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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 雷欽隆 | |
dc.contributor.author | Ting-Chieh Lai | en |
dc.contributor.author | 賴廷杰 | zh_TW |
dc.date.accessioned | 2021-05-13T08:36:49Z | - |
dc.date.available | 2016-08-24 | |
dc.date.available | 2021-05-13T08:36:49Z | - |
dc.date.copyright | 2016-08-24 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-08-07 | |
dc.identifier.citation | [1] RFC 1 https://tools.ietf.org/html/rfc1
[2] Official BitTorrent Specification http://www.bittorrent.org/beps/bep_0003.html [3] Cohen, B. (2003, June). Incentives build robustness in BitTorrent. In Workshop on Economics of Peer-to-Peer systems (Vol. 6, pp. 68-72). [4] Buragohain, C., Agrawal, D., and Suri, S. (2003). A game theoretic framework for incentives in P2P systems. arXiv preprint cs/0310039. [5] Feng, H., Zhang, S., Liu, C., Yan, J., and Zhang, M. (2008, October). P2P incentive model on evolutionary game theory. In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing (pp. 1-4). IEEE. [6] Wang, T. M., Lee, W. T., Wu, T. Y., Wei, H. W., and Lin, Y. S. (2012, March). New p2p sharing incentive mechanism based on social network and game theory. In Advanced Information Networking and Applications Workshops (WAINA), 2012 26th International Conference on (pp. 915-919). IEEE. [7] Rius, J., Cores, F., and Solsona, F. (2009, October). A new credit-based incentive mechanism for p2p scheduling with user modeling. In Advances in P2P Systems, 2009. AP2PS'09. First International Conference on (pp. 85-91). IEEE. [8] Feldman, M., and Chuang, J. (2005). Overcoming free-riding behavior in peer-to-peer systems. ACM sigecom exchanges, 5(4), 41-50. [9] Fundation, Open Networking. 'Software-defined networking: The new norm for networks.' ONF White Paper (2012). [10] McKeown, N., Anderson, T., Balakrishnan, H., Parulkar, G., Peterson, L., Rexford, J., ... and Turner, J. (2008). OpenFlow: enabling innovation in campus networks. ACM SIGCOMM Computer Communication Review, 38(2), 69-74. [11] Ryu https://osrg.github.io/ryu/ [12] Mininet http://mininet.org/ [13] Deluge http://deluge-torrent.org/ [14] Libtorrent http://www.libtorrent.org/ [15] Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE transactions on pattern analysis and machine intelligence, 20(8), 832-844. [16] Deluge RPC Client https://github.com/JohnDoee/deluge-client [17] python-bittorent https://github.com/JosephSalisbury/python-bittorrent [18] Zghaibeh, M., and Anagnostakis, K. G. (2007). On the impact of p2p incentive mechanisms on user behavior. NetEcon+ IBC. [19] Ryu-QoS https://osrg.github.io/ryu-book/en/html/rest_qos.html [20] Random Forest Classifier usage in scikit-learn http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/3790 | - |
dc.description.abstract | 隨著軟體定義網路(SDN)以及機器學習(Machine Learning)越來越熱門,他們成了我們的動機讓我們開始想像著點與點(P2P)網路與他們所能迸出的火花。
然而由於目前大規模的佈建軟體定義網路仍有不少困難,因此我們選擇在環境中混合軟體定義網路與傳統網路,而非直接全部使用軟體定義網路進行實驗。 這篇論文提出一個獎懲機制加強BitTorrent系統中現有的獎懲機制,盡可能減少貢獻度少的不良使用者所使用的流量。我們在Mininet中模擬網路及製造有著不同行為模式的BitTorrent使用者,而資料中心則從交換機、使用者及Tracker收集資料,並針對使用者每段時間的行為進行分類,資料中心也利用分類結果估算出一個分數給每位使用者,並使用此分數給予使用者獎懲,其中我們採用服務品質(QoS)的調整給與獎懲,而服務品質的調整則是利用SDN及Ryu-QoS的功能達到的。 在實驗中我們在傳統網路內模擬了65個使用者,其中大部分都在我們所模擬出的傳統網路內,但其中有一個使用者在我們所模擬的網路外提供檔案。我們可以從貢獻度低的使用者的平均下載量發現其曲線在懲罰後是減少的,進而證明實驗結果有效。 | zh_TW |
dc.description.abstract | With the rising popularity of SDN (Software-defined Networking) and machine learning, we are motivated to apply these two things to peer-to-peer (P2P) network to see what it can do for P2P network.
