A scheduling algorithm using sub-deadline for workflow applications under budget and deadline constrained
- 190 Downloads
With the development of the cloud and grid computing, the cloud infrastructures and grids provide a platform for workflow applications. It is very essential to meet the requirements of users and to complete workflow scheduling efficiently. The scheduling of the workflow is limited by quality of service (QoS) parameters. Many scheduling algorithms have been proposed for the execution of workflow applications using QoS parameters. In this study, we improved a scheduling algorithm that considers workflow applications under budget and deadline constraints. This algorithm provided a simple way to deal with the deadline and budget constraints. The algorithm was named BDSD and used to find a scheduling that satisfies of deadline and budget constraints required by a user. The planning success rate (PSR) was utilized to show the effectiveness of the proposed algorithm. For the simulation experiment, random and real workflow applications were exploited. Experimental results showed that compared with other algorithms the algorithm had a higher PSR.
KeywordsScheduling Sub-deadline Quality of service Planning success rate Workflow application
This work was supported by Beijing Natural Science Foundation (4162007) and Natural Science Foundation of China (61501008).
- 16.Yu, J., Buyya, R., Tham, C.K.: Cost-based scheduling of scientific workflow applications on utility grids. In: First International Conference on e-Science and Grid Computing, IEEE, pp. 1–8 (2005)Google Scholar
- 17.Yu, J., Buyya, R., Tham, C.K.: QoS-based scheduling of workflow applications on service grids. In: Proceedings of 1st IEEE International Conference-Science and Grid Computing, pp. 5–8 (2005)Google Scholar
- 18.Casanova, H., Legrand A., Quinson, M.: Simgrid: A Generic Framework for Large-scale Distributed Experiments. In Proceedings of the 10th International Conference on Computer Modeling and Simulation, UKSIM 2008, IEEE, pp. 126–131 (2008)Google Scholar
- 19.Sakellariou, R., Zhao, H.: A hybrid heuristic for DAG scheduling on heterogeneous systems. Parallel and Distributed Processing Symposium, Proceedings. 18th International. IEEE, vol. 111 (2004)Google Scholar
- 21.Yu, J., Buyya, R.: Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci. Program 14(3), 217–230 (2006)Google Scholar
- 22.Wu, Z., Ni, Z., Gu, L., Liu, X.: A revised discrete particle swarm optimization for cloud workflow scheduling. In: Proceedings of 2010 international conference on computational intelligence and security (CIS), IEEE, pp. 184–188 (2010)Google Scholar