GlobalStat: a statistics service for diverse data collaboration and integration in grid

Beijing, Volume 2005, Issue null, 2005, Pages 6pp.-602.

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As a utility Grid service, there are still some other complements should be made for GlobalStat, such as authorization and encryption system, metadata description and effective data transmission

Abstract:

The rise of extensive computing has spawned an urgent need of information integration and collaboration. In this paper, we present a Grid Statistics Service (GlobalStat), a utility Grid service designed to provide an easy, safe, scalable and stable solution to integrate diverse data and get a global Grid view by statistics methodology. Th...More

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Introduction
  • With a continuously development of Grid, more and more resources, applications and computing data are to be integrated with all shapes and sizes, from disparate organizations[2].
  • Since the resources coordinated in Grid are various in types, it is still hard for current monitor to overcome the semantic limitation of the data it acquires.
  • In most case, it is possible be cognized and process only the system information.
  • While GlobalStat provides a wild semantics freedom, it can globally discover user-defined data oriented by human, application as well as system
Highlights
  • With a continuously development of Grid, more and more resources, applications and computing data are to be integrated with all shapes and sizes, from disparate organizations[2]
  • Since the resources coordinated in Grid are various in types, it is still hard for current monitor to overcome the semantic limitation of the data it acquires
  • GlobalStat organizes participants by sending out StatInvitations, and we introduce the statistics participant list to control the area StatInvition distributed as well as invoke most potential participants farthest
  • As a utility Grid service, there are still some other complements should be made for GlobalStat, such as authorization and encryption system, metadata description and effective data transmission
Methods
  • Design of GlobalStat

    In this part, the authors will give an overview of the architecture of GlobalStat, and show the details in working protocols as well as some special policies.

    3.1 System Framework

    The system structure of a running Grid statistics is roughly shown in Figure 1.
Conclusion
  • Conclusion and Future Work

    In this paper, the authors characterize the large-scale data collecting and integrating as a distinct Grid service component, GlobalStat, which supports extensive data integration and collaboration in dynamic, heterogeneous environment of Grid.

    GlobalStat is under construction.
  • The authors characterize the large-scale data collecting and integrating as a distinct Grid service component, GlobalStat, which supports extensive data integration and collaboration in dynamic, heterogeneous environment of Grid.
  • GlobalStat is under construction.
  • This paper presented many of the design elements and algorithms of GlobalStat, several have been implemented.
  • As a utility Grid service, there are still some other complements should be made for GlobalStat, such as authorization and encryption system, metadata description and effective data transmission.
Summary
  • Introduction:

    With a continuously development of Grid, more and more resources, applications and computing data are to be integrated with all shapes and sizes, from disparate organizations[2].
  • Since the resources coordinated in Grid are various in types, it is still hard for current monitor to overcome the semantic limitation of the data it acquires.
  • In most case, it is possible be cognized and process only the system information.
  • While GlobalStat provides a wild semantics freedom, it can globally discover user-defined data oriented by human, application as well as system
  • Methods:

    Design of GlobalStat

    In this part, the authors will give an overview of the architecture of GlobalStat, and show the details in working protocols as well as some special policies.

    3.1 System Framework

    The system structure of a running Grid statistics is roughly shown in Figure 1.
  • Conclusion:

    Conclusion and Future Work

    In this paper, the authors characterize the large-scale data collecting and integrating as a distinct Grid service component, GlobalStat, which supports extensive data integration and collaboration in dynamic, heterogeneous environment of Grid.

    GlobalStat is under construction.
  • The authors characterize the large-scale data collecting and integrating as a distinct Grid service component, GlobalStat, which supports extensive data integration and collaboration in dynamic, heterogeneous environment of Grid.
  • GlobalStat is under construction.
  • This paper presented many of the design elements and algorithms of GlobalStat, several have been implemented.
  • As a utility Grid service, there are still some other complements should be made for GlobalStat, such as authorization and encryption system, metadata description and effective data transmission.
Reference
  • Ian Foster, What is the Grid? A Three Point Checklist. July 20, 2002, 2. Ann Chervenak, Ian Foster, Carl Kesselman, Charles Salisbury, Steven Tuecke, The Data Grid: Towards an Architecture for the Distributed Management and Analysis of Large Scientific Datasets. Journal of Network and Computer Applications: Special Issue on Network-Based Storage Services, vol. 23, no. 3, p. 187-200, July 2000.
    Google ScholarLocate open access versionFindings
  • 3. http://www.ogsadai.org.uk/4. Stephen Langella, Shannon L. Hastings, Scott Oster, Tahsin M. Kurc, Umit V. Catalyurek, Joel H. Saltz, "A Distributed Data Management Middleware for Data-Driven Application Systems", Proceedings of the 2004 IEEE International Conference on Cluster Computing, 2005.
    Locate open access versionFindings
  • 5. Gurmeet Singh, Shishir Bharathi, Ann Chervenak, Ewa Deelman, Carl Kesselman, Mary Manohar, Sonal Patil, Laura Pearlman, A MetaData Catalog Service for Data Intersive Applications. SC’03.
    Google ScholarFindings
  • 6. Bill Allcock, Joe Bester, John Bresnahan, An L. Chervenak, Ian Foster, Carl Kesselman, Sam Meder, Veronika Nefedova, Darcy Quesnel, Steven Tuecke, Data Management and Transfer in High Performance Computational Grid Enviroments.
    Google ScholarFindings
  • 7. W. Allcock, A. Chervenak, I. Foster, L. Pearlman, V. Welch, M. Wilde, Clobus Toolkit Support for Distributed Data-Intensive Science.
    Google ScholarFindings
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