Proffering a Brand New Methodology to Resource Discovery in Grid based on Economic Criteria Using Learning Automata
Resource discovery is one of the chief services of a grid. A new approach to discover the provenances in grid through learning automata has been propounded in this article. The objective of the aforementioned resource-discovery service is to select the resource based upon the user-s applications and the mercantile yardsticks that is to say opting for an originator which can accomplish the user-s tasks in the most economic manner. This novel service is submitted in two phases. We proffered an applicationbased categorization by means of an intelligent nerve-prone plexus. The user in question sets his or her application as the input vector of the nerve-prone nexus. The output vector of the aforesaid network limns the appropriateness of any one of the resource for the presented executive procedure. The most scrimping option out of those put forward in the previous stage which can be coped with to fulfill the task in question is picked out. Te resource choice is carried out by means of the presented algorithm based upon the learning automata.
[1] I. Foster, C. Kesselman and S. Tuecke, "The anatomy of the Grid:
Enabling scalable virtual organizations", International Journal of
Supercomputer Applications, 2001.
[2] Viktors Berstis, "Fundamentals of Grid Computing", IBM Redbook
series, November 2002, http://ibm.com/redbooks.
[3] R. Buyya, D. Abramson, and J. Giddy, "A Case for Economy Grid
Architecture for Service-Oriented Grid Computing", Proceedings of the
10th IEEE International Heterogeneous Computing Workshop, April
2001.
[4] R. Buyya, D. Abramson, J. Giddy, and H. Stockinger, "Economic
Models for Resource Management and Scheduling in Grid Computing",
The Journal of Concurrency and Computation: Practice and
Experience, May 2002.
[5] M. Murshed, R. Buyya, and D. Abramson, "GridSim: A Portable and
Scalable Toolkit for Modeling and Simulation of Application Scheduling
for Parallel and Distributed Computing", Technical Report, Monash
University, Oct. 2001. Available at: http://www.buyya.com/gridsim/.
[6] K. Narendra and M. A. L. Thathachar, Learning Automata: An
Introduction, Prentice Hall, Englewood Cliffs, New Jersey, 1989.
[7] S. Lalis and A. Karipidis, "An Open Market-Based Framework for
Distributed Computing over the Internet", Proceedings of the First
IEEE/ACM International Workshop on Grid Computing (GRID 2000),
Bangalore, India, Springer Verlag Press, Germany, December 2000.
[8] Maxine Brown, Thomas DeFanti, Michael McRobbie, Alan Verlo, Dana
Plepys, Donald McMullen, Karen Adams, Jason Leigh, Andrew
Johnson, Ian Foster, Carl Kesselman, Andrew Schmidt, Steven
Goldstein: The International Grid (iGrid): Empowering Global Research
Community Networking Using High Performance International Internet
Services. Proceedings of INET, 2003, pp. 3-9.
[9] Puppin, D., Moncelli S., Baraglia, R., Tonellotto, N.,Silvestri, F.: A
peer-to-peer information service for the grid. In: Proceedings of the
GridNets 2004 Workshop, San Jos'e, CA (2004)
[10] K. Narendra and M. A. L. Thathachar, Learning Automata: An
Introduction, Prentice Hall, Englewood Cliffs, New Jersey, 1989.
[11] K. Najim and A. S. Poznyak, Learning Automata: Theory and
Application, Elsevier Science Ltd., Tarrytown, NY, 1994.
[12] A. Galstyan, K. Czajkowski, and K. Lerman, "Resource allocation in the
grid using reinforcement learning", Proceedings of the 3rd International
Joint Conference on Autonomous Agents and Multiagent Systems
(AAMAS'04), pp. 1314-1315, 2004.
[13] Carsten Peterson and Thorsteinn Rognvaldsson, An Introduction to
Artificial Neural Networks, LU TP 91-23, September 1991 (Lectures
given at the 1991 Cern School of Computing, Sweden)
[14] R. Buyya and M. Murshed, "GridSim: A Toolkit for the Modeling and
Simulation of Distributed Resource Management and Scheduling for
Grid Computing", Journal of Concurrency and Computation: Practice
and Experience, pp. 1-32, May 2002.
[15] R. Buyya, D. Abramson, J. Giddy, and H. Stockinger, "Economic
Models for Resource Management and Scheduling in Grid Computing",
The Journal of Concurrency and Computation: Practice and
Experience, May 2002.
[16] Rummelhart, D. E.; Hinton, G. E.; Williams, R. J., (2004).Learning
representations by back-propagation errors. ature, 323, 533-536.
[17] S. Castano, A. Ferrara, S. Montanelli and G. Racca, "Matching
Techniques for Resource Discovery in Distributed Systems Using
Heterogeneous Ontology Descriptions". Proc. Int. Conf. on Information
Technology:Coding and Computing (ITCC-04), vol. 1, pp. 360-366,
2004.
[1] I. Foster, C. Kesselman and S. Tuecke, "The anatomy of the Grid:
Enabling scalable virtual organizations", International Journal of
Supercomputer Applications, 2001.
