Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud

By Daniel Warneke, Odej Kao. IEEE Transactions on Parallel and Distributed Systems, 2011

339 citations recorded by the source corpus.

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Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey (2014, background): “Warneke and Kao (2011) have discussed the challenges and opportunities for efficient parallel data processing in cloud environments and presented Nephele, the first data processing framework to exploit the dynamic resource provisioning offered by today IaaS clouds.”
Resource Management in Clouds: Survey and Research Challenges (2015, background): “Further, a general mechanism could be developed through which a Cloud Provider can pass information about current and expected resource availability to a Cloud User, which then uses the information to optimize its application, as has been demonstrated for MapReduce implementations [19, 103, 184, 235”
The Stratosphere platform for big data analytics (2014, methodology): “…with good performance [25,26], an extensible query language and underlying operator model [37], techniques to infer cloud topologies and detect bottlenecks in distributed execution [9–11], as well as techniques to exploit dynamic resource allocation [68] and evaluate compression schemes [40].”