Abstract
Grid computing came into being an active research area because of the advances in wide-area network technologies and the low cost of computing resources. One motivation of grid computing is to aggregate the power of distributed resources and integrate the resources into a unified platform. To minimize the total completion time of the submitted computing jobs to a grid platform, people employ various scheduling algorithms to dispatch the jobs to the resources. However, it has been proved that the optimal scheduling algorithm is NP-hard. Therefore, many people turn to use heuristic approaches for grid scheduling. In this paper, we introduce ten common scheduling heuristics to schedule a combination of job-chains (linear-dependent jobs) and independent jobs on a heterogeneous environment. We implemented these methods on a grid simulator to evaluate their performance under different circumstances. The results of scheduling job-chains and independent jobs on a heterogeneous environment are quite different from previous studies, and we provide our explanations for the differences. We also propose a hybrid method based on our observation, and the simulation results show that it has good performance in terns of makespan.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Foster, I., Kesselman, C.: The Grid 2: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers Inc., San Francisco (2003)
Yu, J., Buyya, R.: A Taxonomy of Workflow Management Systems for GridComputing. Journal of Grid Computing 3(3), 171–200 (2005)
Du, J., Leung, J.Y.-T., Young, G.H.: Scheduling chain-structured tasks to minimize makespan and mean flow time. Information and Computation 92, 219–236 (1991)
Lin, P.-Y., Liu, P.: Job Scheduling Techniques for Distributed Systems with Temporal Constraints. In: Bellavista, P., Chang, R.-S., Chao, H.-C., Lin, S.-F., Sloot, P.M.A. (eds.) GPC 2010. LNCS, vol. 6104, pp. 280–289. Springer, Heidelberg (2010)
Braun, T.D., Siegel, H.J., Beck, N., Bölöni, L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Hensgen, B.Y.D., Freund, R.F.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib.Comput. 61(6), 810–837 (2001)
Sahu, R., Chaturvedi, A.: Many-Objective Comparison of Twelve Grid Scheduling Heuristics. International Journal of Computer Applications 13(6), 9–17 (2011)
Ullman, J.D.: NP-complete scheduling problems. Journal of Computer and System Sciences 10, 384–393 (1975)
GridSim, http://www.cloudbus.org/gridsim/
Al-ali, R.J., Amin, K., Laszewski, G., Rana, O.F., Walker, D.W., Hategan, M., Zaluzec, N.: Analysis and provision of QoS for distributed grid applications. Journal of Grid Computing 2(2), 163–182 (2004)
Krauter, K., Buyya, R., Maheswaran, M.: A taxonomy and survey of grid resourcemanagement systems. Software Practice and Experience 32, 135–164 (2002)
Sulistio, A., Cibej, U., Venugopal, S., Robic, B., Buyya, R.: A toolkit for modelling and simulating data Grids: an extension to GridSim. Concurr. Comput.: Pract. Exper. 20, 1591–1609 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tsai, MY., Chiang, PF., Chang, YJ., Wang, WJ. (2011). Heuristic Scheduling Strategies for Linear-Dependent and Independent Jobs on Heterogeneous Grids. In: Kim, Th., et al. Grid and Distributed Computing. GDC 2011. Communications in Computer and Information Science, vol 261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27180-9_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-27180-9_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27179-3
Online ISBN: 978-3-642-27180-9
eBook Packages: Computer ScienceComputer Science (R0)