Autonomic Computing and Communications Systems: Third by João Ferreira, João Leitão, Luis Rodrigues (auth.),

By João Ferreira, João Leitão, Luis Rodrigues (auth.), Athanasius V. Vasilakos, Roberto Beraldi, Roy Friedman, Marco Mamei (eds.)

This booklet constitutes the completely refereed post-conference court cases of the thirs overseas ICST convention on Autonomic Computing and verbal exchange platforms, Autonomics 2009, held in Cyprus Univesity of expertise, Limassol, Cyprus, in September 2009.

The eleven revised complete papers out of 26 submitted and hosted five invited talks, masking numerous points of autonomic computing together with program, middelware, networking protocols and evaluation.

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Additional info for Autonomic Computing and Communications Systems: Third International ICST Conference, Autonomics 2009, Limassol, Cyprus, September 9-11, 2009, Revised Selected Papers

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In the former case, a pipeline-like effect is present, provided that a real stream processing situation occurs. In the latter, the overall computation is a client-server schema. e. the server) and it waits for the corresponding results. To increase the performance of each client we can parallelize the server with a proper parallelism degree: – in a task farm structure it is equal to the number of clients; – in a data parallel structure it is independent of the number of clients and can be obtained by the proper cost model of the parallel structure.

The second algorithm performs less steps: q = log2 (N − 1) transformation steps and only one resolution step. t. the second algorithm. This because the second algorithm applies, at each step, the equations (1) to all system elements, instead of only a subset of them. The second part of both algorithms involves the same number of operations. – Number of Functional Dependencies: The first algorithm includes a lower number of functional dependencies because, at each step of the transformation part, equations 1 are solved only for a subset of elements.

G. the average service time for a stream computation or the completion time of a single task) and the the quality of computed results. In this paper we show how multiple versions of a same parallel module can be introduced to target performance issues for different computing nodes, to face with the dynamic nature of pervasive grids and to meet dynamic user requests. g. failures and user requests). g. skeletons). g. related to resource availability and sensor data). This feature can be expressed by exploiting performance models of structured parallel computations [19].

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