A novel deadline and budget constrained scheduling heuristics for computational grids
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The conventional deadline and budget constrained (DBC) scheduling heuristics for economic-based computational grids does not take the inconsistency of grid heterogeneity into account, which can lead to decline of application completion ratios. Motivated by this fact, a novel DBC scheduling heuristics was proposed to deal with sequential workflow applications. In order to valuate the inconsistency, the relative cost (RC) metric was introduced, which was used to indicate the task-starving degree for resources. The new algorithm assigns tasks to resources, considering completion time, budget and RC together. The GridSim toolkit and the benchmark suites of the standard performance evaluation corporation (SPEC) were used to simulate the heterogeneous grid environment and applications. The experimental results show that the task and workflow completion ratios of the new heuristics are higher than those of the conventional heuristics.
Key wordscomputional grids economic-based grid grid brocker grid scheduling simulation
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- FOSTER I, KESSELMAN C. The grid: Blueprint for a new computing infrastructure [M]. San Francisco: Morgan Kaufmann Publishers Inc, 1999: 10–16.Google Scholar
- PELLICER S, LIU H, PAN Y. Mapping, scheduling, and fault tolerance in grid environments [M]// Engineering the Grid: Status and Perspective. California: American Scientific Publishers, 2006: 370–391.Google Scholar
- BRAUN T D, SIEGEL H J, BECK N, BOLONI L, FREUND R F, HENSGEN D, MAHESWARAN M, REUTHER A I, ROBERTSON J P, THEYS M D, YAO B. A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems [J]. Journal of Parallel and Distributed Computing, 2001, 61(6): 810–837.CrossRefGoogle Scholar
- KIM J K, SHIVLE S, SIEGEL H J. Dynamic mapping in a heterogeneous environment with tasks having priorities and multiple deadlines [C]// Proceedings of the 17th International Symposium on Parallel and Distributed Processing, Washington D C: IEEE Computer Society, 2003: 98–110.Google Scholar
- SHETTY S, PADALA P, FRANK M P. A survey of market-based approaches to distributed computing [R]. Gainesville: Department of Computer and Information Science and Engineering, University of Florida, 2003: 3–7.Google Scholar
- VENUGOPAL S, BUYYA R. A deadline and budget constrained scheduling algorithm for science applications on data grids [C]// 6th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP-2005). Melbourne: Springer, 2005: 60–72.Google Scholar
- FENG Hao-lin, SONG Gang-hua, ZHENG Yao, XIA Jun. A deadline and budget constrained cost-time optimization algorithm for scheduling dependent tasks in grid computing [C]// Second International Workshop on Grid and Coorperative Computing. Shanghai: Springer, 2003: 113–120.Google Scholar
- SINGH G, KESSELMAN C, DEELMAN E. A provisioning model and its comparison with best-effort for performance-cost optimization in grids [C]// Proceedings of the 16th International Symposium on High Performance Distributed Computing. California: ACM Press, 2007: 117–126.Google Scholar
- WIECZOREK M, PODLIPNIG S, PRODAN R, FAHRINGER T. Bi-criteria scheduling of scientific workflows for the Grid [C]// Cluster Computing and the Grid 2008. Lyon: IEEE Cpmputer Society, 2008: 9–16.Google Scholar
- BUYYA R, MURSHED M, ABRAMSON D, VENUGOPAL S. Scheduling parameter sweep applications on global grids: A deadline and budget constrained cost-time optimization algorithm [J]. Software: Practice and Experience, 2005, 35(5): 491–512.Google Scholar
- GARG S, BUYYA R, SIEGEL H J. Scheduling parallel applications on utility grids: Time and cost trade-off management [C]// Proceedings of the 32nd Australasian Computer Science Conference (ACSC 2009). Wellington: Australian Computer Society, 2009: 151–159.Google Scholar
- ARMSTRONG R. Investigation of effect of different run-time distribution on smart net performance [D]. Monterey: Department of Computer Science, Naval Postgraduate School, 1997: 82–88.Google Scholar
- JITHESH P V, KELLY N, SIMPSON D R. Bioinformatics application integration and management in GeneGrid: experiments and experiences [C]// All Hands Meeting (AHM2004), Nottingham, UK: Jhon Wiley and Sons Ltd, 2004: 91–99.Google Scholar
- JANG S, TAYLOR V, WU X, PRAJUGO M, DEEL-MAN E, MEHTA G, VAHI K. Performance prediction-based versus load-based site selection: quantifying the difference [C]// Proceedings of the 18th International Conference on Parallel and Distributed Computing Systems. Las Vegas: ACTA Press, 2005: 101–110.Google Scholar
- SPEC. SPEC CPU2000 Results [EB/OL]. [2009-07-30]. https://doi.org/www.specbench.org/osg/cpu2006/results/cpu2006.html.