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基于WinPcap的远程网络微型数据堆叠式采集仿真 被引量:2

Remote Network Micro Data Stacking Acquisition Simulation Based on WinPcap
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摘要 采用当前方法采集远程网络中存在的数据时,数据在远程网络中的信噪比较低,采集数据的时间开销较高,存在数据重构精度低和数据采集效率低的问题。将WinPcap运行机制应用到数据堆叠式采集过程中,提出基于WinPcap的远程网络微型数据堆叠式采集方法,对本地网络监控端中存在的数据进行预处理,在压缩感知的基础上通过WinPcap运行机制对本地数据进行随机采样,并对随机采集到的数据进行压缩处理,将压缩处理后的数据传送到远程网络的处理端中,根据节间点存在的社会关系估计没有传送到处理端的数据,通过压缩感知算法重构数据,实现远程网络微型数据的堆叠式采集。仿真结果表明,所提方法的数据重构精度高、数据采集效率高。 When the current method is used to collect data existing in the remote network,the signal-to-noise ra-tio of the data in the remote network is relatively low,the time cost of collecting data is high,and both the data recon-struction accuracy and the data collection efficiency are quite low.Applying the WinPcap operation mechanism to the data stacking acquisition process,a WinPcap-based remote network micro data stacking acquisition method is pro-posed to preprocess the data existing in the local network monitoring terminal,and the WinPcap operating mechanism is based on the compressed sensing.The local data were randomly sampled,and the randomly collected data were compressed,and the compressed data were transmitted to the processing end of the remote network,and the data that were not transmitted to the processing end were estimated according to the social relationship existing in the inter-node,and compressed.The perceptual algorithm was used to reconstruct the data to realize the stacked collection of remote network micro data.The simulation results show that the proposed method has high data reconstruction accura-cy and high data collection efficiency.
作者 白雪松 BAI Xue-song(Northeastern University at Qinhuangdao,Qinhuangdao Hebei 066004,China)
出处 《计算机仿真》 北大核心 2020年第1期333-337,共5页 Computer Simulation
关键词 运行机制 远程网络 数据堆叠式采集 Operating mechanism Remote network Data stacking
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  • 1刘宏,陈慧玲,庞胜利.光伏并网电站数据采集监测系统[J].可再生能源,2006,24(5):74-76. 被引量:9
  • 2徐中华,刘万华,余成江.清华山维一体化软件EPS脚本语言的应用[J].城市勘测,2007(6):88-90. 被引量:29
  • 3Ptulp I R. Software failures and the road to a petaflop machine[ C ]// Proceedings of the 11 th International Symposium on High Performance Computer Architecture, San Francisco, CA, USA, IEEE Computer Society, 2005.
  • 4Liang Y, Zhang Y, Xiong H, et al. Failure prediction in IBM BlueGene/L event logs [ C ]//Proceedings of Seventh IEEE International Conference on Data Mining Omaha, Nebraska, USA, IEEE Computer Society, 2007:583 - 588 .
  • 5LanZ L, Gu J X, Zheng Z M, et al. A study of dynamic meta-learning for failure prediction in large-scale systems[J]. Journal of Parallel and Distributed Computing, 2010, 70 (6) : 630 - 643.
  • 6Oliner A, Ganapathi A, Xu W. Advances and challenges in log analysis [ J ]. Communications of the ACM , 2012, 55(2): 55 -61.
  • 7Xu W, Huang L, Fox A, et al. Detecting large-scale system problems by mining console logs [ C ]//Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles,New York, NY, USA: ACM, 2009.
  • 8Gainaru A, Cappello F, Snir M, et al. Fault prediction under the microscope: a closer look into HPC systems [ C ]//Proceedings of the International Conference High Performance Computing, Networking, Storage and Analysis, Los Alamitos, CA, USA, IEEE Computer Society Press, 2012.
  • 9Scott S L, Engelmann C, Vallre G R, et al. A tunable holistic resiliency approach for high-performance computing systems [ C ]//Proceedings of the 14th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming,New York, NY, USA,ACM, 2009.
  • 10Nagarajan A B, Mueller F, Engelmann C, et al. Proactive fault tolerance for HPC with Xen virtualization [ C ]// Proceedings of the 21st Annual International Conference on Supercomputing,New York, NY, USA, ACM, 2007: 23- 32.

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