3.7 Conclusions and Future Work
It should be clear that the development of complex and scalable systems requires some form of intelligence. We cannot design general policies and strategies which do not take into account the current state of a system. But the state space of a complex system is very large and it is unfeasible to create a rigid control infrastructure. The only alternative left is to base our actions on logical inference. This process requires a set of policy rules and facts about the state of the system, gathered by a monitoring agent. Similar arguments show that we need to plan if we wish to optimally use the resource-rich environment of a computational grid, subject to quality of service constraints. Further optimization is only possible if various entities making decisions have also the ability to learn. Our future work will allow us to perform more comprehensive measurements on the testbed system we are currently developing. Data collected from these experiments will allow us to create realistic models of large-scale system and study their scalability.
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References
L. Bölöni, D.C. Marinescu, J.R. Rice, P. Tsompanopoulu, and E.A. Vavalis, Agent-based Scientific Simulation and Modeling, Concurrency Practice and Experience 12, 845–861 (2000)
L. Bölöni, K.K. Jun, K. Palacz, R. Sion, and D.C. Marinescu, The Bond Agent System and Applications, in Agent Systems, Mobile Agents, and Applications, Vol. 1882, ed. by D. Kotz and F. Mattern Lecture Notes on Computer Science (Springer-Verlag, 2000), pp. 99–112
A. Borgida and T. Murata, Tolerating Exceptions in Workflows: A Unified Framework for Data and Processes, in Proc. Int. Joint Conference on Work Activities, Coordination and Collaboration (WAC-99), ed. by D. Georgakopoulos, W. Prinz, and A.L. Wolf (ACM Press, New York, 1999), pp. 59–68
I. Foster and C. Kesselman, The Grid: Blueprint for a New Computer Infrastructure, 1st. Edn. (Morgan Kaufmann, San Francisco, CA, 1999)
T. Goss-Walter, R. Letz, T. Kentemich, H.-C. Hoppe, and P. Wieder, An Analysis of the UNICORE Security Model, GGF document GFD-I.18, July 2003
W.E. Grosso, H. Eriksson, R.W. Fergerson, J.H. Gennari, S. Tu, and M.A. Musen, Knowledge Modeling at the Millennium (The Design and Evolution of protégé-2000), in Proc. 12 th International Workshop on Knowledge Acquisition, Modeling and Mangement (KAW’99), Canada, 1999
T. Gruber, A Translation Approach to Portable Ontology Specifications, Knowledge Acquisition 5, 199–220 (1993)
M. Harchol-Balter, T. Leighton, and D. Lewin, Resource Discovery in Distributed Networks, in Proc. 18th Annual ACM Sym. on Principles of Distributed Computing, PODC’99 Piscataway, New Jersey, (IEEE Press, 1999) pp. 229–237
M. Humphrey and M. Thompson, Security Implications of Typical Grid Computing Usage Scenarios, Global Grid Forum document GFD-I.12, October 2000
E. Friedman-Hill, Jess, the Java Expert System Shell, Technical Report Sand 98-8206, Sandia National Laboratory, 1999
D.C. Marinescu, Reflections on Qualitative Attributes of Mobile Agents for Computational, Data, and Service Grids, in Proc. of First IEEE/ACM Symp. on Cluster Computing and the Grid (2001), pp. 442–449
D.C. Marinescu, Internet-based Workflow Management: Towards a Semantic Web, (Wiley, New York 2002)
D.C. Marinescu, G.M. Marinescu, and Y. Ji, The Complexity of Scheduling and Coordination on Computational Grids, in Process Coordination and Ubiquitous Computing, ed. by D.C. Marinescu and C. Lee (CRC, 2002), pp. 119–132
D.C. Marinescu and Y. Ji, A Computational Framework for the 3-D Structure Determination of Viruses with Unknown Symmetry, Journal of Parallel and Distributed Computing 63, 738–758 (2003)
A. Omicini, F. Zamborelli, M. Klush, and R. Tolksdorf, Coordination of Internet Agents: Models, Technologies and Applications (Springer-Verlag, Heidelberg, 2001)
R. Raman, M. Livny, and M. Solomon, Matchmaking: Distributed Resource Management for High Throughput Computing, in Proc. 7th IEEE Int. Symp. on High Perf. Distrib. Comp., Piscataway, New Jersey (IEEE Press, 1998), pp. 140–146
M.R. Thomson, D. Olson, R. Cowles, S. Mullen and M. Helm, CA-based Trust Issues for Grid Authentication and Identity Delegation, Global Grid Forum document GFD-I.17, June 2003
L. Tobin, M. Steve, and W. Peter, T Spaces: The Next Wave, IBM System Journal 37(3), 454–474 (1998)
VINT Project, The NS Manual, 2003
T. Winograd, Language as a Cognitive Process (Addision-Wesley, Reading, MA, 1983)
H. Yu, D.C. Marinescu, A.S. Wu, and H.J. Siegel, A Genetic Approach to Planning in Heterogeneous Computing Environments, in Proc. 17th Int. Parallel and Distributed Proc. Symp. (IPDPS 2003), Nice, France, 2003 (IEEE Computer Society Press, 2003)
H. Yu, X. Bai, G. Wang, Y. Ji, and D.C. Marinescu, Metainformation and Workflow Management for Solving Complex Problems in Grid Environments, Proc. Heterogeneous Computing Workshop, 2004
Global Grid Forum, URL http://www.gridforum.org, 2001
JADE Website, URL http://sharon.cselt.it/projects/jade/
Protégé Website, URL http://protege.stanford.edu/
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Bai, X. et al. (2006). Intelligent Grids. In: Cunha, J.C., Rana, O.F. (eds) Grid Computing: Software Environments and Tools. Springer, London. https://doi.org/10.1007/1-84628-339-6_3
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