References
[1]. Abdelhafez, A., Alba, E., & Luque, G. (2019).
Performance analysis of synchronous and asynchronous
distributed genetic algorithms on multiprocessors. Swarm
and Evolutionary Computation, 49, 147-157. https://doi.
org/10.1016/j.swevo.2019.06.003
[2]. Albers, S., Bampis, E., Letsios, D., Lucarelli, G., & Stotz,
R. (2017). Scheduling on power-heterogeneous
processors. Information and Computation, 257, 22-33.
https://doi.org/10.1016/j.ic.2017.09.013
[3]. Audsley, N. C., Burns, A., Richardson, M. F., & Wellings,
A. J. (1994). STRESS: A simulator for hard real‐time systems.
Software: Practice and Experience, 24(6), 543-564.
https://doi.org/10.1002/spe.4380240603
[4]. Baek, H., Lee, J., & Shin, I. (2018). Multi-level
contention-free policy for real-time multiprocessor
scheduling. Journal of Systems and Software, 137, 36-49. https://doi.org/10.1016/j.jss.2017.11.027
[5]. Bhuiyan, A., Guo, Z., Saifullah, A., Guan, N., & Xiong, H.
(2018). Energy-efficient real-time scheduling of DAG tasks.
ACM Transactions on Embedded Computing Systems
(TECS), 17(5), 1-25. https://doi.org/10.1145/3241049
[6]. Blumenthal, J., Hildebrandt, J., Golatowski, F., &
Timmermann, D. (2003). YASA-A Framework for Validation,
Test, and Analysis of Real-Time Scheduling Algorithms. In
th Proceedings of 5 Real-Time Linux Workshop (pp. 197-204).
[7]. Bose, A., Biswas, T., & Kuila, P. (2019). A novel genetic
algorithm based scheduling for multi-core systems. In
Smart Innovations in Communication and Computational
Sciences (pp. 45-54). Springer, Singapore. https://doi.org/
10.1007/978-981-13-2414-7_5
[8]. Chandarli, Y., Fauberteau, F., Masson, D., Midonnet,
S., & Qamhieh, M. (2012, July). Yartiss: A tool to visualize,
test, compare and evaluate real-time scheduling
rd algorithms. Proceedings of the 3 International Workshop
on Analysis Tools and Methodologies for Embedded and
Real-time Systems, Pisa (Italy).
[9]. Chasapis, D., Moretó, M., Schulz, M., Rountree, B.,
Valero, M., & Casas, M. (2019, June). Power efficient job
scheduling by predicting the impact of processor
manufacturing variability. In Proceedings of the ACM
International Conference on Supercomputing (pp. 296-
307). https://doi.org/10.1145/3330345.3330372
[10]. Chéramy, M., Hladik, P. E., & Déplanche, A. M.
(2014, July). Simso: A simulation tool to evaluate real-time
th multiprocessor scheduling algorithms. In 5 International
Workshop on Analysis Tools and Methodologies for
Embedded and Real-time Systems (WATERS) (pp. 1-6).
[11]. Courbin, P., & George, L. (2011). Fortas: Framework
for real-time analysis and simulation. Proceedings of the
nd 2 International Workshop on Analysis Tools and
Methodologies for Embedded and Real-time Systems
(pp. 21-26).
[12]. De Vroey, S., Goossens, J., & Hernalsteen, C. (1996,
April). A generic simulator of real-time scheduling
th algorithms. In Proceedings of the 29 Annual Simulation
Symposium (pp. 242-249). IEEE. https://doi.org/10.1109/
SIMSYM.1996.492172
[13]. Diaz, A., Batista, R., & Castro, O. (2007, September).
th Realtss: a real-time scheduling simulator. In 2007, 4
International Conference on Electrical and Electronics
Engineering (pp. 165-168). IEEE. https://doi.org/10.11
09/ICEEE.2007.4344998
[14]. Díaz-Ramírez, A., Orduño, D. K., & Mejía-Alvarez, P.
(2012, February). A multiprocessor real-time scheduling
nd simulation tool. In CONIELECOMP 2012, 22 International
Conference on Electrical Communications and
Computers (pp. 157-161). IEEE. https://doi.org/10.1109/
CONIELECOMP.2012.6189901
[15]. Edun, A., Vazquez, R., Gordon-Ross, A., & Stitt, G.
(2019, March). Dynamic scheduling on heterogeneous
multicores. In 2019, Design, Automation & Test in Europe
Conference & Exhibition (DATE) (pp. 1685-1690). IEEE.
https://doi.org/10.23919/DATE.2019.8714804
[16]. Hangan, A., & Sebestyen, G. (2012, July).
RTMultiSim: A versatile simulator for multiprocessor realrd
time systems. In Proceedings of the 3 International
Workshop on Analysis Tools and Methodologies for
Embedded and Real-time Systems, Pisa, Italy.
[17]. Harbour, M. G., García, J. G., Gutiérrez, J. P., &
Moyano, J. D. (2001, June). Mast: Modeling and analysis
th suite for real time applications. In Proceedings 13
Euromicro Conference on Real-Time Systems (pp. 125-
134). IEEE.
