L. Baird, Residual Algorithms: Reinforcement Learning with Function Approximation, Proceedings of the Twelfth International Conference on Machine Learning, pp.30-37, 1995.
DOI : 10.1016/B978-1-55860-377-6.50013-X

P. Beckman, S. ßnadella, N. , and I. ßbeschastnikh, SPRUCE: A System for Supporting Urgent High-Performance Computing, IFIP series, issue.239, pp.295-311, 2007.
DOI : 10.1007/978-0-387-73659-4_16

C. Blanchet, R. Mollon, D. Thain, and G. Deleage, Grid Deployment of Legacy Bioinformatics Applications with Transparent Data Access, 2006 7th IEEE/ACM International Conference on Grid Computing, pp.120-127, 2006.
DOI : 10.1109/ICGRID.2006.311006

J. A. Boyan and A. W. Moore, Generalization in reinforcement learning: Safely approximating the value function, Advances in Neural Information Processing Systems 7, pp.369-376

A. Chandra, M. Adler, and P. Shenoy, Deadline fair scheduling: Bridging the theory and practice of proportionate-fair scheduling in multiprocessor servers, Proc. of the 7th IEEE Real-Time Technology and Applications Symposium, 2001.

D. J. Colling and A. Mcgough, The gridcc project, International Conference on Communication System Software and Middleware, pp.1-4, 2006.

K. Doya, Reinforcement Learning in Continuous Time and Space, Neural Computation, vol.3, issue.1, pp.219-245, 2000.
DOI : 10.1109/9.580874

F. Gagliardi, Building an infrastructure for scientific Grid computing: status and goals of the EGEE project, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.363, issue.1833, p.1833, 2005.
DOI : 10.1098/rsta.2005.1603

J. Montagnat, Workflow-Based Data Parallel Applications on the EGEE Production Grid Infrastructure, Journal of Grid Computing, vol.129, issue.2, pp.369-383, 2008.
DOI : 10.1007/s10723-008-9108-x

URL : https://hal.archives-ouvertes.fr/hal-00683983

I. Foster, C. Kesselman, and S. Tuecke, The Anatomy of the Grid: Enabling Scalable Virtual Organizations, International Journal of High Performance Computing Applications, vol.15, issue.3, pp.200-222, 2001.
DOI : 10.1177/109434200101500302

I. Foster, Y. Zhao, I. Raicu, and S. Lu, Cloud Computing and Grid Computing 360-Degree Compared, 2008 Grid Computing Environments Workshop, pp.1-10, 2008.
DOI : 10.1109/GCE.2008.4738445

C. Germain, R. Texier, and A. Osorio, Interactive Volume Reconstruction and Measurement on the Grid, Methods of Information in Medecine, vol.44, issue.2, pp.227-232, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00288872

C. Germain-renaud, C. Loomis, J. Mo´scickimo´scicki, and R. Texier, Scheduling for Responsive Grids, Journal of Grid Computing, vol.16, issue.2, pp.15-27, 2008.
DOI : 10.1007/s10723-007-9086-4

URL : https://hal.archives-ouvertes.fr/inria-00117486

C. , G. Renaud, J. Perez, B. Kégl, and C. Loomis, Grid Differentiated Services: a Reinforcement Learning Approach, 8th IEEE International Symposium on Cluster Computing and the Grid, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00287826

G. J. Gordon, Reinforcement learning with function approximation converges to a region, Advances in Neural Information Processing Systems, pp.1040-1046, 2001.

I. Szitaßand, V. Gyenes, and A. Lorinczß, Reinforcement Learning with Echo State Networks, Artificial Neural Networks, ICANN 2006, pp.830-839, 2006.

H. Jaeger, Adaptive nonlinear system identification with Echo State Networks, Advances in Neural Information Processing Systems 15, pp.593-600, 2003.

H. Jaeger and H. Haas, Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication, Science, vol.304, issue.5667, pp.78-80, 2004.
DOI : 10.1126/science.1091277

E. Laure, Programming the Grid with gLite*, Computational Methods in Science and Technology, vol.12, issue.1, 2006.
DOI : 10.12921/cmst.2006.12.01.33-45

H. Li and M. Muskulus, Analysis and modeling of job arrivals in a production grid, ACM SIGMETRICS Performance Evaluation Review, vol.34, issue.4, pp.59-70, 2007.
DOI : 10.1145/1243401.1243402

I. Mirman, Going parallel the new way, 2006.

J. Mo´scickimo´scicki, M. T. Bubak, H. C. Lee, A. Muraru, and P. M. Sloot, Quality of service on the grid with user level scheduling, Cracow Grid Workshop, pp.119-129, 2007.

S. M. Park and M. Humpfrey, Feedback-controlled resource sharing for predictable e-science, SC'08: Proceedings of the 2008 ACM/IEEE conference on Supercomputing, pp.1-11, 2008.

J. Perez, C. Germain-renaud, B. Kégl, and C. Loomis, Utility-Based Reinforcement Learning for Reactive Grids, 2008 International Conference on Autonomic Computing, 2008.
DOI : 10.1109/ICAC.2008.18

URL : https://hal.archives-ouvertes.fr/inria-00287354

C. E. Rasmusen and C. Williams, Gaussian Processes in Machine Learning, 2006.
DOI : 10.1162/089976602317250933

Q. Snell, M. J. Clement, D. B. Jackson, and C. Gregory, The Performance Impact of Advance Reservation Meta-scheduling, IPDPS '00/JSSPP '00: Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing, pp.137-153
DOI : 10.1007/3-540-39997-6_9

A. Srinivasan and J. H. Anderson, Efficient scheduling of soft real-time applications on multiprocessors, 15th Euromicro Conference on Real-Time Systems, 2003. Proceedings., pp.1-14
DOI : 10.1109/EMRTS.2003.1212727

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, IEEE Transactions on Neural Networks, vol.9, issue.5, 1998.
DOI : 10.1109/TNN.1998.712192

G. Tesauro, N. K. Jong, R. Das, and M. N. Bennani, On the use of hybrid reinforcement learning for autonomic resource allocation, Cluster Computing, vol.4, issue.4, pp.287-299, 2007.
DOI : 10.1007/s10586-007-0035-6

G. Tesauro and T. J. Sejnowski, A parallel network that learns to play backgammon, Artificial Intelligence, vol.39, issue.3, pp.357-390, 1989.
DOI : 10.1016/0004-3702(89)90017-9

J. Gerald, J. O. Tesauro, and . Kephart, Utility functions in autonomic systems, Proceedings of the 1st International Conference on Autonomic Computing, pp.70-77, 2004.