L. Bottou, Stochastic Learning, Advanced lectures on machine learning, 2004.
DOI : 10.1007/978-1-4757-2440-0

R. Duan, F. Nadeem, J. Wang, Y. Zhang, R. Prodan et al., A Hybrid Intelligent Method for Performance Modeling and Prediction of Workflow Activities in Grids, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, 2009.
DOI : 10.1109/CCGRID.2009.58

D. G. Feitelson, Metrics for Parallel Job Scheduling and Their Convergence, Job Scheduling Strategies for Parallel Processing, 2001.
DOI : 10.1007/3-540-45540-X_11

D. G. Feitelson, D. Tsafrir, and D. Krakov, Experience with using the Parallel Workloads Archive, Journal of Parallel and Distributed Computing, vol.74, issue.10, 2014.
DOI : 10.1016/j.jpdc.2014.06.013

E. Frachtenberg and D. G. Feitelson, Pitfalls in Parallel Job Scheduling Evaluation, Job Scheduling Strategies for Parallel Processing, 2005.
DOI : 10.1007/11605300_13

Y. Georgiou, Resource and Job Management in High Performance Computing
URL : https://hal.archives-ouvertes.fr/tel-01499598

R. Gibbons, A historical application profiler for use by parallel schedulers, Job Scheduling Strategies for Parallel Processing, 1997.
DOI : 10.1007/3-540-63574-2_16

D. A. Lifka, The ANL/IBM SP scheduling system, Job Scheduling Strategies for Parallel Processing, 1995.
DOI : 10.1007/3-540-60153-8_35

A. Matsunaga and J. Fortes, On the Use of Machine Learning to Predict the Time and Resources Consumed by Applications, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, 2010.
DOI : 10.1109/CCGRID.2010.98

C. Mendes and D. Reed, Integrated compilation and scalability analysis for parallel systems, Proceedings. 1998 International Conference on Parallel Architectures and Compilation Techniques (Cat. No.98EX192), 1998.
DOI : 10.1109/PACT.1998.727287

A. W. Mu-'alem and D. G. Feitelson, Utilization, predictability, workloads, and user runtime estimates in scheduling the ibm sp2 with backfilling. Parallel and Distributed Systems, 2001.

A. Nissimov, Locality and its usage in parallel job runtime distribution modeling using HMM, 2006.

A. Nissimov and D. G. Feitelson, Probabilistic Backfilling, Job Scheduling Strategies for Parallel Processing, 2008.
DOI : 10.1007/978-3-540-78699-3_6

B. Nitzberg, J. M. Schopf, and J. P. Jones, PBS Pro: Grid Computing and Scheduling Attributes, Grid resource management, 2004.
DOI : 10.1007/978-1-4615-0509-9_13

L. Rabiner, A tutorial on hidden markov models and selected applications in speech recognition, Proceedings of the IEEE, 1989.

S. Ross, P. Mineiro, and J. Langford, Normalized online learning, Uncertainty in Artificial Intelligence, 2013.

J. M. Schopf, F. Berman, J. M. Schopf, and F. Berman, Using stochastic intervals to predict application behavior on contended resources, Proceedings Fourth International Symposium on Parallel Architectures, Algorithms, and Networks (I-SPAN'99), 1999.
DOI : 10.1109/ISPAN.1999.778962

W. Smith, V. Foster, and . Taylor, Predicting application run times with historical information, Journal of Parallel and Distributed Computing, 2004.

G. Staples, Torque resource manager, Supercomputing, 2006.

D. Tsafrir, Y. Etsion, and D. G. Feitelson, Modeling User Runtime Estimates, Job Scheduling Strategies for Parallel Processing, 2005.
DOI : 10.1007/11605300_1

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.121.9332

D. Tsafrir, Y. Etsion, and D. G. Feitelson, Backfilling Using System-Generated Predictions Rather than User Runtime Estimates, IEEE Transactions on Parallel and Distributed Systems, vol.18, issue.6, 2007.
DOI : 10.1109/TPDS.2007.70606

A. B. Yoo, M. A. Jette, and M. Grondona, SLURM: Simple Linux Utility for Resource Management, Job Scheduling Strategies for Parallel Processing, 2003.
DOI : 10.1007/10968987_3