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IJML 2012 Vol.2(2): 99-106
doi: 10.7763/IJMLC.2012.V2.96

Towards A Grid Collaborative Framework

Binh Thanh Nguyen1 , Duc Huu Nguyen1 , Doan Bang Hoang2

  • 1school of Electronics and Telecommunication of the Hanoi University of Science and Technology, Vietnam.
  • 2Computing and Communications, Faculty of Engineering and Information Technology, the University of Technology, Sydney (UTS), Australia.

Abstract

In Grid Computing, the grid collaborative frameworks that support easy and effective collabaration and coordination between many remote users have emerged as an important research topic recently. In our previous paper [1], we have proposed a grid collaborative framework that is both general purpose and plan supported. With the theoretical foundation based on the activity theory and designed on top of existing OGSA infrastructure, our proposed framework aims at accelerating the development of grid collaborative systems that consider working plans as central role. To support plans, our framework needs including a workflow language that not only can invoke Web services, but also Grid services. Among current workflow languages, the BPEL seems to be most suitabe for our framework, but it still lacks of Grid service invocation capability. Therefore, in our other work [2], a clean solution for this problem has been proposed by using ODE engine. This paper aims to combine these results into a more complete picture of our framework.

Keywords

  • Grid collaborative framework
  • Grid services
  • BPEL
  • BPEL engine
  • ODE
  • Grid computing
96-A377

How to Cite

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Binh Thanh Nguyen, Duc Huu Nguyen, and Doan Bang Hoang, "Towards A Grid Collaborative Framework," International Journal of Machine Learning, vol. 2, no. 2, pp. 99-106, 2012. https://doi.org/10.7763/IJMLC.2012.V2.96

Copyright & License

Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • DOI: 10.18178/IJML
  • Frequency: Quarterly
  • Average Days to Accept: 68 days
  • Acceptance Rate: 27%
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • DOI: 10.18178/IJML
  • Abstracing/Indexing: Google Scholar, Crossref, ProQuest, Electronic Journals Library, CNKI.
  • E-mail: editor@ijml.org
  • APC: 500USD