Journal of Grid Computing

, Volume 8, Issue 1, pp 109–131 | Cite as

An Adaptive Execution Scheme for Achieving Guaranteed Performance in Computational Grids

  • Ajanta De Sarkar
  • Sarbani Roy
  • Dibyajyoti Ghosh
  • Rupam Mukhopadhyay
  • Nandini Mukherjee


Nature of the resource pool in a Grid environment is heterogeneous and dynamic. Availability, load and status of the resources may change at the time of execution of an application. Therefore, in order to maintain the performance guarantee (as has been agreed upon through service level agreements (SLAs) between the client and the resource providers), an application may need to adapt to its run-time environment on the basis of resource availability and application demands. Often it may be required to migrate the application components to a new set of resources during their execution so that performance guarantee can be maintained. Objective of this paper is to present an adaptive execution scheme for achieving guaranteed performance on the basis of the SLAs. The scheme has been implemented based on the notion of performance properties and by deploying a set of autonomous agents within an integrated performance-based resource management framework.


Adaptive execution Performance properties Local tuning Migration and Grid 


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  1. 1.
    Bellifemine, F., Poggi, A., Rimassa, G.: Developing multi agent systems with a FIPA-compliant agent framework. Softw. Pract. Exp. 31, 103–128 (2000)CrossRefGoogle Scholar
  2. 2.
    Berman, F., et al.: New Grid scheduling and rescheduling methods in the grads project. Int. J. Parallel Program. 33(2), 209–229 (2005)CrossRefGoogle Scholar
  3. 3.
    Berman, F., et al.: Toward a framework for preparing and executing adaptive grid programs. In: Proceedings of the 16th International Parallel and Distributed Processing Symposium, pp. 322 (2002)Google Scholar
  4. 4.
    Bull, J.M., Kambities, M.E.: JOMP—an OpenMP-like interface for java. In: Proceedings of the ACM2000 Java Grande Conference, pp. 44–53 (2000)Google Scholar
  5. 5.
    Bull, J.M., Ford, R.W., Dickinson, A.: A feedback based load balance algorithm for physics routines in NWP. In: Hoffman, G.-R., Kreitz, N. (eds.) The Proceedings of the 7th Workshop on the Use of Parallel Processors in Meteorology, pp. 239–249. World Scientific, Hackensack, NJ (1996)Google Scholar
  6. 6.
    Byassee, J.: Unleash mobile agents using Jini, Leverage Jini to mitigate the complexity of mobile agent applications. (2002)
  7. 7.
    Castro, A.: Designing a multi-agent system for monitoring and operations recovery for an airline operations control centre. Master-thesis, University of Porto, Faculty of Engineering (2007)Google Scholar
  8. 8.
    Csaba, L., Lorincz, T., Kozsik, U.A., Horvath, Z.: A method for job scheduling in grid based on job execution status. Multiagent Grid Syst. 1(3), 197–208 (2005)MATHGoogle Scholar
  9. 9.
    Czajkowski, K., Foster, I., Kesselman, C.: Resource and service management, Chapter 18. In: Foster, I., Kesselman, C. (eds.) The Grid 2: Blueprint for a New Computing Infrastructure, 2 edn. Kaufmann, San Francisco, CA (2003)Google Scholar
  10. 10.
    De Sarkar, A., Ghosh, D., Mukhopadhyay, R., Mukherjee, N.: “Implementation of a Grid performance analysis and tuning framework using jade technology” has been accepted in The 2008 International Conference on Grid Computing and Applications (GCA’08: July 14–17, 2008) to be held in Las Vegas, USA (2008)Google Scholar
  11. 11.
    De Sarkar, A., Kundu, S., Mukherjee, N.: A hierarchical agent framework for tuning application performance in Grid environment. In: Proceedings of the 2nd IEEE Asia-Pacific Service Computing Conference, IEEE APSCC 2007, pp. 296–303. Tsukuba, Japan, 11–14 December (2007)Google Scholar
  12. 12.
    Fahringer T., Gerndt, M., Riley, G., Traff, J.: Knowledge specification for automatic performance analysis, APART technical report. Workpackage 2, identification and formalization of knowledge, technical report FZJ-ZAM-IB-9918, D-52425 Julich (1999)Google Scholar
  13. 13.
