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Cloud-based MapReduce services process large datasets in the cloud, significantly reducing users' infrastructure requirements. Almost all of these services are cloud-vendor-specific and thus internally designed within their own cloud infrastructures, resulting in two important limitations. First, cloud vendors don't let developers see and evaluate how the MapReduce architecture is managed internally. Second, users can't build their own private cloud-infrastructure-based offerings or use different public cloud infrastructures for deploying MapReduce services. The authors' proposed framework enables the dynamic deployment of a MapReduce service in virtual infrastructures from either public or private cloud providers.
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S. Loughran, J. M. Alcaraz Calero, A. Farrell, J. Kirschnick and J. Guijarro, "Dynamic Cloud Deployment of a MapReduce Architecture," in IEEE Internet Computing, vol. 16, no. , pp. 40-50, 2012.
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