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研究生:郭士煒
研究生(外文):Guo, Shihwei
論文名稱:植基於GPGPU高效率的平行運算之封包分類與分析
論文名稱(外文):Classification And Analysis Of The Packet Based On GPGPU Parallel Architecture
指導教授:王孝熙洪哲倫洪哲倫引用關係
指導教授(外文):Wang, HsiaohsiHung, Chelun
口試委員:王孝熙洪哲倫葉介山許慶賢林俊淵
口試委員(外文):Wang, HsiaohsiHung, ChelunYeh, JiehshanHsu, ChinghsienLin, Chunyuan
口試日期:2012-07-17
學位類別:碩士
校院名稱:靜宜大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:50
中文關鍵詞:封包分類封包過濾GPGPUCUDA平行運算
外文關鍵詞:Packet classificationGPGPUParallel processPacket filtering
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  • 下載下載:2
  • 收藏至我的研究室書目清單書目收藏:0
現今許多新的網際網路技術隨著網路技術的蓬勃發展提供了各式各樣的應用與服務,而這些深深的依賴著網際網路環境的服務隨之而來則是令人擔憂的資訊安全問題,因此封包分類與過濾對於網際網路安全是非常重要的議題,然而為了保證使用者的服務品質(QoS)封包分類通常需要依賴一個高效能的過濾系統,針對每一個封包過濾出各種重要的資訊,然而傳統的封包分類需花費大量時間來處理此密集型的任務,尤其對於現今雲端運算環境中的海量資料處理,如果透過過去傳統裝置來分類分析網路封包,不僅容易造成此裝置的超量負載,最壞的情況可能會導致此裝置當機。
近年來隨著圖形處理器(GPGPU)迅速的發展,國內外許多學者已經將GPGPU作為一個新的研究領域,因此本論文基於國內外的文獻的基礎上分析GPU架構的特性進而使之用於提高封包分類速度,以實現龐大且多維度的即時封包分類需求,我們實做了兩種知名的封包分類演算法BPF與RFC-BitMap應用於GPGPU的SIMT高度平行架構來提升效能。另外我們探討了CUDA設備上各種記憶體架構對於分類系統效能之影響以及利用不同的資料傳輸技術改善分類系統效能,因此本論文之目的在於使用GPGPU之高效能計算架構來提升雲端環境之海量封包分類效能。
Recently, many new types of services have been provided through internet. Packet classification is an important role of network security that typically relies on a flexible packet filtering system to extrapolate important packet information from each processed packet. However, packet classification task is computation-intensive. In order to accelerate packet classification to reach real-time large amount and multi-dimensional of internet packet classification requirements, we propose two parallel packet classification methods which are implemented on GPGPU platform. We also evaluate the performance of implementation on various memory architectures of CUDA device. The experiment results demonstrate that the proposed methods can achieve significant speed up over the sequential packet classification algorithms on single CPU.
第1章 緒論 1
1.1 研究背景 2
1.2 研究動機 5
1.3 研究目的 7
1.4 論文架構 8
第2章 相關文獻探討 9
2.1 封包擷取技術探討 10
2.2 封包擷取與過濾工具之探討 10
2.3 封包分類演算法 12
2.4 圖形處理器GPU的架構與平行運算技術 17
2.5 GPGPU近年來應用於封包分類過濾之探討 20
第3章 GPGPU計算架構 21
3.1 CUDA PROGRAMMING MODEL 23
3.2 GPGPU的記憶體架構 25
第4章 研究方法 30
4.1 GPGPU封包平行分類 31
第5章 實驗結果 34
5.1 BPF記憶體之間的效能差異 35
5.2 BitMap-RFC記憶體之間的效能差異 37
5.3 GPGPU整體效能評估 37
第6章 結論 41
參考文獻 47
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