Issue No. 06 - Nov.-Dec. (2012 vol. 16)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MIC.2011.163
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.
service-oriented architecture, business data processing, cloud computing, private cloud providers, dynamic cloud deployment, MapReduce architecture, cloud-based MapReduce services process large datasets, users infrastructure requirements, cloud-vendor-specific services, private cloud-infrastructure-based offerings, public cloud infrastructures, MapReduce services, virtual infrastructures, Cloud computing, Computer architecture, Data processing, Computational modeling, Programming, Data models, configuration management, automated deployment, MapReduce, cloud computing, infrastructure as a service
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.