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Abstract

Grid computing shares heterogeneous resources in dynamic service-based environments. This kind of system has the major advantage of enabling rapid composition of distributed applications. However, equilibrating the amount of work assigned to each of the nodes in a grid environment is a complex problem, even more so than for other kinds of parallel systems. In this paper, we present a new extension and reinterpretation of one of the most successful models of awareness in Computer Supported Cooperative Work (CSCW), called the Spatial Model of Interaction (SMI), which manages awareness of interaction through a set of key concepts, to manage task delivery in collaborative distributed systems. This model, called AMBLE (Awareness Model for Balancing the Load in Collaborative Grid Environments), also applies some theoretical principles and theories of multi-agent systems to create a collaborative and cooperative environment that provides autonomous, efficient and independent management of resources available in a grid environment. WE-AMBLE, a Workflow Engine to manage awareness in collaborative grid environments through the AMBLE concepts, provides a workflow engine to manage different levels of awareness, allowing different virtual organizations to share computational resources based on open protocols and interfaces.

Keywords

workflow collaborative environments awareness management delivery task grid environments

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References

Beltrán M. , Bosque, J.L. , and Guzmán, A. (2004). Resource dissemination policies on grids, in On the Move to Meaningful Internet Systems, Lectures Notes in Computer Science, Berlin: Springer-Verlag , pp. 135-144.

Benford S.D. and Fahlén, L.E. (1993). A spatial model of interaction in large virtual environments , in Proceedings of the Third European Conference on Computer Supported Cooperative Work (ECSCW'93), Kluwer Academic Publishers, pp. 109-124.

Berman, F. , Wolski, R. , Figueira, S. , Schopf, J. , and Shao, G. (1996). Application-level scheduling on distributed heterogeneous networks , in Proceedings of the ACM/IEEE Conference on Supercomputing (SC'96), Los Alamitos, CA: IEEE Computer Society.

Chen, J. and Yang, Y. (2006a). Multiple states based temporal consistency for dynamic verification of fixed-time constraints in grid workflow systems, Concurrency and Computation: Practice and Experience.

Chen J. and Yang, Y. (2006b). Selecting necessary and sufficient checkpoints for dynamic verification of fixed-time constraints in grid workflow systems , in Proceedings of the 4th International Conference on Business Process Management (BPM2006), Lectures Notes in Computer Science 4102, Berlin: Springer-Verlag, pp. 445-450.

Cheung W. K. et al. (2004). Dynamic resource selection for service composition in the grid . IEEE/WIC/ACM International Conference on Web Intelligence, Los Alamitos, CA: IEEE Computer Society Press, pp. 412-418.

Das, S.K. , Harvey, D.J. , and Biswas, R. (2001). Parallel processing of adaptive meshes with load balancing . IEEE Trans. on Parallel and Distributed Systems , 12: 1269- 1280.

Deelman, E. , Blythe, J. , Gil, Y. , and Kesselman, C. (2004). Workflow management in GriPhyN, in Grid Resource Management: state of the art and future trends, Kluwer Academic Publisher , pp. 99-116.

Fahlén, L.E. and Brown, C.G. (1992). The use of a 3D Aura metaphor for computer based conferencing and teleworking , in Proceedings of the 4th Multi-G Workshop, pp. 69- 74.

10.

Foster, I. , Jennings, N.R. , and Kesselman, C. (2004). Brain meets brawn: Why grid and agents need each other , in Proceedings of the 3rd International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2004).

11.

Foster, I. , Kesselman, C. , Nick, J. , and Tuecke, S. (2002). The Physiology of the grid: An open grid services architecture for distributed systems integration, Open Grid Service Infrastructure WG, Global Grid Forum.

12.

Greenhalgh, C. (1999). Large Scale Collaborative Virtual Environments, PhD thesis, University of Nottingham. Berlin: Springer-Verlag .

13.

