Foster, and C. Kesselman, The Grid: Blueprint for a New Computing
Infrastructure. Morgan Kafmann Publisher Inc., 1999.
[2] C. Weng, X. Lu, "Heuristic scheduling for bag-of-tasks applications in
combination with QoS in the Computational Grid", Elsevier, Vol. 21
Pages 271-280, 2005.
[3] A. Ali, A. Anjum, J. Bunn, R. Cavanaugh, F. Lingen, R. McClatchey,
M. Mehmood, H. Newman, C. Steenberg, M. Thomas and I. Willers,
"Predicting the Resource Requirements of a Job Submission",
Computing in High Energy Physics (CHEP04) , Switzerland, 2004.
[4] S. Krishnaswamy , S. Wai Loke and A. Zaslavsky "Estimating
Computation Times of Data-Intensive Applications" IEEE Distributed
Systems Online Vol. 5, No. 4, pp. 127-136, April 2004.
[5] P. Keyani, N. Sample and G. Wiederhold, "Scheduling Under
Uncertainty: Planning for the Ubiquitous Grid", proc. of 5th international
conf. on coordination models and languages, pp. 300-316, 2002.
[6] K. Krauter, R. Buyya and M. Maheswaran, "A Taxonomy and survay of
Grid Resource management Systems", Software- practice & experience,
vol. 32, pp. 135-164, 2002.
[7] A. Luther, R. Buyya, R. Ranjam. and S. Venugopal, " Alchemi: A .Netbased
Grid Computing Framework and its Integration into Global
Grids", Technical Report, GRIDS-TR-2003-8, Grid Computing and
Distributed Systems Laboratory, University of Melbourne, Australia,
December 2003.
[8] M. Roehrig, W. Ziegler and P. Wieder, "Grid Scheduling Dictionary of
Terms and Keywords", Vol. GFD-I11 of global Grid Forum Documents,
Global Grid Forum, 2003.
[9] Y. Gao, H. Rong and J. Huang, "Adaptive grid Job scheduling with
Genetic Algorithms", Future Generation Computer Systems, Vol. 21, pp.
151-161, Jan. 2005,
[10] R. Buyya, D. Abramson, and J. Giddy, "Nimrod-G: An Architecture for
a Resource Management and Scheduling System in a Global
Computational Grid," Proc. Fourth Int-l Conf. High-Performance
Computing, Asia-Pacific Region, IEEE CS Press, Los Alamitos, Calif.,
2000.
[11] R. Buyya, D. Abramson, and J. Giddy, "Economy-Driven Resource
Management Architecture for Computational Power Grids," Proc. Int-l
Conf. Parallel and Distributed Processing Techniques and Applications,
CSREA Press, 2000.
[12] R. Buyya et al., "Economic Models for Management of Resources in
Peer-to-Peer and Grid Computing," Proc. SPIE Int-l Conf. Commercial
Applications for High-Performance Computing, SPIE, Bellingham,
Wash., 2001.
[13] O. Elgerd, Electric Energy Systems Theory: An Introduction, 2nd ed.,
McGraw Hill, New York, 1982.
[14] I. Foster and C. Kesselman, "Globus: A Metacomputing Infrastructure
Toolkit," Int-l J. Supercomputer Applications, vol. 11, no. 2, 1997, pp.
115-128.
[15] H. Casanova and J. Dongarra, "NetSolve: A Network Server for Solving
Computational Science Problems," Int-l J. Supercomputer Applications
and High Performance Computing, vol. 11, no. 3, Fall 1997.
[16] I. Foster et al., "A Security Architecture for Computational Grids," Proc.
5th ACM Conf. Computer and Communications Security, ACM Press,
New York, 1998.
[17] R. Buyya, J. Giddy, and D. Abramson, "A Case for Economy Grid
Architecture for Service-Oriented Grid Computing," 10th IEEE Int-l
Heterogeneous Computing Workshop, IEEE CS Press, Los Alamitos,
Calif., 2001.
[1] I. Foster, and C. Kesselman, The Grid: Blueprint for a New Computing
Infrastructure. Morgan Kafmann Publisher Inc., 1999.
[2] C. Weng, X. Lu, "Heuristic scheduling for bag-of-tasks applications in
combination with QoS in the Computational Grid", Elsevier, Vol. 21
Pages 271-280, 2005.
