Grid differentiated services: A reinforcement learning approach

Proceedings CCGRID 2008 - 8th IEEE International Symposium on Cluster Computing and the Grid

Volumn , Issue , 2008, Pages 287-294

  Perez, Julien ^a   Kégl, Balazs ^b   Germain Renaud, Cécile ^a   Loomis, Charles ^b  

^a UNIVERSITÉ PARIS SACLAY   (France)

^b UNIVERSITÉ PARIS SUD   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER COMPUTING; COMPUTER ARCHITECTURE; QUALITY OF SERVICE; SCHEDULING;

AUTONOMIC COMPUTING; AUTONOMIC COMPUTING SYSTEM; DIFFERENTIATED SERVICES; INTER-ARRIVAL TIME; LARGE SCALE PRODUCTIONS; REINFORCEMENT LEARNING APPROACH; RESOURCE AVAILABILITY; SYNTHETIC WORKLOADS;

REINFORCEMENT LEARNING;

EID: 50649087556     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CCGRID.2008.33     Document Type: Conference Paper

References (11)

1. F. G. et. al. Building an Infrastructure for scientific Grid computing: status and goals of the EGEE project. Philosophical Transactions of the Royal Society A, 1833, 2005.
2. C. Germain-Renaud, C. Loomis, J. Mo'scicki, and R. Texier. Scheduling for responsive grids. Journal of Grid Computing, 2007. on line doi= 10.1007/s10723-007-9086-4.
3. E. D. Jensen, C. D. Locke, and H. Tokuda. A time-driven scheduling model for real-time operating systems. In IEEE Real-Time Systems Symposium, pages 112-122, 1985.
4. J. O. Kephart and D. M. Chess. The vision of autonomic computing. IEEE Computer, 36(1):41-50, 2003.
5. L.Amar, A. Barak, E. Levy, and M. Okun. An on-line algorithm for fair-share node allocations in a cluster. In CCGRID, pages 83-91, 2007.
6. C. E. Rasmusen and C. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.
7. L. G. Roberts. Beyond Moore's law: Internet growth trends. Computer, 3(1):117-119, 2000.
8. R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998.
9. G. Tesauro, N. K. Jong, R. Das, and M. N. Bennani. On the use of hybrid reinforcement learning for autonomic resource allocation. Cluster Computing, 10(3):287-299, 2007.
10. G. J. Tesauro and J. O. Kephart. Utility functions in autonomic systems. In Proceedings of the 1st International Conference on Autonomic Computing(ICAC'04), pages 70-77, 2004.
11. D. Vengerov. A reinforcement learning approach to dynamic resource allocation. Eng. Appl. Artif. Intell., 20(3), 2007.