[18]. He, Q., Guan, N., & Guo, Z. (2019). Intra-task priority
assignment in real-time scheduling of dag tasks on multicores.
IEEE Transactions on Parallel and Distributed
Systems, 30(10), 2283-2295. https://doi.org/10.1080/00
207543.2018.1497312
[19]. Jakovljevic, G., Rakamaric, Z., & Babic, D. (2002,
June). Java simulator of real-time scheduling algorithms.
th In ITI 2002 Proceedings of the 24 International
Conference on Information Technology Interfaces (pp.
411-416). IEEE. https://doi.org/10.1109/ITI.2002.1024708
[20]. Ji, M., Zhang, W., Liao, L., Cheng, T. C. E., & Tan, Y.
(2019). Multitasking parallel-machine scheduling with
machine-dependent slack due-window assignment.
International Journal of Production Research, 57(6),
1667-1684.
[21]. Jin, S., Schiavone, G., & Turgut, D. (2008). A
performance study of multiprocessor task scheduling
algorithms. The Journal of Supercomputing, 43(1), 77-97.
https://doi.org/10.1007/s11227-007-0139-z
[22]. Juarez, F., Ejarque, J., & Badia, R. M. (2018).
Dynamic energy-aware scheduling for parallel taskbased
application in cloud computing. Future
Generation Computer Systems, 78, 257-271. https://doi.
org/10.1016/j.future.2016.06.029
[23]. Khalib, Z., Ahmad, B., & Bi, O. (2012, September).
Performance analysis of a non-preemptive dynamic soft
real time scheduler using discrete event simulator. In 2012
Fourth International Conference on Computational
Intelligence, Modelling and Simulation (pp. 182-187).
IEEE. https://doi.org/10.1109/CIMSim.2012.19
[24]. Leupers, R., Aguilar, M. A., Castrillon, J., & Sheng, W.
(2019). Software compilation techniques for
heterogeneous embedded multi-core systems. In
Handbook of Signal Processing Systems (pp. 1021-1062).
Springer, Cham. https://doi.org/10.1007/978-3-319-9173
4-4_28
[25]. Manacero, A., Miola, M. B., & Nabuco, V. A. (2001,
October). Teaching real-time with a scheduler simulator.
st In 31 Annual Frontiers in Education Conference (Vol. 2,
pp. T4D15-T4D19. IEEE. https://doi.org/10.1109/FIE.2001.9
63651
[26]. Nikolic, B., Awan, M. A., & Petters, S. M. (2011,
November). SPARTS: Simulator for power aware and realth
time systems. In 2011, IEEE 10 International Conference
on Trust, Security and Privacy in Computing and
Communications (pp. 999-1004). IEEE. https://doi.org/
10.1109/TrustCom.2011.137
[27]. Öztop, H., Tasgetiren, M. F., Eliiyi, D. T., & Pan, Q. K.
(2018, August). Green permutation flowshop scheduling:
A trade-off-between energy consumption and total flow
time. In International Conference on Intelligent
Computing (pp. 753-759). Springer, Cham. https://doi.
org/10.1007/978-3-319-95957-3_79
[28]. Pillai, A. S., & Isha, T. B. (2013, December). ERTSim: An
embedded real-time task simulator for scheduling. In
2013, IEEE International Conference on Computational
Intelligence and Computing Research (pp. 1-4). IEEE.
https://doi.org/10.1109/ICCIC.2013.6724195
[29]. Pillai, A. S., & Isha, T. B. (2014, March). Optimal task
allocation and scheduling for power saving in
multiprocessor systems. In 2014, Power and Energy
Systems: Towards Sustainable Energy (pp. 1-5). IEEE.
https://doi.org/10.1109/PESTSE.2014.6805324
[30]. Qin, Y., Zeng, G., Kurachi, R., Li, Y., Matsubara, Y., &
Takada, H. (2019). Energy-efficient intra-task dvfs
scheduling using linear programming formulation. IEEE
Access, 7, 30536-30547. https://doi.org/10.1109/ACCESS
.2019.2902353
[31]. Reddy, M. S., Ratnam, C., Rajyalakshmi, G., &
Manupati, V. K. (2018). An effective hybrid multi objective
evolutionary algorithm for solving real time event in
flexible job shop scheduling problem. Measurement,
114, 78-90. https://doi.org/10.1016/j.measurement.
2017.09.022
[32]. Rivas Concepción, J. M., Gutiérrez García, J. J., &
González Harbour, M. (2014). GEN4MAST: A tool for the
evaluation of real-time techniques using a
rd supercomputer. In Proceedings of 3 International
Workshop on Real Time and Distributed Computing in
Emerging Applications Co-located with 34th IEEE Real
Time Systems Symposium (pp. 41-47).
[33]. Sahoo, R. M., & Padhy, S. K. (2019, August).
Improved crow search optimization for multiprocessor
task scheduling: A novel approach. In International
Conference on Application of Robotics in Industry using
Advanced Mechanisms (pp. 1-13). Springer, Cham.
https://doi.org/10.1007/978-3-030-30271-9_1
[34]. Salimi, S., Mawlana, M., & Hammad, A. (2018).