    Fahringer, T., Gerndt, M. Riley, G., Traff, J.: Formalizing OpenMP performance properties with ASL. In: Proceedings of the Third International Symposium on High Performance Computing ISHPC, pp. 428–439. 16–18 October (2000)Google Scholar
  14. 14.
    Fürlinger, K., Gerndt. M.: Finding inefficiencies in OpenMP applications automatically with periscope. In: Proceedings of the 2006 International Conference on Computational Science (ICCS 2006), vol. 2, pp. 494–501. Reading, UK (2006)Google Scholar
  15. 15.
    Furmento, N., Mayer, A., McGough, S., Newhouse, S., Field, T., Darlington, J.: ICENI: optimisation of component applications within a Grid environment. Parallel Comput. 28(12), 1753–1772 (2002)MATHCrossRefGoogle Scholar
  16. 16.
    Ganglia Information Provider. (2007)
  17. 17.
    Ghosh, D., Mukhopadhyay, R., De Sarkar, A., Mukherjee, N.: Study of the execution performance of parallel loops in OpenMP programs using different scheduling strategies. In: National Conference on Trends in Computing Technologies, Chennai, pp. 75–85 (2009)Google Scholar
  18. 18.
  19. 19.
    Hussein, M, Mayes, K., Luján, M., Gurd, J.: Adaptive performance control for distributed scientific coupled models. In: Proceedings of the 21st Annual International Conference on Supercomputing, Seattle, Washington. 17–21 June (2007)Google Scholar
  20. 20.
    Jade Administrator’s Guide. (2008)
  21. 21.
  22. 22.
    Jini. (2007)
  23. 23.
  24. 24.
    Kesler, J.C.: Overview of Grid Computing. MCNC, Research Triangle Park, NC (2003)Google Scholar
  25. 25.
    McGough, S., Cohen, J., Darlington, J., Katsiri, E., Lee, W., Panagiotidi, S., Patel, Y.: An end-to-end workflow pipeline for large-scale grid computing. J Grid Computing 3(3–4), 259–281 (2005)CrossRefGoogle Scholar
  26. 26.
    Performance property specification in Julich Supercomputing Centre (JSC). (2008)
  27. 27.
    Roy, S., Mukherjee, N.: Utilizing Jini features to implement a multiagent framework for performance-based resource allocation in grid environment. In: Proceedings of International Conference on Grid Computing and Applications (GCA’06). The 2006 World Congress in Computer Science, Computer Engineering, and Applied Computing, pp. 52–60. Las Vegas, 26–29 June (2006)Google Scholar
  28. 28.
    Roy, S.: Performance-based resource management in computational Grid environment. Ph.D. Thesis submitted in Jadavpur University, Faculty of Engineering (2007)Google Scholar
  29. 29.
    Roy, S., Sarkar, M., Mukherjee, N.: Optimizing resource allocation for multiple concurrent jobs in Grid environment. In: Proceedings of Third International Workshop on scheduling and Resource Management for Parallel and Distributed systems, SRMPDS’07, Hsinchu, Taiwan, 5–7 December (2007)Google Scholar
  30. 30.
    Sakellariou, R., Zhao, H.: A low-cost rescheduling policy for efficient mapping of workflows on grid systems. Sci. Program. 12(4), 253–262 (2004)Google Scholar
  31. 31.
    Scherer, A., Gross, T., Zwaenepoel, W.: An evaluation of adaptive execution of OpenMP task parallel programs. In: Proceedings of Languages, Compilers, and Runtimes for Scalable Computing (2000)Google Scholar
  32. 32.
  33. 33.
    Sun’s Jini. (2007)
  34. 34.
    Truong, H.-L., Fahringer, T., Dustdar, S.: Dynamic instrumentation, performance monitoring and analysis of grid scientific workflows. J Grid Computing 3(1–2), 1–18 (2005)CrossRefGoogle Scholar
  35. 35.
    Wrzesinska, G., Maassen, J. Bal, H.E.: Self-adaptive applications on the Grid. In: ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PpoPP’07), pp. 121–129. San Jose, CA, USA, 14–17 (2007)Google Scholar
  36. 36.
    Yu, J., Buyya, R.: A taxonomy of workflow management systems for grid computing. J Grid Computing 3(3–4), 171–200 (2005)CrossRefGoogle Scholar
  37. 37.
    Yu, Z., Shi, W.: An adaptive rescheduling strategy for Grid workflow applications. In: Proceedings of IPDPS, pp. 1–8 (2007)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Ajanta De Sarkar
    • 1
  • Sarbani Roy
    • 2
  • Dibyajyoti Ghosh
    • 2
  • Rupam Mukhopadhyay
    • 2
  • Nandini Mukherjee
    • 2
  1. 1.Department of Computer Science and EngineeringBirla Institue of Technology, MesraKolkataIndia
  2. 2.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia

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