隨著人們對多媒體串流的需求增加，點對點(P2P)傳輸技術由於其低布建成本以及高擴展性之特性，近年來已成為即時串流系統的熱門技術。然而，如何解決點對點即時串流系統的瞬間擁塞仍然是一個具挑戰的議題。當點對點即時串流系統遭受到瞬間擁塞時，由於當前系統資源無法滿足所有新進使用者，所以這些大量的新進使用者為了獲得系統資源而會彼此競爭，此資源競爭會使得大部分的新進使用者遭受到很長的啟動延遲時間。
相關研究指出使用者過度競爭系統上傳頻寬資源是造成新進使用者在瞬間擁塞期間遭受到長啟動延遲時間的主要原因。目前已經有研究提出透過週期性地決定一定數量新進使用者進入系統的槽式使用者存取控制機制及將新進使用者以固定大小的樹狀結構分成多組加入系統的使用者批次加入系統機制來減緩此問題。然而，槽式使用者存取控制機制很難精確地決定出使用者存取時槽長度，而使用者批次加入系統機制則很難決定出適當的樹狀結構大小。
在本篇論文中，我們提出一個結構化存取控制機制。結構化存取控制機制預先將新進使用者組織成一個多層網絡結構，透過預先建立新進使用者們的連結關係來取代使用週期性地存取控制，而每層網絡結構使用者數量則透過系統剩餘上傳頻寬與平均影片傳輸率的比值決定。在本篇也提出一個基於使用者上傳頻寬異質性的存取控制分析模型，數值分析結果顯示結構化存取控制機制系統成長趨勢與分析之成長趨勢結果相近。我們提出的結構化存取控制機制能有效地加速系統成長減少使用者啟動延遲時間。

With the increasing demands of multimedia streaming, Peer-to-Peer (P2P) technologies have been applied to live streaming systems due to low deployment cost and high scalability. However, P2P live streaming system still suffers a challenge when there are thousands of new peers want to join into the system in a short time, called flash crowd. When the system is under flash crowd, most of new peers suffer long startup delay. Related studies point out that peer over-competition during flash crowd is the main factor of making new peers suffer long startup delay.
Recent studies have proposed a slot-based user access control mechanism, which periodically determines a certain number of new peers to enter the system, and an user batch join mechanism, which divides new peers into several tree structures with fixed tree size. However, the slot-based user access control mechanism is difficult to accurately determine the optimal time slot length, and the user batch join mechanism is hard to determine the optimal tree size.
In this thesis, we proposed a structured access control (SAC) mechanism, which constructs new peers to a multi-layer mesh structure. The SAC mechanism constructs new peers’ connections in advance to replace periodical access control, and determines the number of peers in each layer by the system’s remaining upload bandwidth and the average video rate. Furthermore, based on heterogeneous upload bandwidth of peers, we propose an analytical model to represent the behavior of the system growth if the system can utilize the upload bandwidth efficiently. The analytical result has shown the same trend in system growth as the SAC mechanism. Additionally, the extensive simulations are conducted to show the SAC mechanism is better than two previously proposed methods in terms of system growth and startup delay.

[1] Y. Huang, T. Z. J. Fu, D. Chiu, J. C. S. Lui, and C. Huang, “Challenges, desing and analysis of a large-scale P2P-VoD system,” in Proc. ACM SIGCOMM 2008 Conf. on Data Commun., SIGCOMM’08,pp. 375-388, Aug. 2008.
[2] “PPTV.” [Online]. Available: http://www.pptv.com
[3] “PPStreaming.” [Online]. Available: http://www.pps.tv
[4] Y. Liu, Y. Guo, C. Liang, “A Survey on Peer-to-Peer Video Streaming Systems,” Peer-to-peer Networking and Applicat., vol. 1, pp. 18-28, Mar. 2008.
[5] M. Castro, P. Druschel, A. M. Kermarrec, A. Nandi, A. Rowstron, A. Singh, “SplitStream: High-bandwidth Multicast in cooperative environments,” in Proc. 19th ACM Symp. on Operating Syst. Principle, SOSP’03, pp. 298-313, Oct. 2003.
[6] B. Li, S. Xie, Y. Qu, Y. Keung, C. Lin, J. Liu, and X. Zhang, “Inside the New Coolstreaming: Principles, Measurements and Performance Implications,” in Proc. 27th IEEE Int. Conf. on Comput. Commun., INFOCOM’08, pp. 1031-1039, Apr. 2008.
[7] D. Kostic, A. Rodriguez, J. Albrecht, and A. Vahdat “Bullet: High Bandwidth Data Dissemination Using an Overlay Mesh,” in Proc. 19th ACM Symp. on Operating Syst. Principle, SOSP’03, pp. 282-297, Oct. 2003.
[8] N. Magharei, R. Rejaie, “PRIME: Peer-to-Peer Receiver-Driven Mesh-Based Streaming,” in Proc. 26th IEEE Int. Conf. on Comput. Commun., INFOCOM’07, pp. 1415-1423, May 2007.
[9] N. Magharei, R. Rejaie, Y. Guo, “Mesh or Multiple-Tree: A Comparative Study of Live P2P Streaming Approaches,” in Proc. 26th IEEE Int. Conf. on Comput. Commun., INFOCOM’07, pp. 1424-1432, May 2007.
[10] H. Wehbe, G. Babonneau, and B. Cousin, “Fast Packet Recovery for PULL-Based P2P Live Streaming Systems,” in Proc. 2nd Int. Conf. on Advances in P2P Syst., AP2PS’10, pp. 7-13, Oct. 2010.
