| 研究生: |
胡瀚 Han Hu |
|---|---|
| 論文名稱: |
內容中心網路下基於查詢相似度之搜尋方法 A Search Approach Based on Query Similarity in Content-Centric Networks |
| 指導教授: |
胡誌麟
Chih-Lin Hu |
| 口試委員: | |
| 學位類別: |
碩士 Master |
| 系所名稱: |
資訊電機學院 - 通訊工程學系 Department of Communication Engineering |
| 論文出版年: | 2018 |
| 畢業學年度: | 106 |
| 語文別: | 中文 |
| 論文頁數: | 69 |
| 中文關鍵詞: | 搜尋演算 、資料暫存 、內容傳遞 、內容傳輸網路 、內容中心網路 |
| 外文關鍵詞: | Search Algorithm, Data Caching, Content Forwarding, Content Distribution Network, Content- Centric Network |
| 相關次數: | 點閱:9 下載:0 |
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內容中心網路(Content-Centric Network,CCN),是一種以資料內容為中心的新一代網路傳輸架構。基於此傳輸架構,資料於傳遞的過程中可以暫存於節點的儲存區內;使得該節點在收到資料請求時,可以直接從儲存區中提取,提高整體網路的傳輸效率。在CCN中,節點在發送查詢的請求時,需要透過Forwarding Information Base (FIB);其功能類似IP協定的路由表,會將查詢的請求轉發至資料來源的提供者。此外,在FIB中的記錄為資料來源的提供者,而非節點中所暫存的資料本身。然而,無法事先得知其他節點中所暫存的資料,因此在查詢資料的過程將耗費龐大的計算成本及時間。如果FIB能同時記錄節點中暫存的資料,將降低網路資源耗費的成本,進而提升網路的效能。但如此一來,網路中的節點會因額外記錄每個節點所儲存的資料導致儲存空間需求的增加,也相對地提高了資料搜尋的成本。本論文提出一套在內容中心網路下基於查詢相似度之搜尋方法(SAQS);此方法以節點本身所暫存資料的訊息進行分析,並以陣列的形式呈現;接著計算暫存的資料與查詢的權重,最後選擇計算結果最佳的節點進行轉發,以此提升搜尋的效率。此外,當有多個節點同時匹配轉發條件時,也能以此方法將能決定出最佳的節點。最後模擬驗證本論文提出的SAQS與其他方法之比較;在不同的網路拓樸環境及參數設定下,SAQS有較佳的資料搜尋命中率,且所耗費的Hop數及延遲時間也較少。
The Content-Centric Network (CCN) is a new generation of network transmission architecture which focuses on data content delivery. In a CCN, the content can be cached in the storage area of a node during the data delivery process, and a node can directly extract data from the storage area when receiving the query, thus improving the efficiency of data content distribution in a network. The node needs to forward the query through the Forwarding Information Base (FIB). Its function is similar to the IP protocol routing table, and the query is forwarded to the provider of the content source. In addition, the record in the FIB is the provider of the content source, not the content itself cached in the node. However, the router cannot know the data cached in other routers in advance, so the process of querying data will consume huge computational cost and time. This paper proposes a Search Approach Based on Query Similarity (SAQS). This method analyzes the information cached in the node itself, then calculates the cached content and the weight of query, and decides the best node for forwarding data in order to improve the efficiency of the search. In addition, when multiple nodes match the forwarding conditions at the same time, this method can also decide the best forwarding node. Finally, SAQS is compared with other methods through simulation verification. Under different network topologies and parameter settings, SAQS has better cache hit ratio, smaller number of hops and shorter delay time.
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