Abstract
Heterogeneous computing (HC) environment utilizes diverse resources with different computational capabilities to solve computing-intensive applications having diverse computational requirements and constraints. The task assignment problem in HC environment can be formally defined as for a given set of tasks and machines, assigning tasks to machines to achieve the minimum makespan. In this paper we propose a new task scheduling heuristic, high standard deviation first (HSTDF), which considers the standard deviation of the expected execution time of a task as a selection criterion. Standard deviation of the expected execution time of a task represents the amount of variation in task execution time on different machines. Our conclusion is that tasks having high standard deviation must be assigned first for scheduling. A large number of experiments were carried out to check the effectiveness of the proposed heuristic in different scenarios, and the comparison with the existing heuristics (Max-min, Sufferage, Segmented Min-average, Segmented Min-min, and Segmented Max-min) clearly reveals that the proposed heuristic outperforms all existing heuristics in terms of average makespan.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ali, S., Siegel, H.J., Maheswaran, M., Ali, S., Hensgen, D., 2000. Task Execution Time Modeling for Heterogeneous Computing Systems. Proc. 9th Heterogeneous Computing Workshop, p.185–200. [doi:10.1109/HCW.2000.843743]
Braun, T.D., Siegel, H.J., Beck, N., Bölöni, L.L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Yao, B., Hensgen, D., et al., 2001. A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parall. Distrib. Comput., 61(6):810–837. [doi:10.1006/jpdc.2000.1714]
Briceno, L.D., Oltikar, M., Siegel, H.J., Maciejewski, A.A., 2007. Study of an Iterative Technique to Minimize Completion Times of Non-makespan Machines. Proc. 17th Heterogeneous Computing Workshop, p.1–14. [doi:10.1109/IPDPS.2007.370325]
Cormen, T.H., Leirson, C.E., Rivest, R.L., 2001. Introduction to Algorithms. MIT Press, Cambridge, MA.
Fernandez-Baca, D., 1989. Allocating modules to processors in a distributed system. IEEE Trans. on Software Eng., 15(11):1427–1436. [doi:10.1109/32.41334]
Foster, I., Kesselman, C., 1998. The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufman Publishers, San Francisco, CA, USA.
Freund, R.F., Gherrity, M., Ambrosius, S., Campbell, M., Halderman, M., Hensgen, D., Keith, E., Kidd, T., Kussow, M., Lima, J.D., et al., 1998. Scheduling Resources in Multi-user, Heterogeneous, Computing Environments with Smartnet. Proc. 7th Heterogeneous Computing Workshop, p.184–199. [doi:10.1109/HCW.1998.666558]
Ibarra, O.H., Kim, C.E., 1977. Heuristic algorithms for scheduling independent tasks on non-identical processors. J. ACM, 24(2):280–289. [doi:10.1145/322003.322011]
Kim, J.K., Shivle, S., Siegel, H.J., Maciejewski, A.A., Braun, T.D., Schneider, M., Tideman S., Chitta, R., Dilmaghani, R.B., Joshi, R., et al., 2007. Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment. J. Parall. Distrib. Comput., 67(2):154–169. [doi:10.1016/j.jpdc.2006.06.005]
Kwok, Y.K., Ahmad, I., 1999a. Benchmarking and comparison of the task graph scheduling algorithms. J. Parall. Distrib. Comput., 59(3):381–422. [doi:10.1006/jpdc.1999.1578]
Kwok, Y.K., Ahmad, I., 1999b. Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput. Surv., 31(4):406–471. [doi:10.1145/344588.344618]
Luan, C.J., Song, G.H., Zheng, Y., 2006. Application-adaptive resource scheduling in a computational grid. J. Zhejiang Univ. Sci. A, 7(10):1634–1641. [doi:10.1631/jzus.2006.A1634]
Luo, P., Lu, K., Shi, Z.Z., 2007. A revisit of fast greedy heuristics for mapping a class of independent tasks onto heterogeneous computing systems. J. Parall. Distrib. Comput., 67(6):695–714. [doi:10.1016/j.jpdc.2007.03.003]
Maheswaran, M., Ali, S., Siegel, H.J., Hensgen, D., Freund, R.F., 1999. Dynamic mapping of a class of independent tasks onto heterogeneous computing system. J. Parall. Distrib. Comput., 59(2):107–131. [doi:10.1006/jpdc.1999.1581]
Ritchie, G., Levine, J., 2004. A Hybrid Ant Algorithm for Scheduling Independent Jobs in Heterogeneous Computing Environments. Proc. 23rd Workshop of the UK Planning and Scheduling Special Interest Group, p.1–7.
Sakellariou, R., Zhao, H., 2004. A Hybrid Heuristic for DAG Scheduling on Heterogeneous Systems. Proc. 18th Parallel and Distributed Processing Symp., p.111–123. [doi:10.1109/IPDPS.2004.1303065]
Shivle, S., Sugavanam, P., Siegel, H.J., Maciejewski, A.A., Banka, T., Chindam, K., Dussinger, S., Kutruff, A., Penumarthy, P., Pichumani, P., et al., 2005. Mapping subtasks with multiple versions on an ad hoc grid. Parall. Comput., Special Issue Heterog. Comput., 31(7):671–690. [doi:10.1016/j.parco.2005.04.003]
Vazirani, V.V., 2002. Approximation Algorithms. Springer, Berlin, Germany.
Wang, L., Siegel, H.J., Roychowdhury, V.P., Maciejewski, A.A., 1997. Task matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach. J. Parall. Distrib. Comput., 47(1):8–22. [doi:10.1006/jpdc.1997.1392]
Wu, M.Y., Shu, W., Zhang, H., 2000. Segmented Min-min: A Static Mapping Algorithm for Meta-tasks on Heterogeneous Computing Systems. Proc. 9th Heterogeneous Computing Workshop, p.375–385. [doi:10.1109/HCW.2000.843759]
Author information
Authors and Affiliations
Corresponding author
Additional information
Project supported by the National Natural Science Foundation of China (No. 60703012), the National Basic Research Program (973) of China (No. 2006CB303000), and the Heilongjiang Provincial Scientific and Technological Special Fund for Young Scholars (No. QC06C033), China
Rights and permissions
About this article
Cite this article
Munir, E.U., Li, Jz., Shi, Sf. et al. A new heuristic for task scheduling in heterogeneous computing environment. J. Zhejiang Univ. Sci. A 9, 1715–1723 (2008). https://doi.org/10.1631/jzus.A0820007
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/jzus.A0820007
Key words
- Heterogeneous computing
- Task scheduling
- Greedy heuristics
- High standard deviation first (HSTDF) heuristic