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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(2): 99-106 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.96

Towards A Grid Collaborative Framework

Binh Thanh Nguyen, Duc Huu Nguyen, and Doan Bang Hoang

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.

Index Terms—Grid collaborative framework, Grid services, BPEL, BPEL engine, ODE, Grid computing.

Binh T. Nguyen and Duc Huu Nguyen are with the school of Electronics and Telecommunication of the Hanoi University of Science and Technology, Vietnam (e-mail: ntbinh1974@gmail.com; ducnh-fit@mail.hut.edu.vn).
Doan B. Hoang is with Computing and Communications, Faculty of Engineering and Information Technology, the University of Technology, Sydney (UTS), Australia (e-mail: Doan.Hoang@uts.edu.au).

[PDF]

Cite: Binh Thanh Nguyen, Duc Huu Nguyen, and Doan Bang Hoang, "Towards A Grid Collaborative Framework," International Journal of Machine Learning and Computing vol. 2, no. 2, pp. 99-106, 2012.

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