Performance analysis of simulation-based optimization of
construction projects using high per formance
computing. Automation in Construction, 87, 158-172.
https://doi.org/10.1016/j.autcon.2017.12.003
[35]. Sensini, F., Buttazzo, G., & Ancilotti, P. (1997, June).
Ghost: A tool for simulation and analysis of real-time
scheduling algorithms. In Proceedings of the IEEE Real-
Time Educational Workshop (pp. 42-49). https://doi.org/10.
1109/EMRTS.2001.934015
[36]. Shin, D., Kim, W., Jeon, J., Kim, J., & Min, S. L. (2002,
February). SimDVS: An integrated simulation environment
for performance evaluation of dynamic voltage scaling
algorithms. In International Workshop on Power-Aware
Computer Systems (pp. 141-156). Springer: Berlin,
Heidelberg. https://doi.org/10.1007/3-540-36612-1_10
[37]. Short, M. (2017). Timing analysis for embedded
systems using non-preemptive EDF scheduling under
bounded error arrivals. Applied Computing and
Informatics, 13(2), 130-139. https://doi.org/10.1016/j.acI.
2016.07.001
[38]. Singhoff, F., Legrand, J., Nana, L., & Marcé, L. (2004,
November). Cheddar: A flexible real time scheduling
framework. In Proceedings of the 2004 Annual ACM SIGAda
International Conference on Ada: The engineering of
correct and reliable software for real-time & distributed
systems using Ada and related technologies (pp. 1-8).
https://doi.org/10.1145/1032297.1032298
[39]. Sreenath, M., & Sukumar, P. (2013). Amalgamate
scheduling of real-time tasks and effective utilization on
multiprocessors with work-stealing. The International Journal
of Engineering and Science (IJES), 2(1), 293-298.
[40]. Strohbach, M., Gellersen, H. W., Kortuem, G., & Kray,
C. (2004, September). Cooperative artefacts: Assessing
real world situations with embedded technology. In
International Conference on Ubiquitous Computing (pp.
250-267). Springer: Berlin, Heidelberg. https://doi.org/10.
1007/978-3-540-30119-6_15
[41]. Sucha, P., Kutil, M., Sojka, M., & Hanzálek, Z. (2006,
October). Torsche scheduling toolbox for matlab. In 2006,
IEEE Conference on Computer Aided Control System
Design, (pp. 1181-1186). IEEE. https://doi.org/101109/
CACSD-CCA-ISIC.2006.4776810
[42]. T'kindt, V., & Billaut, J. C. (2002). Introduction to
scheduling. In Multicriteria Scheduling (pp. 5-27). Springer:
Berlin, Heidelberg.
[43]. Rupanetti, D., & Salamy, H. (2019). Task allocation,
migration and scheduling for energy-efficient real-time
multiprocessor architectures. Journal of Systems
Architecture, 98, 17-26. https://doi.org/10.1016/j.sysarc.
2019.06.003
[44]. Urunuela, R., Déplanche, A. M., & Trinquet, Y. (2010,
September). Storm a simulation tool for real-time
th multiprocessor scheduling evaluation. In 2010, IEEE 15
Conference on Emerging Technologies & Factory
Automation (ETFA 2010) (pp. 1-8). IEEE. https://doi.org/10.
1109/ETFA.2010.5641179
[45]. Wang, Z., Ranka, S., & Mishra, P. (2012, January).
Temperature-aware task partitioning for real-time
th scheduling in embedded systems. In 2012, 25
International Conference on VLSI Design (pp. 161-166).
IEEE. https://doi.org/10.1109/VLSID.2012.64
[46]. Xu, M., Phan, L. T. X., Choi, H. Y., & Lee, I. (2016, April).
Analysis and implementation of global preemptive fixedpriority
scheduling with dynamic cache allocation. In
2016, IEEE Real-Time and Embedded Technology and
Applications Symposium (RTAS) (pp. 1-12). IEEE. https://
doi.org/10.1109/RTAS.2016.7461322
[47]. Yun, Y., Hwang, E. J., & Kim, Y. H. (2019). Adaptive
genetic algorithm for energy-efficient task scheduling on
asymmetric multiprocessor system-on-chip.
Microprocessors and Microsystems, 66, 19-30. https://
doi.org/10.1016/j.micpro.2019.01.011
[48]. Zhang, Y. W. (2019). Energy-aware mixed partitioning
scheduling in standby-sparing systems. Computer
Standards & Interfaces, 61, 129-136. https://doi.org/10.
1016/j.csi.2018.06.004
[49]. Zhou, J., Yan, J., Cao, K., Tan, Y., Wei, T., Chen, M.,
Zhang., G., Chen, X., & Hu, S. (2018). Thermal-aware
correlated two-level scheduling of real-time tasks with
reduced processor energy on heterogeneous MPSoCs.
Journal of Systems Architecture, 82, 1-11. https://doi.org/
10.1016/j.sysarc.2017.09.007