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Batch Resizing Policies and Techniques for Fine-Grain Grid Tasks: The Nuts and Bolts

  • Muthuvelu, Nithiapidary (Multimedia University, Persiaran Multimedia) ;
  • Chai, Ian (Multimedia University, Persiaran Multimedia) ;
  • Chikkannan, Eswaran (Multimedia University, Persiaran Multimedia) ;
  • Buyya, Rajkumar (Cloud Computing and Distributed Systems (CLOUDS) Laboratory, Dept. of Computer Science and Software Engineering, The University of Melbourne)
  • Received : 2010.10.01
  • Accepted : 2011.01.25
  • Published : 2011.06.30

Abstract

The overhead of processing fine-grain tasks on a grid induces the need for batch processing or task group deployment in order to minimise overall application turnaround time. When deciding the granularity of a batch, the processing requirements of each task should be considered as well as the utilisation constraints of the interconnecting network and the designated resources. However, the dynamic nature of a grid requires the batch size to be adaptable to the latest grid status. In this paper, we describe the policies and the specific techniques involved in the batch resizing process. We explain the nuts and bolts of these techniques in order to maximise the resulting benefits of batch processing. We conduct experiments to determine the nature of the policies and techniques in response to a real grid environment. The techniques are further investigated to highlight the important parameters for obtaining the appropriate task granularity for a grid resource.

References

  1. F. Berman, G. C. Fox, A. J. G. Hey, “Grid Computing - Making the Global Infrastructure a Reality”, Wiley and Sons, Mar., 2003.
  2. R. Buyya, S. Date, Y. M. Natsumoti, S. Venugopal, “Neuroscience Instrumentation and Distributed Analysis of Brain Activity Data: A Case for e-Science on Global Grids”, Concurrency and Computation: Practice and Experience (CCPE), Vol.17, No.15, pp.1783-1798, 2005. https://doi.org/10.1002/cpe.888
  3. M. Baker, R. Buyya, D. Laforenza, “Grids and Grid Technologies for Wide-Area Distributed Computing”, Software: Practice and Experience (SPE), Vol.32, No.15, pp.1437-1466, 2002. https://doi.org/10.1002/spe.488
  4. B. Jacob, M. Brown, K. Fukui, N. Trivedi, “Introduction to Grid Computing”, IBM Publication, Dec., 2005.
  5. S. Venugopal, R. Buyya, W. Lyle, “A Grid Service Broker for Scheduling e-Science Applications on Global Data Grids”, Concurrency and Computation: Practice and Experience (CCPE), Vol. 18, pp.685-699, 2006. https://doi.org/10.1002/cpe.974
  6. H. James, K. Hawick, P. Coddington, “Scheduling Independent Tasks on Meta-Computing Systems”, In Proceedings of Parallel and Distributed Computing Systems, Fort Lauderdale, pp. 156-162, 1999.
  7. E. G. Coffman, Jr., M. Yannakakis, M. J. Magazine, C. Santos, “Batch Sizing and Job Sequencing on a Single Machine”, Annals of Operation Research, Vol.26, No. 1-4, pp.135-147, 1990. https://doi.org/10.1007/BF02248589
  8. T. Cheng, M. Kovalyov, “Single Machine Batch Scheduling with Sequential Job Processing”, IIE Transactions, Vol.33, No.5, pp.413-420, 2001.
  9. G. Mosheiov, D. Oron, “A Single Machine Batch Scheduling Problem with Bounded Batch Size”, European Journal of Operational Research, Vol.187, No.3, pp.1069-1079, 2008. https://doi.org/10.1016/j.ejor.2006.01.052
  10. K. E. Maghraoui, T. J. Desell, B. K. Szymanski, C. A. Varela, “The Internet Operating System: Middleware for Adaptive Distributed Computing”, International Journal of High Performance Computing Applications, Vol.20, No.4, pp.467-480, 2006. https://doi.org/10.1177/1094342006068411
  11. A. C. Sodan, A. Kanavallil, B. Esbaugh, "Group-based Optimisation for Parallel Job Scheduling with Scojo-PECT-O", In Proceedings of the 22nd International Symposium on High Performance Computing Systems and Applications, p.102- 109, Washington, DC, USA, 2008. IEEE Computer Society. https://doi.org/10.1109/HPCS.2008.19
  12. N. Muthuvelu, J. Liu, N. L. Soe, S. Venugopal, A. Sulistio, R. Buyya, “A Dynamic Job Groupingbased Scheduling for Deploying Applications with Fine-Grained Tasks on Global Grids”, In Proceedings of the Australasian Workshop on Grid Computing and E-Research, p. 41-48, Australian Computer Society, Inc., 2005.
  13. W. K. Ng, T. Ang, T. Ling, C. Liew, “Scheduling Framework for Bandwidth-Aware Job Groupingbased Scheduling in Grid Computing”, Malaysian Journal of Computer Science, Vol.19, No.2, pp.117-126, 2006.
  14. J. H. Stokes, “Behind the Benchmarks: Spec, Gflops, MIPS et al”, http://arstechnica.com/cpu/2q99/benchmarking-2.html, 2000.
  15. N. Muthuvelu, I. Chai, E. Chikkannan, “An Adaptive and Parameterized Job Grouping Algorithm for Scheduling Grid Jobs”, In Proceedings of the 10th International Conference on Advanced Communication Technology, Vol. 2, pp.975-980, 2008.
  16. N. Muthuvelu, I. Chai, E. Chikkannan, R. Buyya, “On-line Task Granularity Adaptation for Dynamic Grid Applications”, In Proceedings of the 10th International Conference on Algorithms and Architectures for Parallel Processing, Vol.6081, pp. 266-277, 2010. https://doi.org/10.1007/978-3-642-13119-6_24
  17. A. Ghobadi, C. Eswaran, N. Muthuvelu, I. K. T. Tan, Y. L. Kee, “An Adaptive Wrapper Algorithm for File Transfer Applications to Support Optimal Large File ransfers”, In Proceedings of the 11th International Conference on Advanced Communication Technology, p.315-320, Piscataway, NJ, USA, 2009. IEEE Press.
  18. J. Feng, G. Wasson, M. Humphrey, "Resource Usage Policy Expression and Enforcement in Grid Computing", In Proceedings of the 8th IEEE/ACM International Conference on Grid Computing, p.66-73, Washington, DC, USA, 2007. IEEE Computer Society. https://doi.org/10.1109/GRID.2007.4354117
  19. R. G. O. Arnon, “Fallacies of Distributed Computing Explained”, http://www.webperformancematters.com/, 2007.
  20. B. Lowekamp, B. Tierney, L. Cottrell, R. H. Jones, T. Kielmann, M. Swany, “A Hierarchy of Network Performance Characteristics for Grid Applications and Services”, Global Grid Forum, Jun., 2003.
  21. P. Huang, H. Peng, P. Lin, X. Li, “Static Strategy and Dynamic Adjustment: An Effective Method for Grid Task Scheduling”, Future Generation Computer Systems (FGCS), Vol.25, No.8, pp.884-892, 2009. https://doi.org/10.1016/j.future.2009.03.005

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