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By Abdelhamid Mellouk

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CCE is its configuration environment. CCT can support the TDPC in the logical computer cluster. Global Computing Group (GCG) is defined as GCG (id, Am, ICAS, CCTS, GKS, GCE), where id denotes the identifier of GCG; Am denotes the main control agent of GCG; ICAS denotes the set of ICA which GCG includes; CCTS denotes the set of CCT which GCG includes; GKS is its knowledge set. GCE is its configuration environment. Multi-cluster grid (MCG) is defined as MCG (Ma, CCS, N,R, GCG), where Ma denotes the main computer of MCG; CCS denotes the set of all computer clusters which MCG includes; N is the connection network set of MCG; R is the rules of connections; GCG is the global computing group.

57, no. 1, pp. 125-135. P. (2003). Optimal Solution of Integer Multicommodity Flow Problem with Application in Optical Networks. Proc. Of Symposium on Global Optimisation, June, 411-435. , Royer. , (2000). “Quality of Service for Ad hoc On-Demand Distance Vector Routing”, IETF Internet Draft. , (1961). Elements of Queueing Theory and its applications, Mac Graw-Hill Ed. , (2002). “Energy aware routing for low energy ad hoc sensor networks”. Proceeding of IEEE Wireless Communications and Networking Conference (WCNC), Orlando, FL, USA.

T 0 12 24 36 48 60 48 60 72 84 (a) N=10 0 12 24 36 72 84 t (b) Fig. 4. The results of tests 7. Conclusions In order to support the computation-intensive tasks, we collect the idle computational resources of CSCW environment to construct the multi-cluster grid. Because of the heterogeneous resources, the state of the idle computing resources changes in the process of the computing and the task migration. For fitting the state changes, the dynamic rule mechanisms of agents are proposed. According to the Grid techniques, Computing Agent, Cooperation Computing Team, the state space, the action space, the dynamic rule space, the reinforcement learning and so on, a cooperative learning model of agent was designed and implemented in this chapter.

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