[3] A. Ali, A. Anjum, J. Bunn, R. Cavanaugh, F. Lingen, R. McClatchey,
M. Mehmood, H. Newman, C. Steenberg, M. Thomas and I. Willers,
"Predicting the Resource Requirements of a Job Submission",
Computing in High Energy Physics (CHEP04) , Switzerland, 2004.
[4] S. Krishnaswamy , S. Wai Loke and A. Zaslavsky "Estimating
Computation Times of Data-Intensive Applications" IEEE Distributed
Systems Online Vol. 5, No. 4, pp. 127-136, April 2004.
[5] P. Keyani, N. Sample and G. Wiederhold, "Scheduling Under
Uncertainty: Planning for the Ubiquitous Grid", proc. of 5th international
conf. on coordination models and languages, pp. 300-316, 2002.
[6] K. Krauter, R. Buyya and M. Maheswaran, "A Taxonomy and survay of
Grid Resource management Systems", Software- practice & experience,
vol. 32, pp. 135-164, 2002.
[7] A. Luther, R. Buyya, R. Ranjam. and S. Venugopal, " Alchemi: A .Netbased
Grid Computing Framework and its Integration into Global
Grids", Technical Report, GRIDS-TR-2003-8, Grid Computing and
Distributed Systems Laboratory, University of Melbourne, Australia,
December 2003.
[8] M. Roehrig, W. Ziegler and P. Wieder, "Grid Scheduling Dictionary of
Terms and Keywords", Vol. GFD-I11 of global Grid Forum Documents,
Global Grid Forum, 2003.
[9] Y. Gao, H. Rong and J. Huang, "Adaptive grid Job scheduling with
Genetic Algorithms", Future Generation Computer Systems, Vol. 21, pp.
151-161, Jan. 2005,
[10] R. Buyya, D. Abramson, and J. Giddy, "Nimrod-G: An Architecture for
a Resource Management and Scheduling System in a Global
Computational Grid," Proc. Fourth Int-l Conf. High-Performance
Computing, Asia-Pacific Region, IEEE CS Press, Los Alamitos, Calif.,
2000.
[11] R. Buyya, D. Abramson, and J. Giddy, "Economy-Driven Resource
Management Architecture for Computational Power Grids," Proc. Int-l
Conf. Parallel and Distributed Processing Techniques and Applications,
CSREA Press, 2000.
[12] R. Buyya et al., "Economic Models for Management of Resources in
Peer-to-Peer and Grid Computing," Proc. SPIE Int-l Conf. Commercial
Applications for High-Performance Computing, SPIE, Bellingham,
Wash., 2001.
[13] O. Elgerd, Electric Energy Systems Theory: An Introduction, 2nd ed.,
McGraw Hill, New York, 1982.
[14] I. Foster and C. Kesselman, "Globus: A Metacomputing Infrastructure
Toolkit," Int-l J. Supercomputer Applications, vol. 11, no. 2, 1997, pp.
115-128.
[15] H. Casanova and J. Dongarra, "NetSolve: A Network Server for Solving
Computational Science Problems," Int-l J. Supercomputer Applications
and High Performance Computing, vol. 11, no. 3, Fall 1997.
[16] I. Foster et al., "A Security Architecture for Computational Grids," Proc.
5th ACM Conf. Computer and Communications Security, ACM Press,
New York, 1998.
[17] R. Buyya, J. Giddy, and D. Abramson, "A Case for Economy Grid
Architecture for Service-Oriented Grid Computing," 10th IEEE Int-l
Heterogeneous Computing Workshop, IEEE CS Press, Los Alamitos,
Calif., 2001.
@article{"International Journal of Information, Control and Computer Sciences:55073", author = "M. Bohlouli and M. Analoui", title = "Grid-HPA: Predicting Resource Requirements of a Job in the Grid Computing Environment", abstract = "For complete support of Quality of Service, it is better that environment itself predicts resource requirements of a job by using special methods in the Grid computing. The exact and correct prediction causes exact matching of required resources with available resources. After the execution of each job, the used resources will be saved in the active database named "History". At first some of the attributes will be exploit from the main job and according to a defined similarity algorithm the most similar executed job will be exploited from "History" using statistic terms such as linear regression or average, resource requirements will be predicted. The new idea in this research is based on active database and centralized history maintenance. Implementation and testing of the proposed architecture results in accuracy percentage of 96.68% to predict CPU usage of jobs and 91.29% of memory usage and 89.80% of the band width usage.
", keywords = "Active Database, Grid Computing, ResourceRequirement Prediction, Scheduling,", volume = "2", number = "9", pages = "3000-5", }
