Download Data Management in Cloud, Grid and P2P Systems: 5th by Ibrahima Gueye, Idrissa Sarr, Hubert Naacke (auth.), PDF

By Ibrahima Gueye, Idrissa Sarr, Hubert Naacke (auth.), Abdelkader Hameurlain, Farookh Khadeer Hussain, Franck Morvan, A Min Tjoa (eds.)

This e-book constitutes the refereed court cases of the fifth overseas convention on information administration in Grid and Peer-to-Peer structures, Globe 2012, held in Vienna, Austria, in September 2012 along with DEXA 2012.
The nine revised complete papers provided have been conscientiously reviewed and chosen from 15 submissions. The papers are prepared in topical sections on facts administration within the cloud, cloud MapReduce and function overview, and information circulate platforms and disbursed information mining.

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Read 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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Additional resources for Data Management in Cloud, Grid and P2P Systems: 5th International Conference, Globe 2012, Vienna, Austria, September 5-6, 2012. Proceedings

Sample text

Google introduced MapReduce [5] as a solution to the need to process datasets up to multiple terabytes in size on a daily basis. An open-source implementation of the MapReduce model proposed by Google, is provided within the Hadoop project [9], whose popularity rapidly increased over the past years. The MapReduce paradigm has recently been adopted by the Cloud computing community as a support to those Cloud-based applications that are data-intensive. Cloud providers support MapReduce computations so as to take advantage of the huge processing and storage capabilities the Cloud holds, but at the same time, to provide the user with a clean and easy-to-use interface.

In this alternative approach, the maximum number of different updates that can happen at the same time in the same column family should be defined for each column family set as the maximum number of versions in the attribute HColumnDescriptor. Yet another alternative is to add the timestamp as suffix to the 30 B. Yu et al. column qualifier. This does not incur any versioning issues. However, it is important to note that different approaches will result in different significance of the timestamp in the key order and carefully programmed temporal logic in the application program will be required.

As the grep application generates an negligible amount of output data, the cost of data transfers out of the Cloud is virtually zero, as shown in Figure 2(a). As a result, the cost of executing grep requests is only given by the cost of the rented virtual machines. 8 GB. Consequently, the data transfer cost has a significant impact on the total cost of the execution. e. 100 VMs in this scenario), the computation cost accounts only for 17% of the total cost. Pipeline MapReduce Applications. To illustrate the characteristics and requirements of pipeline MapReduce applications, we developed a synthetic test which we then executed in a virtualized environment.

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