Considering the large-scale deployment of SDN nowadays is still a big problem, we construct our environment by the combination of SDN network and traditional network rather than using SDN network for whole environment only. This thesis proposes an incentive policy to reinforce the existing incentive policy in BitTorrent system and the goal of this thesis is to decrease the traffic of bad users as much as possible. We emulate the network in Mininet and several BitTorrent users with different user behavior. The data center collects information comes from switches, hosts, and the tracker and use machine learning model to classify the type of user behavior in each period. The data center also derives a score for each user, and give punishments or rewards to them according to their score. The punishments and rewards are presented in the form of quality of service (QoS), and the task of adjusting QoS is achieved with the help of SDN and Ryu-QoS. There are 65 hosts distributed in our experimental environment. Almost all of them are all distributed in the traditional network, but one of them is distributed outside the network we emulated to provide the source of data. We can see the result of our experiments from the curve of average download speed of all bad users, which exactly decrease after our punishments. | en |
dc.description.provenance | Made available in DSpace on 2021-05-13T08:36:49Z (GMT). No. of bitstreams: 1 ntu-105-R03921079-1.pdf: 1416268 bytes, checksum: d9437fed5540088058eb9928540202f7 (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | 口試委員會審定書 #
致謝 i 中文摘要 ii Abstract iii Contents iv List of Figures vi List of Tables vii Chapter 1 Introduction 1 Chapter 2 Background 3 2.1 Software-defined Network 3 2.1.1 SDN Architecture 3 2.1.2 OpenFlow 4 2.1.3 Ryu 5 2.1.4 Mininet 6 2.2 Peer to peer 6 2.2.1 BitTorrent 7 2.3 Incentive policy in BitTorrent 7 2.4 Deluge 8 2.5 Random Forest Classifier 9 Chapter 3 Simulation 10 3.1 Environment 10 3.2 Deluge Client 12 3.3 Tracker 13 3.4 Data Center 14 3.5 Controller 14 3.6 User Behavior 15 3.7 User type 16 Chapter 4 User classification and Punishment 18 4.1 User classification 18 4.1.1 Extracted features 18 4.2 Punishment 22 4.2.1 Ryu-QoS 22 4.2.2 Incentive policy 23 Chapter 5 Evaluation 25 5.1 Hardware and Network Settings 25 5.2 User Settings 25 5.3 Ryu-QoS setting 28 5.4 User Behavior Classification 28 5.5 Punishment Results 30 Chapter 6 Conclusion 37 Bibliography 38 APPENDIX 40 A1 The settings and results of ratio and the results in the experiment. 40 A2 The results of each classification in the experiment 42 A3 The scores of users in our experiment 44 A4 The limits of download speed in the experiment 46 | |
dc.language.iso | en | |
dc.title | 基於軟體定義網路下的點對點系統聲譽管理機制 | zh_TW |
dc.title | SDN-enabled Reputation Management Mechanism for P2P System | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 顏嗣鈞,郭斯彥,黃秋煌,莊文勝 | |
dc.subject.keyword | BitTorrent,軟體定義網路,獎懲機制,機器學習, | zh_TW |
dc.subject.keyword | BitTorrent,SDN,Incentive policy,Machine Learning, | en |
dc.relation.page | 48 | |
dc.identifier.doi | 10.6342/NTU201602055 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2016-08-08 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
顯示於系所單位: | 電機工程學系 |
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