J. Beran, Statistics for long-memory processes, of Monographs on Statistics and Applied Probability. Chapman and Hall, 1994.

R. N. Bhattacharya, V. K. Gupta, and E. Waymire, The Hurst effect under trends, Journal of Applied Probability, vol.19, issue.03, pp.649-662, 1983.
DOI : 10.1029/WR004i005p00909

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, 1996.
DOI : 10.1007/BF00058655

J. Brevik, D. Nurmi, and R. Wolski, Predicting Bounds on Queuing Delay in Space-shared Computing Environments, 2006 IEEE International Symposium on Workload Characterization, pp.213-224, 2006.
DOI : 10.1109/IISWC.2006.302746

P. Burns, Robustness of the Ljung-Box Test and its Rank Equivalent, The Journal of Derivatives, pp.7-18, 2002.
DOI : 10.2139/ssrn.443560

R. A. Davis, T. Lee, and G. Rodriguez-yam, Structural Break Estimation for Nonstationary Time Series Models, Journal of the American Statistical Association, vol.101, issue.473, pp.229-239, 2006.
DOI : 10.1198/016214505000000745

F. X. Diebold and A. Inoue, Long memory and regime switching, Journal of Econometrics, vol.105, issue.1, pp.131-159, 2001.
DOI : 10.1016/S0304-4076(01)00073-2

P. A. Dinda and D. R. O-'hallaron, Host load prediction using linear models, Cluster Computing, vol.3, issue.4, pp.265-280, 2000.
DOI : 10.1023/A:1019048724544

A. B. Downey, Using queue time predictions for processor allocation, IPPS'97, JSSPP'97, pp.35-57, 1997.
DOI : 10.1007/3-540-63574-2_15

B. Efron, Bootstrap Methods: Another Look at the Jackknife, The Annals of Statistics, vol.7, issue.1, pp.1-26, 1979.
DOI : 10.1214/aos/1176344552

T. Elteto, C. Germain-renaud, P. Bondon, and M. Sebag, Discovering Piecewise Linear Models of Grid Workload, 10th IEEE/ACM Int. Symp. on Cluster, Cloud and Grid Computing, pp.474-484, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00491562

C. Germain-renaud, The Grid Observatory, 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2011.
DOI : 10.1109/CCGrid.2011.68
URL : https://hal.archives-ouvertes.fr/inria-00586502

E. Laure, Programming the Grid with gLite*, Computational Methods in Science and Technology, vol.12, issue.1, pp.33-45, 2006.
DOI : 10.12921/cmst.2006.12.01.33-45

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

M. Lassnig, Identification, Modelling and Prediction of Non-periodic Bursts in Workloads, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp.485-494, 2010.
DOI : 10.1109/CCGRID.2010.118

S. Andreozzi, Glue Schema Specification, V1.3, Open Grid Forum, 2008.

P. Fearnhead, Exact Bayesian curve fitting and signal segmentation, IEEE Transactions on Signal Processing, vol.53, issue.6, pp.2160-2166, 2005.
DOI : 10.1109/TSP.2005.847844

C. W. Granger and N. Hyung, Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns, Journal of Empirical Finance, vol.11, issue.3, pp.399-421, 2004.
DOI : 10.1016/j.jempfin.2003.03.001

C. W. Granger and R. Joyeux, AN INTRODUCTION TO LONG-MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING, Journal of Time Series Analysis, vol.7, issue.1, pp.15-29, 1980.
DOI : 10.2307/3212527

J. R. Hosking, Fractional differencing, Biometrika, vol.68, issue.1, pp.165-176, 1981.
DOI : 10.1093/biomet/68.1.165

M. C. Huebscher and J. A. Mccann, A survey of autonomic computing???degrees, models, and applications, ACM Computing Surveys, vol.40, issue.3, pp.1-728, 2008.
DOI : 10.1145/1380584.1380585

R. Gott and I. , Implications of the Copernican principle for our future prospects, Nature, vol.363, issue.6427, pp.315-319, 1993.
DOI : 10.1038/363315a0

L. Ilija?i´ilija?i´c and L. Saitta, Characterization of a computational grid as a complex system, Procs. of GMAC '09, pp.9-18, 2009.

S. Jha, M. Parashar, and O. Rana, Investigating autonomic behaviours in gridbasedcomputational science applications, Proceedings of GMAC'09, pp.29-38, 2009.

G. Kitagawa and H. Akaike, A procedure for the modeling of non-stationary time series, Annals of the Institute of Statistical Mathematics, vol.15, issue.1, pp.351-363, 1978.
DOI : 10.1007/BF02480225

A. Lagana and . Compchem, COMPCHEM: Progress Towards GEMS a Grid Empowered Molecular Simulator and Beyond, Journal of Grid Computing, vol.181, issue.3???4, pp.571-586, 2010.
DOI : 10.1007/s10723-010-9164-x

B. Lee and J. M. Schopf, Run-time prediction of parallel applications on shared environments, CLUSTER, pp.487-491, 2003.

T. Lee and Y. Yang, Bagging binary and quantile predictors for time series, Journal of Econometrics, vol.135, issue.1-2, pp.465-497, 2006.
DOI : 10.1016/j.jeconom.2005.07.017

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

D. Lingrand, T. Glatard, and J. Montagnat, Modeling the latency on production grids with respect to the execution context, Parallel Computing, vol.35, issue.10-11, pp.493-511, 2009.
DOI : 10.1016/j.parco.2009.07.003
URL : https://hal.archives-ouvertes.fr/hal-00459261

M. Macias, O. Rana, G. Smith, J. Guitart, and . Torres, Maximising revenue in grid markets using an economically enhanced resource manager, Concurrency and Computation: Practice and Experience, 2008.

