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Xiaoning Ding
Researcher at New Jersey Institute of Technology
Publications - 68
Citations - 2397
Xiaoning Ding is an academic researcher from New Jersey Institute of Technology. The author has contributed to research in topics: Cloud computing & Cache. The author has an hindex of 23, co-authored 64 publications receiving 2268 citations. Previous affiliations of Xiaoning Ding include University Heights, Newark & College of William & Mary.
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Proceedings ArticleDOI
Measurements, analysis, and modeling of BitTorrent-like systems
TL;DR: An analysis of representative Bit-Torrent traffic provides several new findings regarding the limitations of BitTorrent systems: due to the exponentially decreasing peer arrival rate in reality, service availability in such systems becomes poor quickly, after which it is difficult for the file to be located and downloaded.
Proceedings ArticleDOI
Gaining insights into multicore cache partitioning: Bridging the gap between simulation and real systems
TL;DR: This paper has comprehensively evaluated several representative cache partitioning schemes with different optimization objectives, including performance, fairness, and quality of service (QoS) and provides new insights into dynamic behaviors and interaction effects.
Proceedings ArticleDOI
DULO: an effective buffer cache management scheme to exploit both temporal and spatial locality
TL;DR: Leveraging the filtering effect of the buffer cache, DULO can influence the I/O request stream by making the requests passed to disk more sequential, significantly increasing the effectiveness ofI/O scheduling and prefetching for disk performance improvements.
Journal ArticleDOI
A performance study of BitTorrent-like peer-to-peer systems
TL;DR: A performance study of BitTorrent-like P2P systems by modeling, based on extensive measurements and trace analysis, demonstrates that inter-torrent collaboration is much more effective than stimulating seeds to serve longer for addressing the service unavailability in BitTorrent systems.
Proceedings Article
DiskSeen: exploiting disk layout and access history to enhance I/O prefetch
TL;DR: The implementation of the DiskSeen scheme in the Linux 2.6 kernel shows that it can significantly improve the effectiveness of prefetching, reducing execution times by 20%-53% for micro-benchmarks and real applications such as grep, CVS, and TPC-H.