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.
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References
Foster I, Kesselman C, Tueche S (2001) The anatomy of the grid: enabling scalable virtual organizations. Int J Supercomput Appl 15(3):200–222
Foster I, Kesselman C (1999) The grid: blueprint for a future computing infrastructure. Morgan Kaufmann, San Mateo
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
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
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
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
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
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
Foster I, Kesselman C (1997) Globus: a metacomputing infrastructure toolkit. Int J Supercomput Appl 11(2):115–128
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
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
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
Haban D, Shin KG (2000) Application of real-time monitoring to scheduling tasks with random execution times. IEEE Trans Softw Eng 16:1374–1389
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
Bemmerl T, Lindhof R, Treml T (1990) The distributed monitor system of TOPSYS. In: Proceeding CONPAR, pp 756–765
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
Microsoft Corporation. Performance Data Helper Library, http://msdn.microsoft.com/library/enus/perfmon/base/performance_data_helper.asp
Sun Microsystems, Inc. Overview of the JNI (Java Native Interface)
Stevens RT (1990) Advanced fractal programming in C. M&T Books, pp 117–123
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Song, EH., Yang, L.T. & Jeong, YS. 3-Hierarchical resource management model on web grid service architecture. J Supercomput 46, 257–275 (2008). https://doi.org/10.1007/s11227-008-0212-2
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DOI: https://doi.org/10.1007/s11227-008-0212-2
Keywords
- Grid service
- Hierarchical resource management
- Grid resource allocation and management
- Grid resource monitoring information visualization
- Task allocation algorithm