A stochastic approach to estimating earliest start times of nodes for scheduling DAGs on heterogeneous distributed computing systems

Cluster Computing

Volumn 14, Issue 4, 2011, Pages 377-395

  Kamthe, Ankur ^a   Lee, Soo Young ^a  

^a AUBURN UNIVERSITY   (United States)

Author keywords

Average parallel execution time; Competing situation; Scheduling; Spatial heterogeneity; Stochastic DAG; Temporal heterogeneity

Indexed keywords

COMPETING SITUATION; PARALLEL EXECUTION TIME; SPATIAL HETEROGENEITY; STOCHASTIC DAG; TEMPORAL HETEROGENEITIES;

COMMUNICATION; COMPUTER SIMULATION; SCHEDULING; STOCHASTIC SYSTEMS;

DISTRIBUTED COMPUTER SYSTEMS;

EID: 81355149797     PISSN: 13867857     EISSN: 15737543     Source Type: Journal    
DOI: 10.1007/s10586-011-0167-6     Document Type: Article

References (24)

1. Grajcar, M.: Genetic list scheduling algorithm for scheduling and allocation on a loosely coupled heterogeneous multiprocessor system. In: Proceedings of the 36th Design Automation Conference, pp. 280-285 (1999).
2. Chan, W.-Y., Li, C.-K.: Scheduling tasks in DAG to heterogeneous processor system. In: Proceedings of the Sixth Euromicro Workshop on Parallel and Distributed Processing (PDP '98), Jan. 1998, pp. 27-31.
3. Topcuoglu, H., Hariri, S., Wu, M.-Y.: Task scheduling algorithms for heterogeneous processors. In: Proceedings of the Eighth Heterogeneous Computing Workshop, 1999 (HCW '99), April 1999, pp. 3-14.
4. Park, G.-L., Shirazi, B., Marquis, J., Choo, H.: Decisive path scheduling: a new list scheduling method. In: Proceedings of the 1997 International Conference on Parallel Processing, Aug. 1997, pp. 472-480.
5. Chan, W.-Y., Li, C.-K.: Heterogeneous dominant sequence cluster (HDSC): a low complexity heterogeneous scheduling algorithm. In: IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, vol. 2, Aug. 1997, pp. 956-959.
6. Liu, Z.: Scheduling of random task graphs on parallel processors. In: Proceedings of the Third International Workshop on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS '95), Jan. 1995, pp. 143-147.
7. Woo, S.-H., Yang, S.-B., Kim, S.-D., Han, T.-D.: Task scheduling in distributed computing systems with a genetic algorithm. In: High Performance Computing on the Information Superhighway (HPC Asia '97), May 1997, pp. 301-305.
8. Hou, E. S. H., Ansari, N., Ren, H.: A genetic algorithm for multiprocessor scheduling. IEEE Trans. Parallel Distrib. Syst. 5(2), 113-120 (1994).
9. Kwok, Y.-K.: Parallel program execution on a heterogeneous PC cluster using task duplication. In: Proceedings of the 9th Heterogeneous Computing Workshop, 2000(HCW 2000), May 2000, pp. 364-374.
10. Liu, Z., Fang, B., Zhang, Y., Tang, J.: Scheduling algorithms for a fork DAG in a NOWs. In: Proceedings of the Fourth International Conference/Exhibition on High Performance Computing in the Asia-Pacific Region, vol. 2, May 2000, pp. 959-960.
11. Lee, S.-Y., Huang, J.: A heterogeneity-aware approach to load balancing of computational tasks: a theoretical and simulation study. Clust. Comput. 11(2), 133-149 (2008).
12. 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 (2002).
13. Almeida, V. A. F., Vasconcelos, I. M. M., Árabe, J. N. C., Menascé, D. A.: Using random task graphs to investigate the potential benefits of heterogeneity in parallel systems. In: Proceedings of the 1992 ACM/IEEE Conference on Supercomputing, Nov. 1992, pp. 683-691.
14. Iverson, M. A., Ozguner, F., Potter, L. C.: Statistical prediction of task execution times through analytic benchmarking for scheduling in a heterogeneous environment. In: Proceedings of the 8th Heterogeneous Computing Workshop (HCW '99), April 1999, p. 99.
15. Huang, J., Lee, S.-Y.: Effects of spatial and temporal heterogeneity on performance of a target task in heterogeneous computing environments. In: 15th ISCA International Conference on Parallel and Distributed Systems, Sept. 2002, pp. 301-306.
16. Yang, L., Schopf, J. M., Foster, I.: Conservative scheduling: using predicted variance to improve scheduling decisions in dynamic environments. In: SuperComputing'03, November 2003, pp. 31-45.
17. Schopf, J., Berman, F.: Stochastic scheduling. CS Dept. Technical Report CS-99-03, University of California, San Diego.
18. Bajaj, R., Agarwal, D. P.: Improving scheduling of tasks in a heterogeneous environment. IEEE Trans. Parallel Distrib. Syst. 15(2), 107-118 (2004).
19. Dogan, A., Ozguner, F.: Stochastic scheduling of a meta-task in heterogeneous distributed computing. In: International Conference on Parallel Processing Workshops, pp. 369-374 (2001).
20. Li, Y. A., Antonio, J. K.: Estimating the execution time distribution for a task graph in a heterogeneous computing system. In: Proceedings of the 6th Heterogeneous Computing Workshop (HCW '97), April 1997, p. 172.
21. Stavrinides, G. L., Karatza, H. D.: Scheduling multiple task graphs with end to end deadlines in distributed real time systems utilizing imprecise computations. J. Syst. Softw. 83, 1004-1014 (2010).
22. Lee, Y. C., Subrata, R., Zomaya, A. Y.: On the performance of a dual objective optimization model for workflow applications on grid platforms. IEEE Trans. Parallel Distrib. Syst. 20(9), 1273-1284 (2009).
23. Boeres, C., Chochia, G., Thanisch, P.: On the scope of applicability of the ETF algorithm. In: Workshop on Parallel Algorithms for Irregularly Structured Problems, pp. 159-164 (1995).
24. Sih, G. C., Lee, E. A.: A compile-time scheduling heuristic for interconnection strained heterogeneous processor architectures. IEEE Trans. Parallel Distrib. Syst. 4(2), 175-187 (1995).