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Big Data Storage and Processing on Azure Clouds: Experiments at Scale and Lessons Learned.

, , , and . Cloud Computing for Data-Intensive Applications, Springer, (2014)

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Gestion du Big Data hautes performances dans les centres de données cloud. (High-Performance Big Data Management Across Cloud Data Centers).. École normale supérieure de Rennes, France, (2014)Dimensionality Reduction for Low-Latency High-Throughput Fraud Detection on Datastreams., , , , , and . ICMLA, page 1170-1177. IEEE, (2019)Adaptive file management for scientific workflows on the Azure cloud., , , , and . IEEE BigData, page 273-281. IEEE Computer Society, (2013)DataSteward: Using Dedicated Compute Nodes for Scalable Data Management on Public Clouds., , and . TrustCom/ISPA/IUCC, page 1057-1064. IEEE Computer Society, (2013)Big Data Storage and Processing on Azure Clouds: Experiments at Scale and Lessons Learned., , , and . Cloud Computing for Data-Intensive Applications, Springer, (2014)When FPGAs are better at floating-point than microprocessors., , , and . FPGA, page 260. ACM, (2008)Exploiting Crosstalk Effects in FPGAs for Generating True Random Numbers., , , and . ICETE (Selected Papers), volume 130 of Communications in Computer and Information Science, page 159-173. Springer, (2009)TomusBlobs: scalable data-intensive processing on Azure clouds., , , and . Concurr. Comput. Pract. Exp., 28 (4): 950-976 (2016)OverFlow: Multi-Site Aware Big Data Management for Scientific Workflows on Clouds., , and . IEEE Trans. Cloud Comput., 4 (1): 76-89 (2016)Towards a unified storage and ingestion architecture for stream processing., , , , , , and . IEEE BigData, page 2402-2407. IEEE Computer Society, (2017)