Download Cloud Computing: First International Conference, CloudComp by Guilherme Koslovski, Tram Truong Huu, Johan Montagnat, PDF

By Guilherme Koslovski, Tram Truong Huu, Johan Montagnat, Pascale Vicat-Blanc Primet (auth.), Dimiter R. Avresky, Michel Diaz, Arndt Bode, Bruno Ciciani, Eliezer Dekel (eds.)

This ebook indicates the convention court cases of CloudComp 2009 held in Munich, Germany, in October 2009.

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Extra info for Cloud Computing: First International Conference, CloudComp 2009 Munich, Germany, October 19–21, 2009 Revised Selected Papers

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We have applied several cloud technologies such as Hadoop[7], Dryad and DryadLINQ[8,9], and CGL-MapReduce[lO], to various scientific applications wiz: (i) Cap3[II] data analysis; (ii) High Energy Physics(HEP ) data analysis ; (iv) Kmeans clustering[12]; and, (v) Matrix Multiplication. The streaming based MapReduce [13] runtime - CGL-MapReduce- developed by us extends the MapReduce model to iterative MapReduce domain as well. Our experience suggests that although most "pleasingly parallel" applications can be performed using cloud technologies such as Hadoop, CGL-MapReduce, and Dryad , in a fairly easy manner, scientific applications, which require complex communication patterns, still require more efficient runtime support such as MPI[ 14].

Therefore, all our implementations are written using DryadLINQ although it uses Dryad as the underlying runtime). Overhead = [P * T(P) -T(1)]fT(1). (1) Table 3. 3 - 64 bit All three cloud runtimes work competitively well for the CAP3 application. In the Hadoop implementation of HEP data analysis, we kept the input data in a high performance parallel file system rather than in the HDFS because the analysis scripts written in ROOT could not access data from HDFS . This causes Hadoop 's map tasks to access data remotely resulting lower performance compared to DryadLINQ and CGL-MapReduce implementations, which access input files from local disks.

Listing below shows the VXDL description of this communication-intensive link. Com m u ni c a t l on Ln e e n e Lv e bi < vxdl: latency> O. :l t M a tc h > Let us now illustrate how each VPXI description can be embedded in a physical substrate. We propose two different solutions for both VPXI , which correspond to four different physical allocat ions as represented in figure 4.

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