G. Aupy, A. Benoit, F. Dufossé, and Y. Robert, Reclaiming the energy of a schedule: models and algorithms, Concur. Comput.: Pract. Exp, vol.25, issue.11, pp.1505-1523, 2013.
URL : https://hal.archives-ouvertes.fr/inria-00584944

H. M. Fard, R. Prodan, J. J. Barrionuevo, and T. Fahringer, A multi-objective approach for workflow scheduling in heterogeneous environments, Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID 2012), pp.300-309, 2012.

R. Griessl, M. Peykanu, J. Hagemeyer, M. Porrmann, S. Krupop et al., FPGA-accelerated heterogeneous hyperscale server architecture for next-generation compute clusters, 2015.

, IBM: IBM ILOG CPLEX V12.5 user's manual for CPLEX, 2013.

Y. C. Lee and A. Y. Zomaya, Minimizing energy consumption for precedenceconstrained applications using dynamic voltage scaling, 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, pp.92-99, 2009.

L. Zaourar, M. Ait-aba, D. Briand, and J. M. Philippe, Modeling of applications and hardware to explore task mapping and scheduling strategies on a heterogeneous micro-server system, 2017.
URL : https://hal.archives-ouvertes.fr/cea-01838133

M. Mezmaz, N. Melab, Y. Kessaci, Y. C. Lee, E. G. Talbi et al., A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems, J. Parallel Distrib. Comput, vol.71, issue.11, pp.1497-1508, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00639966

H. F. Sheikh and I. Ahmad, Efficient heuristics for joint optimization of performance, energy, and temperature in allocating tasks to multi-core processors, 2014 International Green Computing Conference (IGCC), pp.1-8, 2014.

K. M. Tarplee, R. Friese, A. A. Maciejewski, and H. J. Siegel, Efficient and scalable pareto front generation for energy and makespan in heterogeneous computing systems, Recent Advances in Computational Optimization. SCI, vol.580, pp.161-180, 2015.

K. M. Tarplee, R. Friese, A. A. Maciejewski, H. J. Siegel, and E. K. Chong, Energy and makespan tradeoffs in heterogeneous computing systems using efficient linear programming techniques, IEEE Trans. Parallel Distrib. Syst, vol.27, issue.6, pp.1633-1646, 2016.

O. C. Vasquez-perez, Ordonnancement de tâches pour concilier la minimisation de la consommation d'´ energie avec la qualité de service: optimisation et théorie des jeux, vol.6, 2014.

G. Xie, X. Xiao, R. Li, and K. Li, Schedule length minimization of parallel applications with energy consumption constraints using heuristics on heterogeneous distributed systems, Concurr. Comput.: Pract. Exp, 2016.

B. D. Young, S. Pasricha, A. A. Maciejewski, H. J. Siegel, and J. T. Smith, Heterogeneous makespan and energy-constrained DAG scheduling, Proceedings of the 2013 Workshop on Energy Efficient High Performance Parallel and Distributed Computing, pp.3-12, 2013.

L. Zhang, K. Li, C. Li, and K. Li, Bi-objective workflow scheduling of the energy consumption and reliability in heterogeneous computing systems, Inf. Sci, vol.379, pp.241-256, 2017.

L. Zhang, K. Li, Y. Xu, J. Mei, F. Zhang et al., Maximizing reliability with energy conservation for parallel task scheduling in a heterogeneous cluster, Inf. Sci, vol.319, pp.113-131, 2015.

X. Zhong and C. Z. Xu, Energy-aware modeling and scheduling for dynamic voltage scaling with statistical real-time guarantee, IEEE Trans. Comput, vol.56, issue.3, pp.358-372, 2007.