The Journal of Supercomputing

, 46:257 | Cite as

3-Hierarchical resource management model on web grid service architecture

Article

Abstract

In this paper, we developed a framework for efficient resource management within the grid service environment. For considering the grid service architecture and functions, the resource management is the most important to grid service; therefore, GridRMF (Grid Resource Management Framework) is modeled and developed in order to respond to such variable characteristics of resources as accordingly as possible. GridRMF uses the participation level of grid resource as a basis of its hierarchical management. This hierarchical management divides managing domains into two parts: VMS (Virtual Organization Management System) for virtual organization management and RMS (Resource Management System) for metadata management. VMS mediates resources according to optimal virtual organization selection mechanism, and responds to malfunctions of the virtual organization by LRM (Local Resource Manager) automatic recovery mechanism. RMS, on the other hand, responds to load balance and fault by applying resource status monitoring information into adaptive performance-based task allocation algorithm.

Keywords

Grid service Hierarchical resource management Grid resource allocation and management Grid resource monitoring information visualization Task allocation algorithm 

References

  1. 1.
    Foster I, Kesselman C, Tueche S (2001) The anatomy of the grid: enabling scalable virtual organizations. Int J Supercomput Appl 15(3):200–222 Google Scholar
  2. 2.
    Foster I, Kesselman C (1999) The grid: blueprint for a future computing infrastructure. Morgan Kaufmann, San Mateo Google Scholar
  3. 3.
    Foster I, Kesselman C (2002) The physiology of the Grid: An open Grid services architecture for distributed systems integration. In: Open grid service infrastructure WG, global grid forum Google Scholar
  4. 4.
    Buyya R, Chapin S, DiNucci D (2000) Architectural models for resource management in the grid. In: Grid computing grid 2000. Lecture notes in computer science, vol 1971, pp 18–34 Google Scholar
  5. 5.
    Kim KH, Buyya R (2007) Fair resource sharing in hierarchical virtual organizations for global grids. In: Proceedings of the 8th IEEE/ACM international conference on grid computing (Grid 2007). IEEE Press, New York, pp 50–57 Google Scholar
  6. 6.
    Venugopal S, Buyya R, Winton L (2006) A grid service broker for scheduling e-science applications on global data grids. Concurr Comput: Pract Exp 18(6):685–699 CrossRefGoogle Scholar
  7. 7.
    Jeong YS, Song EH, Xu CZ (2005) Dynamic and adaptive parallel task processing on GRID service architecture. In: Proceedings of the 23rd IASTED international multi-conference, PDCN, pp 270–275 Google Scholar
  8. 8.
    Jeong YS, Park BJ, Song EH (2004) Realtime monitoring for parallel distributed processing system based on Internet. In: VECPAR04, high performance computing for computational science, Spain, pp 857–862 Google Scholar
  9. 9.
    Foster I, Kesselman C (1997) Globus: a metacomputing infrastructure toolkit. Int J Supercomput Appl 11(2):115–128 Google Scholar
  10. 10.
    Chapin SJ, Katramatos D, Karpovich J, Grimshaw AS (1999) The legion resource management system. In: IPDPS workshop on job scheduling strategies for parallel processing. Lecture notes in computer science, vol 1659, pp 162–178 Google Scholar
  11. 11.
    Casanova H, Dongarra J (1997) NetSolve: a network server for solving computational science problems. Int J Supercomput Appl High Perform Comput 11(3):21–223 Google Scholar
  12. 12.
    Waheed A, Smith W, George J, Yan J (2000) An infrastructure for monitoring and management in computational grids. Lect Not Comput Sci 1915:235–245 CrossRefGoogle Scholar
  13. 13.
    Haban D, Shin KG (2000) Application of real-time monitoring to scheduling tasks with random execution times. IEEE Trans Softw Eng 16:1374–1389 CrossRefGoogle Scholar
  14. 14.
    Domingues P, Silva L, Silva JG (2003) DRMonitor—a distributed resource monitoring system. In: Proceeding of the eleventh euromicro conference on parallel, distributed and network-based processing, pp 127–133 Google Scholar
  15. 15.
    Bemmerl T, Lindhof R, Treml T (1990) The distributed monitor system of TOPSYS. In: Proceeding CONPAR, pp 756–765 Google Scholar
  16. 16.
    Knop MW, Paritosh PK, Dinda PA, Schopf JM (2001) Windows performance monitoring and data reduction using watchtower and argus. Technical Report NWU-CS-01-6, Department of Computer Science, Northwestern University Google Scholar
  17. 17.
    Microsoft Corporation. Performance Data Helper Library, http://msdn.microsoft.com/library/enus/perfmon/base/performance_data_helper.asp
  18. 18.
    Sun Microsystems, Inc. Overview of the JNI (Java Native Interface) Google Scholar
  19. 19.
    Stevens RT (1990) Advanced fractal programming in C. M&T Books, pp 117–123 Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Eun-Ha Song
    • 1
  • Laurence T. Yang
    • 2
  • Young-Sik Jeong
    • 1
  1. 1.Department of Computer EngineeringWonkwang UniversityIksanS. Korea
  2. 2.Department of Computer ScienceSt. Francis Xavier UniversityAntigonishCanada

Personalised recommendations