By Ibrahima Gueye, Idrissa Sarr, Hubert Naacke (auth.), Abdelkader Hameurlain, Farookh Khadeer Hussain, Franck Morvan, A Min Tjoa (eds.)
This publication constitutes the refereed court cases of the fifth foreign convention on info administration in Grid and Peer-to-Peer platforms, Globe 2012, held in Vienna, Austria, in September 2012 along side DEXA 2012.
The nine revised complete papers awarded have been rigorously reviewed and chosen from 15 submissions. The papers are geared up in topical sections on facts administration within the cloud, cloud MapReduce and function review, and knowledge circulate structures and allotted facts mining.
Read Online or Download Data Management in Cloud, Grid and P2P Systems: 5th International Conference, Globe 2012, Vienna, Austria, September 5-6, 2012. Proceedings PDF
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Extra info for Data Management in Cloud, Grid and P2P Systems: 5th International Conference, Globe 2012, Vienna, Austria, September 5-6, 2012. Proceedings
2 MapReduce Applications: Case Studies In general, the MapReduce model is appropriate for a large class of data-intensive applications which need to perform computations on large amounts of data. This type of applications can be expressed as computations that are executed in parallel on a large number of nodes. ) can be MapReduce Applications in the Cloud 39 modeled using the MapReduce paradigm. In this paper, we take as study case three typical MapReduce applications: Distributed Grep. The grep application searches through a huge text given as input file, for a specific pattern and outputs the occurrences of that pattern.
To evaluate these factors, we run 3 MapReduce applications with the Hadoop framework in two environments: first on clusters belonging to the Grid’5000  grid platform, then in a Nimbus  Cloud deployed on the Grid’5000 testbed. We then consider the payment scheme used by Amazon for the rental of their Cloud resources and we compute the cost of using our Nimbus deployment for running MapReduce applications. Our goal is to assess and understand the overhead generated by the execution of a MapReduce computation in a Cloud.
The parallel version was a MapReduce version, utilizing as many mappers as possible to improve the insertion rate. The sequential version created one mapper only, whose behavior is described by the pseudo-code listed in Fig. 1. Fig. 2 instead lists the pseudo-code describing the behavior of the parallel version with multiple-mappers. Fig. 3. Experimental results Fig. 3 shows the effective growing rates of data population as the CCDO object database rapidly grows over time. , the parallel version of the Bigtable approach), and the MySQL-based approach in terms of amount of time (in seconds) necessary to one full CCDO insertion constituted by 30,019 PUT statements for the Bigtable approach (in contrast to 3,004 INSERT statements for the MySQL approach) are compared.
Data Management in Cloud, Grid and P2P Systems: 5th International Conference, Globe 2012, Vienna, Austria, September 5-6, 2012. Proceedings by Ibrahima Gueye, Idrissa Sarr, Hubert Naacke (auth.), Abdelkader Hameurlain, Farookh Khadeer Hussain, Franck Morvan, A Min Tjoa (eds.)