Maximize throughput scheduling and cost-fairness optimization for multiple DAGs with deadline constraint

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9529, Issue , 2015, Pages 621-634

  Wang, Wei ^a   Wu, Qingbo ^a   Tan, Yusong ^a   Wu, Fuhui ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

Deadline constrained scheduling; Multiple DAGs; Workflow scheduling

Indexed keywords

ALGORITHMS; COMPUTER ARCHITECTURE; COST REDUCTION; COSTS; DIRECTED GRAPHS; OPTIMIZATION; PARALLEL ARCHITECTURES; SCHEDULING;

COMPUTER RESOURCES; CONSTRAINED SCHEDULING; DEADLINE CONSTRAINT; DIRECTED ACYCLIC GRAPH (DAG); FAIRNESS SCHEDULING; MAXIMIZE THROUGHPUT; MULTIPLE DAGS; WORKFLOW SCHEDULING;

SCHEDULING ALGORITHMS;

EID: 84951946626     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-27122-4_43     Document Type: Conference Paper

References (22)

1. Zhao, H., Sakellariou, R.: Scheduling multiple DAGs onto heterogeneous systems. In: 20th International Parallel and Distributed Processing Symposium, IPDPS 2006, p. 14. IEEE (2006)
2. Hwang, K., Fox, G.C., Dongarra, J., et al.: Cloud Comptuing and Distributed System: From Parallel Processing to Web of Things. Machinery Industy Press, Beijing (2013)
3. Hnig, U., Schiffmann, W.: A meta-algorithm for scheduling multiple DAGs in homogeneous system environments. In: Proceedings of the Eighteenth IASTED International Conference on Parallel and Distributed Computing and Systems (PDCS 2006) (2006)
4. Bittencourt, L.F., Madeira, E.R.M.: Towards the scheduling of multiple workflows on computational grids. J. Grid Comput. 8(3), 419-441 (2010)
5. Tian, G., Xiao, C., Zhusheng, X., et al.: Hybrid scheduling strategy for mulitple DAGs workflow in heterogeneous system. J. Softw. 23(10), 2720-2734 (2012)
6. Arabnejad, H., Barbosa, J.: Fairness resource sharing for dynamic workflow scheduling on heterogeneous systems. In: 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications (ISPA), pp. 633-639. IEEE (2012)
7. Mao, M., Humphrey, M.: Auto-scaling to minimize cost and meet application deadlines in cloud workflows. In: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, p. 49. ACM (2011)
8. Yu, Z., Shi, W.: A planner-guided scheduling strategy for multiple workflow applications. In: International Conference on Parallel Processing-Workshops, ICPP-W 2008, pp. 1-8. IEEE (2008)
9. Yuan, Y., Li, X., Wang, Q., et al.: Deadline division-based heuristic for cost optimization in workflow scheduling. Inf. Sci. 179(15), 2562-2575 (2009)
10. Abrishami, S., Naghibzadeh, M., Epema, D.H.J.: Deadline-constrained workflow scheduling algorithms for Infrastructure as a service clouds. Future Gener. Comput. Syst. 29(1), 158-169 (2013)
11. 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, 8 p. 147. IEEE (2005)
12. Abrishami, S., Naghibzadeh, M., Epema, D.H.J.: Cost-driven scheduling of grid workflows using partial critical paths. IEEE Trans. Parallel Distrib. Syst. 23(8), 1400-1414 (2012)
13. Liu, K., Jin, H., Chen, J., et al.: A compromised-time-cost scheduling algorithm in SwinDeW-C for instance-intensive cost-constrained workflows on cloud computing platform. Int. J. High Perform. Comput. Appl. 24, 445-456 (2010)
14. Henan, Z., Sakellariou, R.: Scheduling multiple DAGs onto heterogeneous systems. In: Proceedings of the 20th International Parallel and Distributed Processing Symposium, Island of Rhodes, Greece, p. 159, 25-29 April 2006
15. Honig, U., Schiffmann, W.: A meta-algorithm for scheduling multiple DAGs in homogeneous system environments. In: Proceedings of the IEEE 18th IASTED International Conference on Parallel and Distributed Computing and System, pp. 147-152 (2006)
16. Stavrinides, G.L., Karatza, H.D.: Scheduling multiple task graphs with end-toend deadlines in distributed real-time systems utilizing imprecise computations. J. Syst. Softw. 83(6), 1004-1014 (2010)
17. Stavrinides, G.L., Karatza, H.D.: Scheduling real-time DAGs in heterogeneous clusters by combining imprecise computations and bin packing techniques for the exploitation of schedule holes. Future Gener. Comput. Syst. 28(7), 977-988 (2012)
18. Baker, T.P.: An analysis of EDF schedulability on a multiprocessor. IEEE Trans. Parallel Distrib. Syst. 16(8), 760-768 (2005)
19. Davis, R.I., Burns, A.: A survey of hard real-time scheduling for multiprocessor systems. ACM Comput. Surv. 43(4), 1-44 (2011)
20. Oh, S.-H., Yang, S.-M.: A modified least-laxity-first scheduling algorithm for realtime tasks. In: Proceedings of the Fifth International Conference on Real-Time Computing Systems and Applications, pp. 31-36 (1998)
21. Tian, G.-Z., Xiao, C.-B., Xie, J.-Q.: Scheuling and fair cost-optimizing methods for concrrrent multiple DAGs with deadline sharing resources. Chin. J. Comput. 37(7), 1067-1619 (2014)
22. Topcuoglu, H., Hariri, S., Wu, M.-Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260-274 (2012)