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研究生： 游明軒
Yu, Ming-Hsuan
論文名稱： P2P即時影音串流系統之Peer負載監控與平衡機制
Monitoring and Balancing of Peer Loading for P2P Live Streaming System
指導教授： 黃能富
Huang, Nen-Fu
口試委員: 陳俊良
Chen, Jiann Liang
石維寬
Shih, Wei-Kuan
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年： 2015
畢業學年度： 103
語文別： 中文
論文頁數： 58
中文關鍵詞： 點對點網路 、即時串流 、低延遲
外文關鍵詞： P2P Networks, Live streaming, Low latency
相關次數： 點閱：4  下載：0 
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    • 隨著網路科技蓬勃發展，使用者的頻寬使用量也越來越大。對於即時影音串流系統來說，雖然有助於它的發展以提供更高解析度的影像畫質，但相對地，它們付出相當大的花費在頻寬成本上。而藉由P2P架構，使用者可以分享他們頻寬以減少系統本身的負擔。但由於須在時限內將影音串流傳送到使用者，P2P拓樸便成為系統中相當重要的一環，一個強壯及良好的拓樸能給予使用者好的服務品質，而一個脆弱拓樸會導致頻繁的拓樸變動，進而影響使用者的服務品質，降低使用者體驗。
    在本篇論文中，我們研究著重於如何建立一個良好的拓樸並維護它。在三個層面限制下，提供良好的影像品質。第一個層面是盡可能節省頻寬成本，充分利用使用者的頻寬資源；第二個層面是在基於一個保證影像延遲底下，再盡可能降低使用者的影像延遲；最後一個層面是盡可能保持住良好的拓樸，避免良好的拓樸崩壞。基於這三個層面，我們實作出一套即時影音串流系統，並設計一系列的演算法強化此系統。
    實驗方面，我們在PlanetLab上做一系列的實驗以驗證所提出的演算法。除此之外，我們更在真實網路環境測試我們的系統，使用者遍及全國各地。實驗結果顯示我們的演算法能強化系統，更證明我們的系統在真實環境之可行性。

    With the development of Internet, the requirement of bandwidth usages increases. Live video streaming systems are favorable to provide higher resolution video; however, they undertake more bandwidth costs. By means of P2P architecture, users share their bandwidth to save bandwidth resource on servers. Since the data information has to be disseminated to all of users with time limit, the topology of P2P systems plays a considerable role. A well-structured topology can provide users better quality of services while a fragile topology decreases quality of services even user experience.
    In this thesis, our research aims at how to construct a robust topology and how to maintain it with the following limitations. First, the system should save the bandwidth resource as more as possible and exploit the upload bandwidth from users. Second, despite the guaranteed latency, the system should minimize the delay of video. Third, the system prevents the topology from fragments. Based on these limitations, we implement a P2P based live streaming system, and several proposed algorithms are designed to improve the system.
    The proposed algorithms are verified in the experiments performed on PlanetLab. Furthermore, we perform nationwide experiments in Taiwan. The experimental results show the superior performance of the algorithms and the feasibility.

    Contents Chapter 1 Introduction 1 Chapter 2 Related Works 3 2.1 Tree-Push Topology 3 2.2 Mesh-Pull Topology 4 2.3 Mesh-Push Topology 5 2.4 Hybrid Tree-Mesh Topology 6 Chapter 3 System Architecture 8 3.1 System Overview 8 3.1.1 Sources 8 3.1.2 Media Server 9 3.1.3 Manager Server 9 3.1.4 PK Server 9 3.1.5 Log Server 10 3.1.6 Channel Network 10 3.2 Chunk Format 10 3.3 Streaming 11 3.4 Time Synchronization 12 3.5 Source Delay 13 3.6 Rescue 14 3.7 Two-way Connect 15 Chapter 4 Algorithms 17 4.1 Queue Scheduling 18 4.2 ESD Parent Selection 19 4.3 Peer Loading Detection 23 4.4 Rescue Grouping 26 Chapter 5 Experimental Results 30 5.1 Experiments on PlanetLab 30 5.1.1 Experiments on Different Parent Selections 31 5.1.2 Experiments on Peer Loading Detection 35 5.1.3 Experiments on Rescue Grouping 40 5.2 Nationwide Experiments 42 Chapter 6 Conclusion and Future Works 46 Figure List Fig. 2 1 Tree-Push Topology 4 Fig. 2 2 Mesh-Pull Topology 5 Fig. 2 3 Mesh-Push Topology 6 Fig. 2 4 Hybrid Tree-Mesh Topology 7 Fig. 3 1 System Architecture 8 Fig. 3-2 Chunk Format 10 Fig. 3-3 Streaming Flow 12 Fig. 3 4 Time Synchronization 12 Fig. 3 5 Source Delay of Chunk k 14 Fig. 3-6 Incoming Flow Blocked by Firewall 15 Fig. 3-7 Procedure of Two-Way Connect 16 Fig. 4 1 The Block Diagram of the Algorithms in Peer Life Cycle 18 Fig. 4 2 The Queueing Model 19 Fig. 4 3 The Bandwidth-N(q) Model 20 Fig. 4 4 The Illustration of Estimated Source Delay 22 Fig. 4 5 The Illustration of Single Node Failure Problem 27 Fig. 5-1 Nodes Distribution on PlanetLab 30 Fig. 5-2 Peer Source Delay Distribution with Different Selection Policies 33 Fig. 5-3 Performance with Different Selection Policies 34 Fig. 5-4 Comparison of Peer Loading Detection 36 Fig. 5-5 Standard Deviation of Bandwidth Influenced by α 37 Fig. 5-6 Server Peak Bandwidth Influenced by α 37 Fig. 5-7 Number of Rescues Influenced by α 38 Fig. 5-8 Mean Source Delay Influenced by α 39 Fig. 5-9 Continuity Influenced by α 39 Fig. 5-10 Server Bandwidth Saving Ratio Influenced by α 39 Fig. 5-11 Comparison of Rescue Grouping 41 Fig. 5-12 Performance of Rescue Grouping 42 Fig. 5-13 Platform for Live Streaming 43 Fig. 5-14 Comparison of Two-way Connect and STUN 44   Table List Table 4 1 Peer Substream-State 21 Table 4 2 Definitions and Symbols 24 Table 4 3 Pseudo Code of Peer Loading Detection 25 Table 4 4 Pseudo Code of Source Delay Detection 28 Table 5 1 Nationwide Experiments using STUN 44 Table 5 2 Nationwide Experiments using Two-way Connect 45

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