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Sparse structures for searching and broadcasting algorithms over internet graphs and peer-to-peer computing systems

機(jī)譯：用于通過(guò)Internet圖和對(duì)等計(jì)算系統(tǒng)搜索和廣播算法的稀疏結(jié)構(gòu)

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In a broadcasting problem, a message is sent from a source to all the other nodes in the network. Blind flooding is a classical mechanism for broadcasting, where each node retransmits received message to all its neighbors. Despite its important advantages, an increase in the number of requests or the network size or density produces communication overheads that limit the scalability of blind flooding, especially in networks with dynamic topologies. Theoretically optimal solution is based on minimal spanning trees (MST), but its construction is expensive. We discuss here protocols based on local knowledge and newly proposed sparse structures. In weighted RNG (Relative Neighborhood Graph), messages are forwarded only on links which are not the ‘longest’ in any triangle. In weighted RNGQ, messages are forwarded to links which are not the longest in any triangle or quadrangle. Each node constructs weighted MST based on its 2-hop knowledge. Weighted LMST (Localized LMST) preserves only edges that are selected by both endpoints, and messages are forwarded on these links. Any available metric, such as delay, reliability, reputation etc. can be used as weight. Analysis and simulation show that weighted RNG performs better than existing Flooding and Rumor Mongering (or Gossip) schemes. The parameterless weighted LMST, MST, RNG and RNGQ (RNG over Quadrangle) based broadcasting schemes are compared, showing overall advantage for LMST. We also hint that a number of existing protocols can be simplified by applying concept from this article.
機(jī)譯：在廣播問(wèn)題中，消息從源發(fā)送到網(wǎng)絡(luò)中的所有其他節(jié)點(diǎn)。盲洪是廣播的一種經(jīng)典機(jī)制，其中每個(gè)節(jié)點(diǎn)將接收到的消息重新發(fā)送到其所有鄰居。盡管具有重要優(yōu)勢(shì)，但請(qǐng)求數(shù)量或網(wǎng)絡(luò)大小或密度的增加都會(huì)產(chǎn)生通信開(kāi)銷(xiāo)，從而限制盲目泛洪的可伸縮性，尤其是在具有動(dòng)態(tài)拓?fù)涞木W(wǎng)絡(luò)中。從理論上講，最佳解決方案基于最小生成樹(shù)（MST），但其構(gòu)建成本很高。我們?cè)谶@里討論基于本地知識(shí)和新提出的稀疏結(jié)構(gòu)的協(xié)議。在加權(quán)RNG（相對(duì)鄰域圖）中，僅在不是三角形中“最長(zhǎng)”的鏈接上轉(zhuǎn)發(fā)郵件。在加權(quán)RNGQ中，消息被轉(zhuǎn)發(fā)到不是任何三角形或四邊形最長(zhǎng)的鏈接。每個(gè)節(jié)點(diǎn)都基于其2跳知識(shí)構(gòu)造加權(quán)MST。加權(quán)LMST（本地化LMST）僅保留兩個(gè)端點(diǎn)選擇的邊緣，并且消息在這些鏈接上轉(zhuǎn)發(fā)。任何可用的度量標(biāo)準(zhǔn)（例如延遲，可靠性，信譽(yù)度等）都可以用作權(quán)重。分析和仿真表明，加權(quán)RNG的性能要優(yōu)于現(xiàn)有的Flooding and Rumor Mongering（或八卦）方案。比較了無(wú)參數(shù)加權(quán)LMST，MST，RNG和RNGQ（四邊形上的RNG）廣播方案，顯示了LMST的總體優(yōu)勢(shì)。我們還暗示可以通過(guò)應(yīng)用本文中的概念來(lái)簡(jiǎn)化許多現(xiàn)有協(xié)議。

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