M. Azure, [3] Azure cli. https://azure.microsoft. com/en-us/documentation

C. Anglano and M. Canonico, Scheduling algorithms for multiple bag-oftask applications on desktop grids: A knowledge-free approach, 22nd IEEE Int. Symposium on Parallel and Distributed Processing (IPDPS), pp.1-8, 2008.

S. Blagodurov, A. Fedorova, E. Vinnik, T. Dwyer, and F. Hermenier, Multi-objective job placement in clusters, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis on, SC '15, pp.661-6612, 2015.
DOI : 10.1145/2807591.2807636

URL : https://hal.archives-ouvertes.fr/hal-01279724

D. Chang, J. H. Son, and M. Kim, Critical path identification in the context of a workflow, Information and Software Technology, vol.44, issue.7, pp.405-417, 2002.
DOI : 10.1016/S0950-5849(02)00025-3

W. Chen, R. F. Da-silva, E. Deelman, and R. Sakellariou, Balanced Task Clustering in Scientific Workflows, 2013 IEEE 9th International Conference on e-Science, pp.188-195, 2013.
DOI : 10.1109/eScience.2013.40

R. Coutinho, L. Drummond, Y. Frota, D. De-oliveira, and K. Ocana, Evaluating Grasp-based cloud dimensioning for comparative genomics: A practical approach, 2014 IEEE International Conference on Cluster Computing (CLUSTER), pp.371-379, 2014.
DOI : 10.1109/CLUSTER.2014.6968789

D. De-oliveira, K. A. Ocaña, F. Baião, and M. Mattoso, A Provenance-based Adaptive Scheduling Heuristic for Parallel Scientific Workflows in Clouds, Journal of Grid Computing, vol.37, issue.Database issue, pp.521-552, 2012.
DOI : 10.1007/s10723-012-9227-2

E. Deelman, D. Gannon, M. Shields, and I. Taylor, Workflows and e-Science: An overview of workflow system features and capabilities, Future Generation Computer Systems, vol.25, issue.5, pp.528-540, 2009.
DOI : 10.1016/j.future.2008.06.012

R. Duan, R. Prodan, and X. Li, Multi-Objective Game Theoretic Schedulingof Bag-of-Tasks Workflows on Hybrid Clouds, IEEE Transactions on Cloud Computing, vol.2, issue.1, pp.29-42, 2014.
DOI : 10.1109/TCC.2014.2303077

K. Etminani and M. Naghibzadeh, A Min-Min Max-Min selective algorihtm for grid task scheduling, 2007 3rd IEEE/IFIP International Conference in Central Asia on Internet, pp.1-7, 2007.
DOI : 10.1109/CANET.2007.4401694

R. Littauer, K. Ram, B. Ludäscher, W. Michener, and R. Koskela, Trends in Use of Scientific Workflows: Insights from a Public Repository and Recommendations for Best Practice, International Journal of Digital Curation, vol.7, issue.2, pp.92-100, 2012.
DOI : 10.2218/ijdc.v7i2.232

J. Liu, E. Pacitti, P. Valduriez, and M. Mattoso, Parallelization of scientific workflows in the cloud, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01024101

J. Liu, V. Silva, E. Pacitti, P. Valduriez, and M. Mattoso, Scientific workflow partitioning in multi-site clouds, BigDataCloud'2014: 3rd Workshop on Big Data Management in Clouds in conjunction with Euro-Par 2014, p.12, 2014.
URL : https://hal.archives-ouvertes.fr/lirmm-01073613

J. Liu, E. Pacitti, P. Valduriez, and M. Mattoso, A Survey of Data-Intensive Scientific Workflow Management, Journal of Grid Computing, vol.1, issue.Webserver-Issue, pp.1-37, 2015.
DOI : 10.1007/s10723-015-9329-8