Greenhalgh, C.M. , and Benford, S.D. (1995). MASSIVE: A virtual reality system for tele-conferencing , ACM Transactions on Computer Human Interfaces, 2(3): 239-261.

14.

Harchol-Balter, M. (2001). Job placement with unknown duration and no preemption , ACM SIGMETRICS Performance Evaluation Review, 28(4): 3-5.

15.

Harchol-Balter, M. and Downey, A.B. (1997). Exploiting process lifetime distributions for dynamic load balancing, ACM Trans. on Computer Systems, 15(3): 253-285.

16.

Kunz, T. (1991). The influence of different workload descriptions on a heuristic load balancing scheme , IEEE Transactions on Software Engineering, 17(7): 725-730.

17.

Letsch, T. (2000). Redesign and Implementation of a Mobile Agent System Compliant with the MAFFinder Part of the MASIF Standard, PhD thesis, Department of Informatics, Technischen Universität München .

18.

Oinn, T. et al. (2004). Taverna: a tool for the composition and enactment of bioinformatics workflows , Bioinformatics, 20(17): 3045-3054.

19.

Pastor L. and Bosque, J.L. (2001). An efficiency and scalability model for heterogeneous cluster , in Proceedings of the 3rd IEEE International Conference on Cluster Computing, Los Alamitos, CA: IEEE Computer Society Press, pp. 427-434.

20.

Russ, S.H. , Robinson, J. , Flachs, B.K. , and Heckel, B. (1998). The Hector distributed runtime environment , IEEE Transactions on Parallel and Distributed Systems, 9(11): 1002- 1114.

21.

Salvadores, M. , Herrero, P. , Pérez, M.S. , and Sanchez, A. (2005). Modelling the N + ROWA model approach inside the WS-Replication resource, in Second International On the Move Federated Conferences (OTM'05), Lecture Notes in Computer Science 3762, Berlin: Springer, pp. 397-405.

22.

Shen, W. et al. (2002). Adaptive negotiation for agent-based grid computing, Journal of the American Statistical Association .

23.

Tannenbaum, T. , Wright, D. , Miller, K. , and Livny, M. (2002). Condor- A Distributed Job Scheduler, in Beowulf Cluster Computing with Linux, Cambridge, MA: MIT Press.

24.

University of Tennessee (2006). Performance of various computers using standard Linear equations software, Technical Report CS-89-85, University of Tennessee , http://www.netlib.org/benchmark/performance.pdf accessed February, 2007.

25.

van Nieuwpoort, R.V. , Wrzesinska, G. , Maassen, J. , Kielmann, T. , and Bal, H.E. (2006). Adaptive load balancing for divide-and-conquer grid applications , Journal of Supercomputing.

26.

von Laszewski, G. et al. (2004). GridAnt: A client-controllable grid workflow system , in Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04), Los Alamitos, CA: IEEE Computer Society Press.

27.

Xiao, L. , Chen, S. , and Zhang, X. (2002). Dynamic cluster resource allocations for jobs with known and unknown memory demands , IEEE Trans. on Parallel and Distributed Systems, 13(3): 223-240.

28.

Xu, C. and Lau, F. (1997). Load balancing in parallel computers: theory and practice. Kluwer Academic Publishers .

29.

Yu J. and Buyya, R. (2005). A taxonomy of scientific workflow systems for grid computing , ACM SIGMOD Record, 34(3): 44-49.

30.

Zhang, Z. and Luo, S. (2003). Constructing grid system with mobile multiagent . Proc. of the 2nd Int. Conference on Machine Learning and Cybernetics, Xi'an.

31.

Zomaya, A.Y. and Teh, Y.-H. (2001). Observations on using genetic algorithms for dynamic load-balancing , IEEE Trans. on Parallel and Distributed Systems , 12(9): 899-911.

WE-AMBLE: a Workflow Engine To Manage Awareness in Collaborative Grid Environments

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