- 學位論文
基於軟體定義網路下的點對點系統聲譽管理機制
SDN-enabled Reputation Management Mechanism for P2P System
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摘要
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。
並列摘要
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.
參考文獻
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