Download Advances in Grid and Pervasive Computing: 5th International by Peter Sloot (auth.), Paolo Bellavista, Ruay-Shiung Chang, PDF

By Peter Sloot (auth.), Paolo Bellavista, Ruay-Shiung Chang, Han-Chieh Chao, Shin-Feng Lin, Peter M. A. Sloot (eds.)

This ebook constitutes the complaints of the fifth overseas convention, CPC 2010 , held in Hualien, Taiwan in may perhaps 2010. The sixty seven complete papers are conscientiously chosen from 184 submissions and concentrate on issues corresponding to cloud and Grid computing, peer-to-peer and pervasive computing, sensor and moile networks, service-oriented computing, source administration and scheduling, Grid and pervasive functions, semantic Grid and ontologies, cellular trade and companies.

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Extra info for Advances in Grid and Pervasive Computing: 5th International Conference, GPC 2010, Hualien, Taiwan, May 10-13, 2010. Proceedings

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Fr). fr). References 1. : Scheduling heuristics for efficient broadcast operations on grid environments. In: IPDPS (2006) 2. : Broadcasting on meshes with wormhole routing. J. Parallel Distrib. Comput. 35(2), 111–122 (1996) 3. : Efficient collective communication in distributed heterogeneous systems. J. Parallel Distrib. Comput. 63(3), 251–263 (2003) 4. : A grid-enabled MPI: Message passing in heterogeneous distributed computing systems. In: Proceedings of SC 1998. ACM Press, New York (1998) 5. : Scheduling for atomic broadcast operation in heterogeneous networks with one port model.

Simulation represented in Figure 1 show that bottomUp keeps giving good performances as well as our new heuristic ’MostCrit’ even though they do not give exactly the same values. Other heuristics behave worse. The conclusion of our simulations is that BottomUp and MostCrit heuristics give evenly good results independently of the ratio of inter-cluster communication performances over intra-cluster communication performances. This point has never been observed before to our knowledge. time in ms 9000 20000 ECEFLA ECEFLAT ECEFLAt Bup MOSTCrit 18000 time in ms 10000 8000 7000 16000 14000 12000 6000 5000 ECEFLA ECEFLAT ECEFLAt Bup MOSTCrit 10000 5 10 15 20 25 30 35 40 Number of clusters 45 50 55 55 5 10 15 20 25 30 35 40 Number of clusters Fig.

There was, however, not much motivation and hence effort to make desktop applications scale across multiple machines. Indeed, the additional effort for the parallelization of those applications would hardly pay off especially in a situation when a typical user did not have multiple computers available. org/) or Clondike [2] that can easily transform a small network of machines into a cluster at the operating system level. -S. Chang et al. ): GPC 2010, LNCS 6104, pp. 15–26, 2010. c Springer-Verlag Berlin Heidelberg 2010 16 ˇˇtava and P.

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