T. Guo, U. Sharma, T. Wood, S. Sahu, and P. Shenoy, Seagull: Intelligent cloud bursting for enterprise applications, Proceedings of the 2012 USENIX Conference on Annual Technical Conference, ser. USENIX ATC'12, pp.33-33
DOI : 10.1145/2602571

T. Kosar, E. Arslan, B. Ross, and B. Zhang, StorkCloud, Proceedings of the 4th ACM workshop on Scientific cloud computing, Science Cloud '13, pp.29-36, 2013.
DOI : 10.1145/2465848.2465855

N. Laoutaris, M. Sirivianos, X. Yang, and P. Rodriguez, Inter-datacenter bulk transfers with netstitcher, ACM SIGCOMM Computer Communication Review, vol.41, issue.4, pp.74-85, 2011.
DOI : 10.1145/2043164.2018446

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.208.6427

T. White, Hadoop: The Definitive Guide, 2010.

M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, Spark: Cluster computing with working sets, HotCloud'10: Proceedings of the 2Nd USENIX Conference on Hot Topics in Cloud Computing, pp.10-10, 2010.

J. Dean and S. Ghemawat, MapReduce, Communications of the ACM, vol.51, issue.1, pp.107-113, 2008.
DOI : 10.1145/1327452.1327492

T. Gunarathne, T. Wu, J. Qiu, and G. Fox, MapReduce in the Clouds for Science, 2010 IEEE Second International Conference on Cloud Computing Technology and Science, pp.565-572, 2010.
DOI : 10.1109/CloudCom.2010.107

X. Zhang, L. T. Yang, C. Liu, and J. Chen, A Scalable Two-Phase Top-Down Specialization Approach for Data Anonymization Using MapReduce on Cloud, IEEE Transactions on Parallel and Distributed Systems, vol.25, issue.2, pp.363-373, 2014.
DOI : 10.1109/TPDS.2013.48

B. Sharma, T. Wood, and C. R. Das, HybridMR: A Hierarchical MapReduce Scheduler for Hybrid Data Centers, 2013 IEEE 33rd International Conference on Distributed Computing Systems, pp.102-111, 2013.
DOI : 10.1109/ICDCS.2013.31

A. Abouzeid, K. Bajda-pawlikowski, D. Abadi, A. Silberschatz, and A. Rasin, HadoopDB, Proceedings of the VLDB Endowment, pp.922-933, 2009.
DOI : 10.14778/1687627.1687731

K. Shirahata, H. Sato, and S. Matsuoka, Hybrid Map Task Scheduling for GPU-Based Heterogeneous Clusters, 2010 IEEE Second International Conference on Cloud Computing Technology and Science, pp.733-740, 2010.
DOI : 10.1109/CloudCom.2010.55

M. M. Rafique, A. R. Butt, and D. S. Nikolopoulos, A capabilities-aware framework for using computational accelerators in data-intensive computing, Journal of Parallel and Distributed Computing, vol.71, issue.2, pp.185-197, 2011.
DOI : 10.1016/j.jpdc.2010.09.004

M. M. Rafique, B. Rose, A. R. Butt, and D. S. Nikolopoulos, CellMR: A framework for supporting mapreduce on asymmetric cell-based clusters, 2009 IEEE International Symposium on Parallel & Distributed Processing, pp.1-12, 2009.
DOI : 10.1109/IPDPS.2009.5161062

T. Bicer, D. Chiu, and G. Agrawal, A Framework for Data-Intensive Computing with Cloud Bursting, 2011 IEEE International Conference on Cluster Computing, pp.169-177, 2011.
DOI : 10.1109/CLUSTER.2011.21

M. Mattess, R. N. Calheiros, and R. Buyya, Scaling MapReduce Applications Across Hybrid Clouds to Meet Soft Deadlines, 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA), pp.629-636, 2013.
DOI : 10.1109/AINA.2013.51

H. Zhang, G. Jiang, K. Yoshihira, and H. Chen, Proactive Workload Management in Hybrid Cloud Computing, IEEE Transactions on Network and Service Management, vol.11, issue.1, pp.90-100, 2014.
DOI : 10.1109/TNSM.2013.122313.130448

M. Cardosa, C. Wang, A. Nangia, A. Chandra, and J. Weissman, Exploring MapReduce efficiency with highly-distributed data, Proceedings of the second international workshop on MapReduce and its applications, MapReduce '11, pp.27-34, 2011.
DOI : 10.1145/1996092.1996100

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.221.5250

B. Heintz, A. Chandra, and J. Weissman, Cross-Phase Optimization in MapReduce, pp.277-302

B. Nicolae, P. Riteau, and K. Keahey, Bursting the Cloud Data Bubble: Towards Transparent Storage Elasticity in IaaS Clouds, 2014 IEEE 28th International Parallel and Distributed Processing Symposium, pp.135-144, 2014.
DOI : 10.1109/IPDPS.2014.25

URL : https://hal.archives-ouvertes.fr/hal-00947599

H. Ohnaga, K. Aida, and O. Abdul-rahman, Performance of Hadoop Application on Hybrid Cloud, 2015 International Conference on Cloud Computing Research and Innovation (ICCCRI), pp.130-138, 2015.
DOI : 10.1109/ICCCRI.2015.25

R. Roman, B. Nicolae, A. Costan, and G. Antoniu, Understanding Spark Performance in Hybrid and Multi-Site Clouds, BDAC'15: 6th International Workshop on Big Data Analytics, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01239140

K. Ousterhout, R. Rasti, S. Ratnasamy, S. Shenker, and B. Chun, Making sense of performance in data analytics frameworks, NSDI'15: The 12th USENIX Conference on Networked Systems Design and Implementation, pp.293-307, 2015.

F. J. Clemente-castelló, B. Nicolae, K. Katrinis, M. M. Rafique, R. Mayo et al., Enabling big data analytics in the hybrid cloud using iterative mapreduce, UCC'15: 8th International Conference on Utility and Cloud Computing, pp.290-299, 2015.

K. Shvachko, H. Huang, S. Radia, and R. Chansler, The Hadoop Distributed File System, 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), 2010.
DOI : 10.1109/MSST.2010.5496972

S. Godard, Sysstat: System performance tools for the linux os, 2004.

V. Pillet, J. Labarta, T. Cortes, and S. Girona, Paraver: A tool to visualize and analyze parallel code, Proceedings of WoTUG-18: Transputer and occam Developments, pp.17-31, 1995.