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基于深度布隆過濾器的NDN網(wǎng)絡三級名字查找方法

吳慶濤 師君如 張明川 王倩玉 朱軍龍 張宏科

吳慶濤, 師君如, 張明川, 王倩玉, 朱軍龍, 張宏科. 基于深度布隆過濾器的NDN網(wǎng)絡三級名字查找方法[J]. 電子與信息學報, 2021, 43(12): 3597-3604. doi: 10.11999/JEIT200766
引用本文: 吳慶濤, 師君如, 張明川, 王倩玉, 朱軍龍, 張宏科. 基于深度布隆過濾器的NDN網(wǎng)絡三級名字查找方法[J]. 電子與信息學報, 2021, 43(12): 3597-3604. doi: 10.11999/JEIT200766
Qingtao WU, Junru SHI, Mingchuan ZHANG, Qianyu WANG, Junlong ZHU, Hongke ZHANG. A Three-level Name Lookup Method Based on Deep Bloom Filter for Named Data Networking[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3597-3604. doi: 10.11999/JEIT200766
Citation: Qingtao WU, Junru SHI, Mingchuan ZHANG, Qianyu WANG, Junlong ZHU, Hongke ZHANG. A Three-level Name Lookup Method Based on Deep Bloom Filter for Named Data Networking[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3597-3604. doi: 10.11999/JEIT200766

基于深度布隆過濾器的NDN網(wǎng)絡三級名字查找方法

doi: 10.11999/JEIT200766
基金項目: 國家自然科學基金(61871430, 61976243),中原科技創(chuàng)新領軍人才(214200510012),河南省教育廳基礎研究專項(19zx010),河南省教育廳重點科研項目(20A520011)
詳細信息
    作者簡介:

    吳慶濤:男,1975年生,教授,研究方向為云計算、物聯(lián)網(wǎng)、下一代網(wǎng)絡

    師君如:女,1997年生,碩士生,研究方向為信息中心網(wǎng)絡

    張明川:男,1977年生,教授,研究方向為物聯(lián)網(wǎng)、下一代網(wǎng)絡、機器學習

    王倩玉:女,1991年生,碩士生,研究方向為信息中心網(wǎng)絡

    朱軍龍:男,1982年生,副教授,研究方向為人工智能、機器學習、新型網(wǎng)絡

    張宏科:男,1957年生,教授,研究方向為下一代網(wǎng)絡、智慧協(xié)同網(wǎng)絡

    通訊作者:

    張明川 zhang_mch@haust.edu.cn

  • 中圖分類號: TN919.2; TP393

A Three-level Name Lookup Method Based on Deep Bloom Filter for Named Data Networking

Funds: The National Natural Science Foundation of China (61871430, 61976243), The Leading Talents of Science and Technology in the Central Plain of China (214200510012), The Basic Research Projects in the University of Henan Province (19zx010), The Key Project of the Education Department Henan Province (20A520011)
  • 摘要: 為提高命名數(shù)據(jù)網(wǎng)絡(Name Data Networking, NDN)路由過程中內(nèi)容名字查找的效率,該文提出一種基于深度布隆過濾器的3級名字查找方法。該方法使用長短記憶神經(jīng)網(wǎng)絡(Long Short Term Memory, LSTM)與標準布隆過濾器相結合的方法優(yōu)化名字查找過程;采用3級結構優(yōu)化內(nèi)容名字在內(nèi)容存儲器(Content Store, CS)、待定請求表(Pending Interest Table, PIT)中的精確查找過程,提高查找精度并降低內(nèi)存消耗。從理論上分析了3級名字查找方法的假陽性率,并通過實驗驗證了該方法能夠有效節(jié)省內(nèi)存、降低查找過程的假陽性。
  • 圖  1  深度布隆過濾器查找結構

    圖  2  學習模型結構

    圖  3  名字字符串長度分布

    圖  4  初始過濾器系數(shù)選擇對假陽性率的影響

    圖  5  GRU大小對假陰性率和假陽性率的影響

    圖  6  閾值τ大小對假陽性率和假陰性率的影響

    圖  7  假陽性率

    圖  8  內(nèi)存消耗

    表  1  面向深度布隆過濾器的名字查找算法

     輸入:內(nèi)容集合S,非內(nèi)容集合U,閾值$\tau $
     輸出:內(nèi)容名字x
     1: 調用LSTM架構使用集合S和集合U獲得一個集合D
     2: 查找x在初始過濾器中進行精確匹配;
     3: while 對每一個$x \in S$ do
     4: If b[i]=1 then
     5:   將匹配內(nèi)容x發(fā)送到深度學習模型中;
     6:  else
     7:  將未匹配的x發(fā)送到FIB表中進行最長前綴匹配查找;
     8: end while
     9: if $(x, y) \in D $ then//在第2級深度學習模型中進行精確匹配查找
     10: 計算$f(x)=\dfrac{1}{1+{ \rm{e} }^{-x} }$;
     11: end if
     12: if $x \in S $且$ f(x) < \tau $ then
     13: 將查找獲得的內(nèi)容名x在路由表中對應的數(shù)據(jù)包進行轉發(fā);
     14: else if $x \in S $且$f(x) < \tau $ then
     15: 將未匹配的內(nèi)容名字x發(fā)送到第3級備份過濾器進行查找;
     16: while在備份布隆過濾器查找b[i]=1 do
     17:  將查找獲得的內(nèi)容名x在路由表中對應的數(shù)據(jù)包進行轉發(fā);
     18: end while
     19: else
     20: 將第3級未匹配的內(nèi)容發(fā)送到FIB表中進行最長前綴匹配查找;
     21: end if
    下載: 導出CSV

    表  2  服務器配置

    主要模塊具體配置
    主板LENOVO-LNVNB161216
    CPUIntel Core? i7-9750H (6核,主頻2.60 GHz)
    內(nèi)存DDR4 8GB (內(nèi)存頻率 2667 MHz)
    下載: 導出CSV

    表  3  GRU和隱藏層參數(shù)配置與編號

    配置編號參數(shù)配置配置編號參數(shù)配置配置編號參數(shù)配置
    I型GRU大小=32,隱藏層大小=8IV型GRU大小=16,隱藏層大小=8VII型GRU大小=8,隱藏層大小=4
    II型GRU大小=32,隱藏層大小=4V型GRU大小=16,隱藏層大小=4VIII型GRU大小=4,隱藏層大小=4
    III型GRU大小=16,隱藏層大小=16VI型GRU大小=8,隱藏層大小=8VIIII型GRU大小=4,隱藏層大小=8
    下載: 導出CSV
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  • 收稿日期:  2020-08-27
  • 修回日期:  2021-09-24
  • 網(wǎng)絡出版日期:  2021-10-22
  • 刊出日期:  2021-12-21

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