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研究生:葉璿霆
研究生(外文):Ruei-Ting Yeh
論文名稱:叢集演算法在疊代網路之運用
論文名稱(外文):The Application of Clustering Algorithm in Overlay Network
指導教授:鍾添曜
學位類別:碩士
校院名稱:元智大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:50
中文關鍵詞:點對點網路架構配對叢集K-mean座標重心
外文關鍵詞:Keywords:Peer-to-peermatchingclusteringK-meancoordinategravity
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  • 被引用被引用:0
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結構性的點對點疊代網路(P2P overlay network),提供一個可伸縮性且分散式多元應用的新平臺,諸如檔案的分享(file sharing)和多媒體的串流(multi-media streaming)。然而疊代網路是邏輯實體網路的呈現,在資料搜尋與索引未能考慮到各個節點的實體位置,因此我們利用網路鄰近位置的資訊來對節點分群,以改善整個搜尋效能和路由繞送的效能。本論文探討四個叢集演算法之效能,其中以K-mean為基礎的重疊網路之切割網路演算法分別為K-mean、K-mean with weight 和K-mean with Exchange;另一個以Matching為主的演算法CMA(Cluster Matching Algorithm)。這些演算法使用GBT(Gravity Based Topology),以重心(Gravity)和節點能力值(Power Index)來進行叢集。此拓墣結構之穩定度對整個應用的效能有巨大的影響並以此達到一個穩定且節點平均分佈的切割網路。模擬結果顯示,CMA演算法與K-mean為基礎之相關演算法比較以叢集控制節點為中心的叢集演算法表現更為優越。
Structural P2P overlay network offers a new application platform for elasticity and distributed applications,such as file sharing and multimedia streaming. An overlay network is a logical network, whose topology changes frequently when peer nodes join and leave dynamically and thus make it hard to maintain the topology. Furthermore, if we don’t consider the physical position of each node on data search and index, the searching performance will become poor. Thus in this thesis, we use the network proximity clustering to improve the search and routing effect. This thesis compares the performance of 4 algorithms,among K-mean、K-mean with weight and K-mean with Exchange based on K-mean algorithm;the other is CMA(Cluster Matching Algorithm)based on Matching Algorithm. We use the gravity and computing power to cluster peer nodes so that suitable and compact clusters can be achieved and average in this topology. According to the result of simulation, CMA and K-mean related algorithms is superior to the algorithms based on cluster headers.
中文摘要 I
英文摘要 II
圖目錄 V
表目錄 VI
1. 導論 7
1.1. 研究動機 8
1.2. 研究目的 9
1.3. 研究貢獻 9
1.4. 章節概要 9
2. 文獻探討 10
2.1. 定位 10
2.1.1. 全球網路定位 10
2.1.2. 階層式定位服務 11
2.1.3. Vivaldi’s 定位 13
2.2. 叢集 14
2.2.1. CBT (Cluster Based Topology) 14
2.2.2. Distributed Binning 15
2.3. Clustering 分類概述 16
3. Clustering架構與演算法 20
3.1. GBC 系統架構 20
3.2. GBC叢集形成與維護演算法 21
3.2.1. GBC叢集重心的計算 22
3.2.2. GBC節點網路能力的計算 23
3.3. GBC Clustering 之方法 23
3.3.1. K-mean Clustering演算法 24
3.3.2. K-mean Clustering with Weight演算法 27
3.3.3. CMA(Clustering Matching Algorithm)演算法 28
3.3.4. K-mean with Exchange演算法 32
3.3.5. K-Large Power with Threshold演算法 33
4. 系統模擬與分析 35
4.1. 模擬環境設計與參數設定 35
A. Count值 37
B. 加權平均距離 37
C. 節點連結之延展性 38
4.2. 效能比較 38
4.2.1. 各演算法效能評估 41
4.2.2. 各演算法實際距離之效能評估 47
4.2.3. 各演算法之Ratio值比較 48
5. 結論與未來展望 51
5.1. 結論 51
5.2. 未來展望 53
參考文獻 55
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