Virtual Workflow Constrained Time-accuracy Optimization Algorithm Scheduling by Iterative Reduction
LUO Zhiyong①② ZHU Zihao① YOU Bo② LIU Jiahui①
①(School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China) ②(School of Mechanical Engineering, Harbin University of Science and Technology, Harbin 150080, China)
Abstract：For the problem of the production of complex operations, this paper uses workflow technology and takes the completion time as constraint, and proposes a Virtual Iterative Reduction Algorithm (VIRA) to achieve better production accuracy in the constraint completion time. By virtualizing tasks in mutual constraint into a virtual
node, the algorithm uses inverse iterative way to determine a path that completion time and production accuracy get balance. By comparison, the virtual iterative reduction algorithm can increase the production accuracy in the constraint completion time, and it is found to improve the accuracy of the algorithm by changing the deadline, the number of tasks and other parameters.
DE P, DUNNE E J, GHOSH J B, et al. Complexity of the discrete time-cost trade-off problem for project networks[J]. Operations Research, 1997, 45(2): 302-306. doi: 10.1287/ opre.45.2.302.
[2]
KUMAR A, DIJKMAN R, and SONG M. Optimal resource assignment in workflows for maximizing cooperation[C]. 11th International Conference, BPM 2013, Beijing, China, 2013: 26-30. doi: 10.1007/978-3-642-40176-3_20.
[3]
BUYYA R, GIDDY J, and ABRAMSON D. An evaluation of economy-based resource trading and scheduling on computational power grids for parameter sweep applications [C]. Proceedings of the 2nd International Workshop on Active Middleware Services, Pittsburgh, USA, 2000: 221-230. doi: 10.1007/978-1-4419-8648-1_19.
[4]
DELDARI A, NAGHIBZADEH M, and ABRISHAMI S. CCA: A deadline-constrained workflow scheduling algorithm for multicore resources on the cloud[J]. The Journal of Supercomputing, 2017, 73(2): 756-781. doi: 10.1007/s11227- 016-1789-5.
[5]
ALKHANAK E N, LEE S P, REZAEI R, et al. Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: A review, classifications, and open issues[J]. Journal of Systems and Software, 2016, 133: 1-26. doi: 10.1016/j.jss.2015.11.023.
[6]
VIRIYAPANT K and SMANCHAT S. A deadline- constrained scheduling for dynamic multi-instances parameter sweep workflow[C]. 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), Okayama, Japan, 2016: 1-6. doi: 10.1109/ ICIS.2016.7550820.
[7]
ARABNEJA H, BARBOSA J G, and PRODAN R. Low-time complexity budget-deadline constrained workflow scheduling on heterogeneous resources[J]. Future Generation Computer Systems, 2016, 55: 29-40. doi: 10.1016/j.future.2015.07.021.
[8]
VERMA A and KAUSHAL S. Cost-time efficient scheduling plan for executing workflows in the cloud[J]. Journal of Grid Computing, 2015, 13(4): 1-12. doi: 10.1007/s10723-015- 9344-9.
LIANG Helan, DU Yanhua, and LI Sujian. Research on dynamic scheduling of scientific workflows with temporal constraints [J]. Systems Engineering-Theory & Practice, 2015(9): 2410-2421. doi: 10.12011/1000-6788(2015)9-2410.
CAO Bin, WANG Xiaotong, XIONG Lirong, et al. Searching method for particle swarm optimization of cloud workflow scheduling with time constraint[J]. Computer Integrated Manufacturing Systems, 2016, 22(2): 372-380.
