Moving MapReduce into the Cloud : Elasticity, Efficiency and Scalability
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Moving MapReduce into the cloud and leveraging the elastic resource allocation of cloud computing offer an opportunity for efficiency and affordable big data analytics. However, due to the virtualization overhead, the architectural bottlenecks of MapReduce implementation, and the semantic gap between the MapReduce runtime and the resource manager of the cloud platform makes building efficient and scalable virtual MapReduce clusters a very challenging task. This thesis presents adaptive resource management approaches for virtual MapReduce clusters. To this end, we proposed and designed a synergistic ...