[2] Viktors Berstis, "Fundamentals of Grid Computing", IBM Redbook
series, November 2002, http://ibm.com/redbooks.
[3] R. Buyya, D. Abramson, and J. Giddy, "A Case for Economy Grid
Architecture for Service-Oriented Grid Computing", Proceedings of the
10th IEEE International Heterogeneous Computing Workshop, April
2001.
[4] R. Buyya, D. Abramson, J. Giddy, and H. Stockinger, "Economic
Models for Resource Management and Scheduling in Grid Computing",
The Journal of Concurrency and Computation: Practice and
Experience, May 2002.
[5] M. Murshed, R. Buyya, and D. Abramson, "GridSim: A Portable and
Scalable Toolkit for Modeling and Simulation of Application Scheduling
for Parallel and Distributed Computing", Technical Report, Monash
University, Oct. 2001. Available at: http://www.buyya.com/gridsim/.
[6] K. Narendra and M. A. L. Thathachar, Learning Automata: An
Introduction, Prentice Hall, Englewood Cliffs, New Jersey, 1989.
[7] S. Lalis and A. Karipidis, "An Open Market-Based Framework for
Distributed Computing over the Internet", Proceedings of the First
IEEE/ACM International Workshop on Grid Computing (GRID 2000),
Bangalore, India, Springer Verlag Press, Germany, December 2000.
[8] Maxine Brown, Thomas DeFanti, Michael McRobbie, Alan Verlo, Dana
Plepys, Donald McMullen, Karen Adams, Jason Leigh, Andrew
Johnson, Ian Foster, Carl Kesselman, Andrew Schmidt, Steven
Goldstein: The International Grid (iGrid): Empowering Global Research
Community Networking Using High Performance International Internet
Services. Proceedings of INET, 2003, pp. 3-9.
[9] Puppin, D., Moncelli S., Baraglia, R., Tonellotto, N.,Silvestri, F.: A
peer-to-peer information service for the grid. In: Proceedings of the
GridNets 2004 Workshop, San Jos'e, CA (2004)
[10] K. Narendra and M. A. L. Thathachar, Learning Automata: An
Introduction, Prentice Hall, Englewood Cliffs, New Jersey, 1989.
[11] K. Najim and A. S. Poznyak, Learning Automata: Theory and
Application, Elsevier Science Ltd., Tarrytown, NY, 1994.
[12] A. Galstyan, K. Czajkowski, and K. Lerman, "Resource allocation in the
grid using reinforcement learning", Proceedings of the 3rd International
Joint Conference on Autonomous Agents and Multiagent Systems
(AAMAS'04), pp. 1314-1315, 2004.
[13] Carsten Peterson and Thorsteinn Rognvaldsson, An Introduction to
Artificial Neural Networks, LU TP 91-23, September 1991 (Lectures
given at the 1991 Cern School of Computing, Sweden)
[14] R. Buyya and M. Murshed, "GridSim: A Toolkit for the Modeling and
Simulation of Distributed Resource Management and Scheduling for
Grid Computing", Journal of Concurrency and Computation: Practice
and Experience, pp. 1-32, May 2002.
[15] R. Buyya, D. Abramson, J. Giddy, and H. Stockinger, "Economic
Models for Resource Management and Scheduling in Grid Computing",
The Journal of Concurrency and Computation: Practice and
Experience, May 2002.
[16] Rummelhart, D. E.; Hinton, G. E.; Williams, R. J., (2004).Learning
representations by back-propagation errors. ature, 323, 533-536.
[17] S. Castano, A. Ferrara, S. Montanelli and G. Racca, "Matching
Techniques for Resource Discovery in Distributed Systems Using
Heterogeneous Ontology Descriptions". Proc. Int. Conf. on Information
Technology:Coding and Computing (ITCC-04), vol. 1, pp. 360-366,
2004.
@article{"International Journal of Information, Control and Computer Sciences:59719", author = "Ali Sarhadi and Mohammad Reza Meybodi and Ali Yousefi", title = "Proffering a Brand New Methodology to Resource Discovery in Grid based on Economic Criteria Using Learning Automata", abstract = "Resource discovery is one of the chief services of a grid. A new approach to discover the provenances in grid through learning automata has been propounded in this article. The objective of the aforementioned resource-discovery service is to select the resource based upon the user-s applications and the mercantile yardsticks that is to say opting for an originator which can accomplish the user-s tasks in the most economic manner. This novel service is submitted in two phases. We proffered an applicationbased categorization by means of an intelligent nerve-prone plexus. The user in question sets his or her application as the input vector of the nerve-prone nexus. The output vector of the aforesaid network limns the appropriateness of any one of the resource for the presented executive procedure. The most scrimping option out of those put forward in the previous stage which can be coped with to fulfill the task in question is picked out. Te resource choice is carried out by means of the presented algorithm based upon the learning automata.
", keywords = "Resource discovery, learning automata, neural network, economic policy", volume = "3", number = "2", pages = "440-7", }