J. Meng, S. T. Chakradhar, and A. Raghunathan, Best-effort parallel execution framework for recognition and mining applications, IPDPS, pp.1-12, 2009.

N. Mi, G. Casale, L. Cherkasova, and E. Smirni, Injecting realistic burstiness to a traditional client-server benchmark, Proceedings of the 6th international conference on Autonomic computing, ICAC '09, pp.149-158, 2009.
DOI : 10.1145/1555228.1555267

T. N. Minh, L. Wolters, and D. Epema, A Realistic Integrated Model of Parallel System Workloads, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp.464-473, 2010.
DOI : 10.1109/CCGRID.2010.32

A. Mutz, R. Wolski, and J. Brevik, Eliciting honest value information in a batch-queue environment, 2007 8th IEEE/ACM International Conference on Grid Computing, pp.291-297, 2007.
DOI : 10.1109/GRID.2007.4354145

F. Nadeem, M. M. Yousaf, R. Prodan, and T. Fahringer, Soft Benchmarks-Based Application Performance Prediction Using a Minimum Training Set, 2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science'06), 2006.
DOI : 10.1109/E-SCIENCE.2006.261155

J. Perez, C. Germain-renaud, B. Kégl, and C. Loomis, Utility-based reinformcement learning for reactive grids, The 5th IEEE ICAC Autonomic Computing, 2008.

J. Perez, C. Germain-renaud, B. Kégl, and C. Loomis, Multi-objective Reinforcement Learning for Responsive Grids, Journal of Grid Computing, vol.39, issue.3, pp.473-492, 2010.
DOI : 10.1007/s10723-010-9161-0
URL : https://hal.archives-ouvertes.fr/hal-00491560

A. Pugliese, D. Talia, and R. Yahyapour, Modeling and Supporting Grid Scheduling, Journal of Grid Computing, vol.34, issue.3, pp.195-213, 2008.
DOI : 10.1007/s10723-007-9083-7

R. Raman, M. Livny, and M. Solomon, Matchmaking: distributed resource management for high throughput computing, Proceedings. The Seventh International Symposium on High Performance Distributed Computing (Cat. No.98TB100244), pp.140-147, 1998.
DOI : 10.1109/HPDC.1998.709966

I. Rish, R. Das, G. Tesauro, and J. Kephart, Autonomic computing: A new challenge for machine learning, 2006.

J. Rissanen, Stochastic Complexity in Statistical Inquiry, World Scientific, 1989.
DOI : 10.1142/0822

E. Rogers, Diffusion of innovations, 1983.

H. J. Skaug and D. Tjostheim, Testing for Serial Independence Using Measures of Distance between Densities, Athens Conference on Applied Probability and Time Series, Springer Lecture Notes in Statistics 115, 1996.
DOI : 10.1007/978-1-4612-2412-9_27

W. Smith, V. E. Taylor, and I. T. Foster, Using Run-Time Predictions to Estimate Queue Wait Times and Improve Scheduler Performance, IPPS/SPDP '99, JSSPP'99, pp.202-219, 1999.
DOI : 10.1007/3-540-47954-6_11

O. Sonmez, N. Yigitbasi, A. Iosup, and D. Epema, Trace-based evaluation of job runtime and queue wait time predictions in grids, Proceedings of the 18th ACM international symposium on High performance distributed computing, HPDC '09, 2009.
DOI : 10.1145/1551609.1551632

R. S. Sutton, A. G. Barto, and R. J. Williams, Reinforcement learning is direct adaptive optimal control, American Control Conference, pp.2143-2146, 1992.
DOI : 10.1109/37.126844

M. S. Taqqu and V. Teverovsky, Robustness of whittle-type estimators for time series with long-range dependence, Communications in Statistics. Stochastic Models, vol.115, issue.4, pp.723-757, 1997.
DOI : 10.1214/ss/1177010131

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, Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies, IEEE Internet Computing, vol.11, issue.1, pp.22-30, 2007.
DOI : 10.1109/MIC.2007.21

V. Teverovsky and M. Taqqu, Testing for long-range dependence in the presence of shifting means or a slowly declining trend, using a variance-type estimator, Journal of Time Series Analysis, vol.18, issue.3, pp.279-304, 1997.
DOI : 10.1111/1467-9892.00050

D. Thain, J. Bent, A. Arpaci-dusseau, R. Arpaci-dusseau, and M. Livny, Gathering at the well, Proceedings of the 2001 ACM/IEEE conference on Supercomputing (CDROM) , Supercomputing '01, 2001.
DOI : 10.1145/582034.582092

R. Wolski, N. T. Spring, and J. Hayes, Predicting the CPU availability of time-shared Unix systems on the computational grid, Proceedings. The Eighth International Symposium on High Performance Distributed Computing (Cat. No.99TH8469), pp.293-301, 2000.
DOI : 10.1109/HPDC.1999.805288

L. Yang, J. M. Schopf, and I. Foster, Conservative Scheduling, Proceedings of the 2003 ACM/IEEE conference on Supercomputing, SC '03, p.31, 2003.
DOI : 10.1145/1048935.1050182

Y. Yao, Estimating the number of change-points via Schwarz' criterion, Statistics & Probability Letters, vol.6, issue.3, pp.181-189, 1988.
DOI : 10.1016/0167-7152(88)90118-6

X. Zhang, C. Furtlehner, J. Perez, C. Germain-renaud, and M. Sebag, Toward autonomic grids, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pp.987-996, 2009.
DOI : 10.1145/1557019.1557126
URL : https://hal.archives-ouvertes.fr/inria-00393825