Asghari, A., & Sohrabi, M. K. (2021). Combined use of coral reefs optimization and multi-agent deep Q-network for energy-aware resource provisioning in cloud data centers using DVFS technique. Cluster Computing.
https://doi.org/10.1007/s10586-021-03368-3
Barzegar, B., Motameni, H., & Movaghar, A. (2019). EATSDCD: A green energy-aware scheduling algorithm for parallel task-based application using clustering, duplication and DVFS technique in cloud datacenters. Journal of Intelligent & Fuzzy Systems, 36(6), 5135–5152.
https://doi.org/10.3233/JIFS-171927
Di, S., Robert, Y., Vivien, F., Vivien, F., ENS Lyon and INRIA, F., Profile, V., Kondo, D., Wang, C.-L., & Cappello, F. (2013). Optimization of cloud task processing with checkpoint-restart mechanism. Proceedings of the International Conference on High Performance Computing.
https://doi.org/http://dx.doi.org/10.1145/2503210.2503217
Fan, M., Han, Q., & Yang, X. (2017). Energy minimization for on-line real-time scheduling with reliability awareness. Journal of Systems and Software, 127, 168–176 .
Fatehi, S., Motameni, H., Barzegar, B., & Golsorkhtabaramiri, M. (2020). Energy Aware Multi Objective Algorithm for Task Scheduling on DVFS-Enabled Cloud Datacenters using Fuzzy NSGA-II. International Journal of Nonlinear Analysis and Applications.
https://dx.doi.org/10.22075/ijnaa.2020.21625.2283
Garg, S. K., Yeo, C. S., Anandasivam, A., & Buyya, R. (2011). Environment-conscious scheduling of HPC applications on distributed cloud-oriented data centers. Journal of Parallel and Distributed Computing, 71(6), 732–749.
Guérout, T., Monteil, T., Costa, G. Da, Calheiros, R., Buyya, A., & Alexandru, M. (2013). Energy-aware simulation with DVFS. Simulation Modelling Practice and Theory.
https://www.sciencedirect.com/science/article/abs/pii/S1569190X13000786
Hassan, H. A., Salem, S. A., & Saad, E. M. (2020). A smart energy and reliability aware scheduling algorithm for workflow execution in DVFS-enabled cloud environment. Future Generation Computer Systems, 112, 431–448.
https://doi.org/10.1016/j.future.2020.05.040
Ismail, L., & Fardoun, A. (2016). Eats: Energy-aware tasks scheduling in cloud computing systems. Procedia Computer Science, 83, 870–877.
Ismayilov, G., & Topcuoglu, H. R. (2020). Neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing. Future Generation Computer Systems, 102, 307–322.
https://doi.org/10.1016/j.future.2019.08.012
Liu, D., Chen, X., Ying, Y., Zhang, L., Li, W., Jiang, L., & Che, S. (2016). MnZn power ferrite with high Bs and low core loss. Ceramics International, 42(7), 9152–9156.
https://doi.org/10.1016/j.ceramint.2016.03.005
Peng, Z., Barzegar, B., Yarahmadi, M., Motameni, H., & Pirouzmand, P. (2020). Energy-Aware Scheduling of Workflow Using a Heuristic Method on Green Cloud. Scientific Programming, 2020, 1–14.
https://doi.org/10.1155/2020/8898059
Safari, M., & Khorsand, R. (2018). Energy-aware scheduling algorithm for time-constrained workflow tasks in DVFS-enabled cloud environment. Simulation Modelling Practice and Theory.
https://doi.org/10.1016/j.simpat.2018.07.006
Tang, Z., Cheng, Z., Li, K., & Khan, S. . (2016). An energy-efficient task scheduling algorithm in DVFS-enabled cloud environment. J. Grid Comput.
Topcuoglu, H., Hariri, S., & Wu, M. (2002). Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems, 13(3), 260–274.
Wu, T., Gu, H., Zhou, J., Wei, T., Liu, X., & Chen, M. (2018). Soft error-aware energy-efficient task scheduling for workflow applications in DVFS-enabled cloud. Journal of Systems Architecture.
https://doi.org/https://doi.org/10.1016/j.sysarc.2018.03.001
Xie, G., Chen, Y., Liu, Y., Wei, Y., Li, R., & Li, K. (2016). Resource consumption cost minimization of reliable parallel applications on heterogeneous embedded systems. IEEE Transactions on Industrial Informatics, 13(4), 1629–1640.
Xie, G., Chen, Y., Xiao, X., Xu, C., Li, R., & Li, K. (2017). Energy-efficient fault-tolerant scheduling of reliable parallel applications on heterogeneous distributed embedded systems. IEEE Transactions on Sustainable Computing, 3(3), 167–181.
Yeganeh-Khaksar, A., Ansari, M., Safari, S., Yari-Karin, S., & Ejlali, A. (2021). Ring-DVFS: Reliability-Aware Reinforcement Learning-Based DVFS for Real-Time Embedded Systems. IEEE Embedded Systems Letters, 13(3), 146–149.
https://doi.org/10.1109/LES.2020.3033187
Zhang, L., Li, K., Li, C., & Li, K. (2017a). Bi-objective workflow scheduling of the energy consumption and reliability in heterogeneous computing systems. Inf. Sci. (Ny). 379, 241–256.
Zhang, L., Li, K., Li, C., & Li, K. (2017b). Bi-objective workflow scheduling of the energy consumption and reliability in heterogeneous computing systems. Information Sciences, 379, 241–256.
Zhang, L., Li, K., & Xu, Y. (2016). Joint optimization of energy efficiency and system reliability for precedence constrained tasks in heterogeneous systems. International Journal of Electrical Power & Energy Systems.
ارسال نظر در مورد این مقاله