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In this paper, we investigate how MapReduce and Cloud computing can accelerate performance of applications and scale up the computing resources through a real data mining use case in the Biomedical Sciences. We have prototyped the data mining task using the MapReduce model and evaluated it in the Cloud. A performance evaluation model has been built for assessing the eff ciency of the prototype. The...
MapReduce is emerging as an important programming model for data-intensive application. Adapting this model to desktop grid would allow taking advantage of the vast amount of computing power and distributed storage to execute new range of application able to process enormous amount of data. In 2010, we have presented the first implementation of MapReduce dedicated to Internet Desktop Grid based on...
As Cloud computing provides Anything as a Service (XaaS), many applications can be developed and run on the Cloud without concerns of platforms. Data-incentive applications are also easily developed on virtual machines provided by the Cloud. In this work, we investigate cost-effective resource provisioning for MapReduce applications with deadline constraints, as the MapReduce programming model is...
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