Distinguished ACM Speaker:
Based in China
Yu Hua is a professor in Huazhong University of Science and Technology. He was Postdoc Research Associate in McGill University and Postdoc Research Fellow in University of Nebraska-Lincoln in 2009-2011. He was Research Assistant in The Hong Kong Polytechnic University in 2006. His research interests include large-scale cloud storage systems, in-memory computing, NVM devices, metadata management, data deduplication and data analytics. He publishes more than 100 papers in major journals and conferences, including ACM Transactions on Architecture and Code Optimization (TACO), IEEE Transactions on Computers (TC), IEEE Transactions on Parallel and Distributed Systems (TPDS), Proceedings of the IEEE (PIEEE), IEEE Transactions on Industrial Informatics (TII), USENIX FAST, USENIX ATC, SC, INFOCOM, HPDC, ICDCS, DATE and MSST. He serves for multiple international conferences and journals, such as ASPLOS, USENIX ATC, EuroSys, RTSS, DAC, IPDPS, MSST, INFOCOM, ICNP, ICDCS, etc, and Associate Editor in Journal of Communications and Networks (JCN) and Frontiers of Computer Science (FCS). He is the senior member of ACM, IEEE and CCF, and a member of USENIX.
ACM Senior Member, 2016
ACM Member Since: September 2012
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- Deduplication-aware Architecture and System for Edge Computing: Edge computing requires cost-efficient storage infrastructure to deliver high performance. Deduplication-based schemes are able to meet these needs due to salient features of space and bandwidth efficiency, as well as high throughput. In this talk, I...
- Deduplication-aware Ecosystem: A Bottom-up Approach: In the era of big data, the rapid growth in data volume and complexity requires highly efficient schemes to reduce the amounts of data. Deduplication schemes can remove the redundant data, which is helpful to obtain space savings and improve network ...
- Smarter Storage for Big Data Analytics: Architecture and Systems : In the era of big data, the explosive growth in data volume and complexity requires highly efficient searchable data analytics. Existing cloud storage systems have largely failed to offer an adequate capability for real-time analytics for big data. I...
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