An adaptive algorithm for scheduling parallel jobs in meteorological Cloud
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Jorissen, 2012, A high performance scientific cloud computing environment for materials simulations, Comput. Phys. Commun., 183, 1911, 10.1016/j.cpc.2012.04.010
Akioka, 2004, Extended forecast of CPU and network load on computational Grid, 765
Nadeem, 2013, Optimizing execution time predictions of scientific workflow applications in the Grid through evolutionary programming, Future Gener. Comput. Syst., 29, 926, 10.1016/j.future.2012.10.005
Hao, 2015, An evaluation of nine heuristic algorithms with data-intensive jobs and computing-intensive jobs in a dynamic environment, IET softw., 9, 7, 10.1049/iet-sen.2014.0014
Zhu, 2012, A cost-effective scheduling algorithm for scientific workflows in clouds, 256
Wan, 2012, A QoS-awared scientific workflow scheduling schema in cloud computing, 634
Liu, 2013, CCBKE-Session key negotiation for fast and secure scheduling of scientific applications in cloud computing, Future Gener. Comput. Syst., 29, 1300, 10.1016/j.future.2012.07.001
Chen, 2013, Privacy-preserving and verifiable protocols for scientific computation outsourcing to the cloud, J. Parallel Distrib. Comput., 8
Yuan, 2010, A data placement strategy in scientific cloud workflows, Future Gener. Comput. Syst., 26, 1200, 10.1016/j.future.2010.02.004
M. Wang, L. Zhu, J. Chen, A QoS-awared scientific workflow scheduling schema in cloud computing. Information Science and Technology (ICIST). 2012 International Conference on, pp. 634–639, ISBN: 978-1-4577-0343-0, doi: 10.1109/ICIST.2012.6221722
Fan, 2012, An effective approximation algorithm for the malleable parallel task scheduling problem, J. Parallel Distrib. Comput., 72, 693, 10.1016/j.jpdc.2012.01.011
Sun, 2014, Competitive online adaptive scheduling for sets of parallel jobs with fairness and efficiency, J. Parallel Distrib. Comput., 74, 2180, 10.1016/j.jpdc.2013.12.003
Wang, 2013, Energy-aware parallel task scheduling in a cluster, Future Gener. Comput. Syst., 29, 1661, 10.1016/j.future.2013.02.010
Liu, 2013, Adaptive energy-efficient scheduling algorithm for parallel tasks on homogeneous clusters, J. Netw. Comput. Appl.
Ramezani, 2015, Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments, World Wide Web-internet Web Inf. Syst., 18, 1737, 10.1007/s11280-015-0335-3
Li, 2015, Global EDF scheduling for parallel real-time tasks, Real-Time Syst., 51, 395, 10.1007/s11241-014-9213-9
Hao, 2015, Performance analysis of gang scheduling in a grid, J. Netw. Syst. Manag., 23, 650, 10.1007/s10922-014-9312-x
Zhang, 2015, Maximizing reliability with energy conservation for parallel task scheduling in a heterogeneous cluster, Inf. Sci., 319, 113, 10.1016/j.ins.2015.02.023
Ferry, 2013, A real-time scheduling service for parallel tasks, 261
Banerjee, 1990, Approximate algorithms for the partitionable independent task scheduling problem, vol. I, 72
Tao, 2013, Two-tier policy-based consolidation control for workload with soft deadline constrain in virtualized data center, 2357
Matijaš, 2013, Load forecasting using a multivariate meta-learning system, Expert Syst. Appl., 40, 4427, 10.1016/j.eswa.2013.01.047
Yang, 1996, An effective and practical performance prediction model for parallel computing on nondedicated heterogeneous NOW, J. Parallel Distrib. Comput., 38, 63, 10.1006/jpdc.1996.0129
Fan, 2012, An effective approximation algorithm for the malleable parallel task scheduling problem, J. Parallel Distrib. Comput., 72, 693, 10.1016/j.jpdc.2012.01.011
Quinn, 2004
Tucker, 1989, Process control and scheduling issues for multiprogrammed shared-memory multiprocessors, ACM SIGOPS Oper. Syst. Rev., 23, 159, 10.1145/74851.74866
Suter, 2004, From Heterogeneous Task Scheduling to Heterogeneous Mixed Parallel Scheduling, vol. 3149, 230
Topcuouglu, 2002, Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing, IEEE Trans. Parallel Distrib. Syst., 13, 260, 10.1109/71.993206
Anousha, 2013, An Improved Min-Min Task Scheduling Algorithm in Grid Computing, 7861, 103, 10.1007/978-3-642-38027-3_11
Falco, 2014, Two new fast heuristics for mapping parallel applications on cloud computing, Future Gener. Comp. Syst., 37, 1, 10.1016/j.future.2014.02.019
Papazachos, 2010, Performance evaluation of bag of gangs scheduling in a heterogeneous distributed system, J. Syst. Softw., 83, 1346, 10.1016/j.jss.2010.01.009
Liu, 2013, A Novel Deadline Assignment Strategy for a Large Batch of ParallelTasks with Soft Deadlines in the Cloud, 51
Zong, 2011, 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
Dutta, 2011, Service deactivation aware placement and defragmentation in enterprise clouds, 24
M. Gillespie. “Amdahl's Law, Gustafson's Trend, and the Performance Limits of Parallel Applications.” Online].WWW-sivu:http://software.intel.com/sites/default/files/m/d/4/1/d/8/Gillespie-0053-AAD_Gustafson-Amdahl_v1__2_.rh.final.pdf (2008).
Hao, 2012, An enhanced load balancing mechanism based on deadline control on GridSim, Future Gener. Comput. Syst., 28, 657, 10.1016/j.future.2011.10.010
Jiang, 2013, Optimal Cloud Resource Auto-Scaling for Web Applications, 58