NeTS: Small: Sketching Big Network Data

NeTS：小：勾画大网络数据

• 批准号: 1719222
• 负责人: Shigang Chen
• 金额: $ 50万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1719222&HistoricalAwards=false
• 关键词: NeTS; Small; Sketching; Big; Network

Sketches are compact data structures maintained in network switches that store the summary of network data flows and permit answering queries with estimated results. This project aims to develop new virtual sketching methods that reduce big network data to measurement summaries orders-of-magnitude smaller than what the traditional sketches can do. The new methods hold the promise of allowing routers to perform measurement on large network traffic at line speed, allowing enterprise systems to keep their network data records for much longer time, and allowing users with ordinary computing resources to work on big network data.A major research thrust in sketches is to reduce their memory footprint. Virtual sketches permit different flows to share a common pool of memory.Space sharing can drastically reduce the memory requirement, but this sharing introduces noise. Fortunately, we find that for randomized sharing schemes, the noise can be statistically measured and removed. This project will investigate (1) a theoretical study on new methods of space sharing, (2) common procedures for highly efficient online operations on virtual sketches with low overhead, (3) spatial virtual sketches for joint measurement of network data at different routers, (4) temporal virtual sketches for joint measurement of network data across different time periods, and (5) virtual composite sketches for sophisticated network data measurement.

草图是在网络交换机中维护的紧凑数据结构，它存储网络数据流的摘要，并允许使用估计结果回答查询。该项目旨在开发新的虚拟素描方法，将大数据减少到比传统素描所能做到的数量级更小的测量摘要。新方法有望允许路由器以线速对大量网络流量进行测量，允许企业系统将其网络数据记录保留更长的时间，并允许拥有普通计算资源的用户处理大型网络数据。草图中的一个主要研究重点是减少他们的内存占用。虚拟草图允许不同的数据流共享一个公共内存池。空间共享可以显著降低内存需求，但这种共享会带来噪音。幸运的是，我们发现对于随机共享方案，噪声可以被统计地测量和去除。该项目将研究(1)新的空间共享方法的理论研究，(2)低开销虚拟草图的高效在线操作的通用程序，(3)不同路由器上的网络数据联合测量的空间虚拟草图，(4)跨不同时间段的网络数据联合测量的时间虚拟草图，以及(5)复杂网络数据测量的虚拟合成草图。

项目成果

Missing-Tag Detection With Unknown Tags

未知标签的缺失标签检测

• DOI: 10.1109/tnet.2020.2984706
• 发表时间: 2020
• 期刊: IEEE/ACM Transactions on Networking
• 作者: Zhang, Youlin;Chen, Shigang;Zhou, You;Fang, Yuguang
• 通讯作者: Fang, Yuguang

Spread Estimation With Non-Duplicate Sampling in High-Speed Networks

高速网络中非重复采样的扩展估计

• DOI: 10.1109/tnet.2021.3078725
• 发表时间: 2021-05
• 期刊: IEEE/ACM Transactions on Networking
• 作者: He Huang;Yu-E Sun;Chaoyi Ma;Shigang Chen;Yang Du;Haibo Wang;Qingjun Xiao
• 通讯作者: Qingjun Xiao

HeavyKeeper: An Accurate Algorithm for Finding Top-k Elephant Flows

• DOI: 10.1109/tnet.2019.2933868
• 发表时间: 2019-10-01
• 期刊: IEEE-ACM TRANSACTIONS ON NETWORKING
• 影响因子: 3.7
• 作者: Yang, Tong;Zhang, Haowei;Li, Xiaoming
• 通讯作者: Li, Xiaoming

Estimating Cardinality for Arbitrarily Large Data Stream With Improved Memory Efficiency

提高内存效率的任意大数据流的基数估计

• DOI: 10.1109/tnet.2020.2970860
• 发表时间: 2020-03
• 期刊: IEEE/ACM Transactions on Networking
• 作者: Xiao Qingjun;Chen Shigang;Zhou You;Luo Junzhou
• 通讯作者: Luo Junzhou

Achieving High Scalability Through Hybrid Switching in Software-Defined Networking

通过软件定义网络中的混合交换实现高可扩展性

• DOI: 10.1109/tnet.2018.2789339
• 发表时间: 2018-02
• 期刊: IEEE/ACM Transactions on Networking
• 作者: Hongli Xu;He Huang;Shigang Chen;Gongming Zhao;Liusheng Huang
• 通讯作者: Liusheng Huang