[11] X. Zhangm, J. Liu, B. Li, and T. O. Yum,“Cool-streaming/Donet: A Data-driven Overlay Network for Peer-to-Peer Live Media Streaming,” in Proc. 24th Annu. Joint Conf. of the IEEE Comput. and Commun. Soc., INFOCOM’05, vol. 3, pp. 2102-2111, Mar. 2005.
[12] D. Kostic, R. Braud, C. Killian, and E. Vandekieft, J. W. Anderson, A. C. Snoeren, and A. Vahdat,“Maintaining High Bandwidth under Dynamic Network Conditions,” in Proc. 2005 USENIX Annu. Tech. Conf., USENIX’05, pp. 1501-1511, Apr. 2005.
[13] F. Liu, B. Li, L. Zhong, and B. Li, “Understanding the Flash Crowd in P2P Live Video Streaming Systems,” in Proc. 17th Int. Packet Video Workshop, PV’09, pp. 1-10, May 2009.
[14] S.M.Y. Seyyedi, B. Akbari, “Hybrid CDN-P2P Architectures for Live Video Streaming: Comparative Study of Connected and Unconnected Meshes,” in Proc. 2011 Int. Symp. on Comput. Networks and Distributed Syst., CNDS’11, pp. 175-180, Feb. 2011.
[15] G. Deng, C. Chen,“Simulation Study of Buffering Mechanism in P2P Live Streaming System,” in Proc. 4th IET Int. Conf. on Wireless, Mobile & Multimedia Networks, ICWMMN’11, pp. 266-269, Nov. 2011.
[16] A. Vlavianos, M. Iliofotou, and M. Faloutsos, “BiToS: Enhancing BitTorrent for Supporting Streaming Applications,” in Proc. 25th IEEE Int. Conf. on Comput. Commun., INFOCOM’06, pp. 1-6, Apr. 2006.
[17] K. W. Hwang, V. Misra, and D. Rubenstein, “Stored media streaming in bittorrent-like p2p networks,” Tech. Rep., Columbia University, NY, no. cucs-024-08, Apr. 2008.
[18] X. Hei, C. Liang, J. Liang, Y. Liu, and K. Ross, “A Measurement Study of a Large-Scale P2P IPTV System,” IEEE Trans. Multimedia, vol. 9, no. 8, pp. 1672-1687, Dec. 2007.
[19] B. Li, G. Keung, S. Xie, F. Liu, Y. Sun, and H. Yin, “An Empirical Study of Flash Crowd Dynamics in a P2P-based Live Video Streaming System,” in Proc. 2008 IEEE Globe Telecommun. Conf., GLOBECOM’08, pp. 1-5, Nov. 2008.
[20] F. Liu, B. Li, L. Zhong, B. Li, H. Jin, and X. Liao, “Flash Crowd in P2P Live Streaming Systems: Fundamental Characteristics and Design Implications,” IEEE Trans. Parallel Distrib. Syst., vol.23, no.7, pp. 1227-1239, July 2012.
[21] H. Wu, H. Jiang, J. Liu, Y. Sun, J. Li, and Z. Li, ”How P2P Live Streaming Systems Scale Quickly Under a Flash Crowd?,” in Proc. 30th IEEE Int. Performance Computing and Commun. Conf., IPCCC’11, pp.1-8, Nov. 2011.
[22] T. Chung, and O. Lin, “A Batch Join Scheme for Flash Crowd Reduction in IPTV Systems,” in Proc. 17th IEEE Int. Conf. on Parallel and Distrib. Syst., ICPADS’11, pp. 823-828, Dec. 2011.
[23] Y. Chen, B. Zhang, C. Chen, D.M. Chiu,“ Performance Modeling and Evaluation of Peer-to-Peer Live Streaming Systems Under Flash Crowds,” IEEE/ACM Trans. Networking, vol. 22, no. 4, pp. 1106-1120 Aug. 2014.
[24] F. Liu, B. Li, L. Zhong, B. Li, and D. Niu, “How P2P Streaming Systems Scale Over Time Under a Flash Crowd,” in Proc. 8th Int. Workshop on Peer-to-Peer Syst., IPTPS’09, pp. 1-5, Apr. 2009.
[25] Z. Chen, B. Li, G.Keung, H. Yin, C. Lin, and Y. Wang, “How Scalable could P2P Live Media Streaming System be with the Stringent Time Constraint?,” in Proc. 2009 IEEE Int. Commun. Conf., ICC’09, pp. 1-5, June 2009.
[26] A. Varga, “The OMNeT++ discrete event simulation system,” in Proc. 15th European Simulation Multiconference, ESM’01, pp.319-324, June 2001.
[27] I. Baumgart, B. Heep, S. Krause, “OverSim: A scalable and flexible overlay frame work for simulation and real network applications,” in Proc. 9th IEEE Int. Conf. on Peer-to-Peer Computing, P2P’09, pp. 87-88, Sep. 2009.
[28] Trace file [Online]. Available:http://www-tkn.ee.tu-berlin.de/research/trace/ltvt.html
[29] C. Huang, J. Li, and K. Ross, “Can Internet Video-on-Demand be Profitable?,” in Proc. ACM SIGCOMM 2007 Conf. on Applications, technologies, architectures, and protocols for comput. Commun., SIGCOMM’07, pp. 133-144, Aug. 2007.