本文選題：對等網(wǎng)絡　切入點：流量優(yōu)化　出處：《湖北工業(yè)大學》2014年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)的發(fā)展，，對等網(wǎng)絡技術（Peer-to-Peer,P2P）在文件下載、流媒體、即時消息以及VoIP等領域得到了廣泛應用。P2P應用在不斷豐富網(wǎng)絡應用的同時，也消耗網(wǎng)絡50%～90%的網(wǎng)絡帶寬，對Internet服務提供商（Internet ServiceProvider, ISP）的沖擊很大，造成了網(wǎng)絡擁塞，信息安全等問題。因此，優(yōu)化P2P的流量成為解決P2P內(nèi)容提供商（Peer-to-Peer Content Provider, PCP）和ISP之間緊張關系的重要途徑。 P2P流量優(yōu)化本質(zhì)是減少域間流量傳輸，主要思想是流量本地化，核心是促進P2P與ISP之間的合作。本文圍繞基于三方博弈的P2P流量優(yōu)化框架，對基于緩存博弈的P2P流量優(yōu)化模型和基于合作博弈的P2P流量優(yōu)化模型進行了深入研究，主要工作如下： （1）基于三方博弈的P2P流量優(yōu)化框架。在對從基于PCP的逆向工程傳輸優(yōu)化技術到基于ISP主導的P2P流量管理技術的分析基礎上，本文認為ISP、PCP和P2P三方博弈管理是P2P流量優(yōu)化管理的趨勢，并提出了基于三方博弈的P2P流量優(yōu)化框架，并細化成兩個具體的P2P流量優(yōu)化模型。 （2）基于緩存博弈的P2P流量優(yōu)化模型。在P2P網(wǎng)絡中，邏輯網(wǎng)絡拓撲和實際的網(wǎng)絡拓撲的不匹配是導致大量域間P2P流量產(chǎn)生的重要原因，基于部署緩存的流量本地化思想能很好的解決這一問題。在對緩存部署機制分析的基礎上，提出了包括Tracker服務器、ISP服務器、Cache服務器和P2P用戶節(jié)點的緩存博弈模型，將流量優(yōu)化歸結(jié)為以緩存總收益作為優(yōu)化目標的優(yōu)化問題，提出了一種基于分段緩存思想的緩存策略。仿真實驗表明，緩存博弈能明顯地減少域間流量傳輸，并使域間傳輸成本過高的ISP獲得正收益。 （3）基于合作博弈的P2P流量負載均衡模型。在對基于博弈論的P2P流量優(yōu)化研究進行歸納總結(jié)的基礎上，從流量負載均衡著手對P2P流量進行優(yōu)化，提出了基于合作博弈的P2P流量負載均衡模型，并推導求得模型的均衡解，從演化迭代角度對均衡的動態(tài)收斂性進行了討論，然后提出了流量負載均衡算法（TLBA）并從公平性角度對算法進行了分析。最后的仿真實驗表明，基于合作博弈的P2P流量負載均衡模型能較好的實現(xiàn)ISP均衡的處理P2P流量請求。
[Abstract]:With the development of Internet, Peer-to-PeerP (P2P) technology has been widely used in the fields of file downloading, streaming media, instant messaging and VoIP.The impact on Internet Service provider (ISP) is very great, which causes network congestion, information security and so on.Therefore, optimizing P2P traffic becomes an important way to solve the tension between Peer-to-Peer Content provider and ISP.P2P traffic optimization is essentially to reduce inter-domain traffic transmission, the main idea is traffic localization, the core is to promote the cooperation between P2P and ISP.In this paper, the P2P traffic optimization model based on cache game and the P2P traffic optimization model based on cooperative game are deeply studied around the framework of P2P traffic optimization based on tripartite game. The main work is as follows:1) P2P traffic optimization framework based on tripartite game.Based on the analysis of the reverse engineering transmission optimization technology based on PCP and P2P traffic management technology based on ISP, this paper thinks that the game management between ISP and P2P is the trend of P2P traffic optimization management.A P2P traffic optimization framework based on tripartite game is proposed and refined into two specific P2P traffic optimization models.2) P2P traffic optimization model based on cache game.In P2P networks, the mismatch between logical network topology and actual network topology is an important reason that leads to a large number of inter-domain P2P traffic. The idea of traffic localization based on deployment cache can solve this problem very well.Based on the analysis of cache deployment mechanism, a cache game model including Tracker server / cache server and P2P user node is proposed. The optimization of traffic is attributed to the optimization of total revenue of cache.This paper presents a caching strategy based on segmented caching.Simulation results show that cache game can significantly reduce inter-domain traffic transmission and make the ISP with high inter-domain transmission cost gain positive income.3) P2P traffic load balancing model based on cooperative game.On the basis of summing up the P2P traffic optimization research based on game theory, this paper optimizes the P2P traffic from traffic load balance, proposes a P2P traffic load balancing model based on cooperative game, and deduces the equilibrium solution of the model.The dynamic convergence of equilibrium is discussed from the point of view of evolutionary iteration, and then the traffic load balancing algorithm (TLBA) is proposed and analyzed from the point of view of fairness.Finally, the simulation results show that the P2P traffic load balancing model based on cooperative game can achieve ISP balance in handling P2P traffic requests.
【學位授予單位】：湖北工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.02

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