URL : https://hal.archives-ouvertes.fr/lirmm-01144760

M. Maheswaran, S. Ali, H. J. Siegel, D. Hensgen, and R. F. Freund, Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems, Proceedings. Eighth Heterogeneous Computing Workshop (HCW'99), p.30, 1999.
DOI : 10.1109/HCW.1999.765094

R. T. Marler and J. S. Arora, Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, pp.369-395, 2004.

K. A. Oca¯-na, D. De-oliveira, F. Horta, J. Dias, E. Ogasawara et al., Exploring Molecular Evolution Reconstruction Using a Parallel Cloud Based Scientific Workflow, In Advances in Bioinformatics and Computational Biology Lecture Notes in Computer Science, vol.7409, pp.179-191, 2012.
DOI : 10.1007/978-3-642-31927-3_16

E. S. Ogasawara, J. Dias, V. Silva, F. S. Chirigati, D. De-oliveira et al., Chiron: a parallel engine for algebraic scientific workflows, Concurrency and Computation: Practice and Experience, pp.252327-2341, 2013.
DOI : 10.1002/cpe.3032

URL : https://hal.archives-ouvertes.fr/lirmm-00806557

D. D. Oliveira, K. A. Ocaña, E. Ogasawara, J. Dias, J. Gonçalves et al., Performance evaluation of parallel strategies in public clouds: A study with phylogenomic workflows, Ozsu and P. Valduriez. Principles of Distributed Database Systems, pp.1816-1825, 2013.
DOI : 10.1016/j.future.2012.12.019

M. Rahman, M. R. Hassan, R. Ranjan, and R. Buyya, Adaptive workflow scheduling for dynamic grid and cloud computing environment. Concurrency and Computation: Practice and Experience, pp.1816-1842, 2013.

M. A. Rodriguez and R. Buyya, A Responsive Knapsack-Based Algorithm for Resource Provisioning and Scheduling of Scientific Workflows in Clouds, 2015 44th International Conference on Parallel Processing, p.2015
DOI : 10.1109/ICPP.2015.93

I. Sardiña, C. Boeres, L. De, and A. Drummond, An efficient weighted biobjective scheduling algorithm for heterogeneous systems, Euro-Par 2009 ? Parallel Processing Workshops, pp.102-111, 2010.

S. Smanchat, M. Indrawan, S. Ling, C. Enticott, and D. Abramson, Scheduling Multiple Parameter Sweep Workflow Instances on the Grid, 2009 Fifth IEEE International Conference on e-Science, pp.300-306, 2009.
DOI : 10.1109/e-Science.2009.49

X. Sun and Y. Chen, Reevaluating Amdahl???s law in the multicore era, Journal of Parallel and Distributed Computing, vol.70, issue.2, pp.183-188, 2010.
DOI : 10.1016/j.jpdc.2009.05.002

H. Topcuouglu, S. Hariri, and M. Wu, Performance-effective and low-complexity task scheduling for heterogeneous computing, IEEE Transactions on Parallel and Distributed Systems, vol.13, issue.3, pp.260-274, 2002.
DOI : 10.1109/71.993206

M. Wieczorek, R. Prodan, and T. Fahringer, Scheduling of scientific workflows in the ASKALON grid environment, ACM SIGMOD Record, vol.34, issue.3, pp.56-62, 2005.
DOI : 10.1145/1084805.1084816

J. Yu, R. Buyya, and C. K. Tham, Cost-based scheduling of scientific workflow applications on utility grids, First Int. Conf. on e-Science and Grid Computing, pp.140-147, 2005.

Z. Yu and W. Shi, An Adaptive Rescheduling Strategy for Grid Workflow Applications, 2007 IEEE International Parallel and Distributed Processing Symposium, pp.1-8, 2007.
DOI : 10.1109/IPDPS.2007.370305

L. Zadeh, Optimality and non-scalar-valued performance criteria, IEEE Transactions on Automatic Control, vol.8, issue.1, pp.59-60, 1963.
DOI : 10.1109/TAC.1963.1105511