Adaptive energy-aware scheduling method in a meteorological cloud

Future Generation Computer Systems - Tập 101 - Trang 1142-1157 - 2019
Yongsheng Hao1,2,3, Jie Cao1,4, Tinghuai Ma5, Sai Ji2,5
1School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing 210044, China
2Network Centre, Nanjing University of Information Science & Technology, Nanjing, 210044, China
3Engineering Research Center for Software Testing and Evaluation of Fujian Province, Xiamen University of Technology, 361024, China
4Management school, Xuzhou University of Technology, Xuzhou, 221018, China
5School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China

Tài liệu tham khảo

Hameed, 2016, A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems, Computing, 98, 751, 10.1007/s00607-014-0407-8 Zong, 2010, EAD And PEBD: two energy-aware duplication scheduling algorithms for parallel tasks on homogeneous clusters, IEEE Trans. Comput., 60, 360, 10.1109/TC.2010.216 Hazra, 2018, Energy aware task scheduling algorithms in cloud environment: A survey, 631 Reddy, 2019, Energy-aware virtual machine allocation and selection in cloud data centers, Soft Comput., 23, 1917, 10.1007/s00500-017-2905-z Li, 2018, An energy-aware task offloading mechanism in multiuser mobile-edge cloud computing, Mob. Inf. Syst., 2018 Li, 2015, Global EDF scheduling for parallel real-time tasks, Real-Time Syst., 51, 395, 10.1007/s11241-014-9213-9 Li, 2016, Power and performance management for parallel computations in clouds and data centers, J. Comput. System Sci., 82, 174, 10.1016/j.jcss.2015.07.001 Wang, 2016, Multiagent-based resource allocation for energy minimization in cloud computing systems, IEEE Trans. Syst. Man Cybern., 47, 205 Juarez, 2018, Dynamic energy-aware scheduling for parallel task-based application in cloud computing, Future Gener. Comput. Syst., 78, 257, 10.1016/j.future.2016.06.029 Oukfif, 2015, Energy-aware dpso algorithm for workflow scheduling on computational grids, 651 Ebaid, 2014, Energy-aware heuristics for scheduling parallel applications on high performance computing platforms, 000282 Liu, 2014, Adaptive energy-efficient scheduling algorithm for parallel tasks on homogeneous clusters, J. Netw. Comput. Appl., 41, 101, 10.1016/j.jnca.2013.10.009 Slusanschi, 2013, Scalability study of two weather prediction models, 129 Xie, 2010, High-performance computing for the simulation of dust storms, Comput. Environ. Urban Syst., 34, 278, 10.1016/j.compenvurbsys.2009.08.002 Xu, 2012, Energy minimizing for parallel real-time tasks based on level-packing, 98 Sun, 2018, Scheduling parallel tasks under multiple resources: List scheduling vs. Pack scheduling, 194 Hao, 2016, An adaptive algorithm for scheduling parallel jobs in meteorological cloud, Knowl.-Based Syst., 98, 226, 10.1016/j.knosys.2016.01.038 Hao, 2015, Performance analysis of gang scheduling in a grid, J. Netw. Syst. Manage., 23, 650, 10.1007/s10922-014-9312-x Thanavanich, 2013, Efficient energy aware task scheduling for parallel workflow tasks on hybrids cloud environment, 37 Garg, 2016, Energy-aware workflow scheduling in grid under QoS constraints, Arab. J. Sci. Eng., 41, 495, 10.1007/s13369-015-1705-y Ye, 2018, Online scheduling of moldable parallel tasks, J. Sched., 21, 647, 10.1007/s10951-018-0556-2 Zahaf, 2017, Energy-efficient scheduling for moldable real-time tasks on heterogeneous computing platforms, J. Syst. Archit., 74, 46, 10.1016/j.sysarc.2017.01.002 Celaya, 2014, An adaptive policy to minimize energy and sla violations of parallel jobs on the cloud, 507 Rauber, 2012, Towards an energy model for modular parallel scientific applications, 523 Sanders, 2012, Energy efficient frequency scaling and scheduling for malleable tasks, 167 Zhu, 2014, Performance–energy adaptation of parallel programs in pervasive computing, J. Supercomput., 70, 1260, 10.1007/s11227-014-1226-6 Mo, 2018, Energy-quality-time optimized task mapping on DVFS-enabled multicores, IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst., 37, 2428, 10.1109/TCAD.2018.2857300 Safari, 2018, Energy-aware scheduling algorithm for time-constrained workflow tasks in DVFS-enabled cloud environment, Simul. Model. Pract. Theory, 87, 311, 10.1016/j.simpat.2018.07.006 Sahuquillo, 2016, A dynamic execution time estimation model to save energy in heterogeneous multicores running periodic tasks, Future Gener. Comput. Syst., 56, 211, 10.1016/j.future.2015.06.011 Wu, 2008, Parallel execution time prediction of the multitask parallel programs, Perform. Eval., 65, 701, 10.1016/j.peva.2008.04.001 Nadeem, 2017, Modeling and predicting execution time of scientific workflows in the grid using radial basis function neural network, Cluster Comput., 20, 2805, 10.1007/s10586-017-1018-x Tom, 2013, Energy-aware simulation with DVFS, Simul. Model. Pract. Theory, 39, 76, 10.1016/j.simpat.2013.04.007 Manumachu, 2017, Bi-objective optimization of data-parallel applications on homogeneous multicore clusters for performance and energy, IEEE Trans. Comput., 67, 160, 10.1109/TC.2017.2742513