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研究生:劉思妤
研究生(外文):Liu, Szu-Yu
論文名稱:在軟體定義網路中利用長短期記憶演算法進行網路控管
論文名稱(外文):Using LSTM algorithm to improve network management in SDN
指導教授:古政元古政元引用關係
指導教授(外文):Ku, Cheng-Yuan
口試日期:2019-07-29
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:英文
論文頁數:54
中文關鍵詞:流量工程軟體定義網路壅塞控制品質保證長短期記憶演算法
外文關鍵詞:Traffic engineeringSDNCongestion controlQoSLSTM
相關次數:
  • 被引用被引用:0
  • 點閱點閱:245
  • 評分評分:
  • 下載下載:56
  • 收藏至我的研究室書目清單書目收藏:0
到目前為止存在許多網路監控技術,網路管理員必須擁有準確的監控才能高效營運。在本文中,我們提出了動態調整閾值-長短期記憶網路方法進行靈活的預防壅塞機制。在資源有限的網路環境中,軟體定義網路流量工程可提高網路利用率和服務品質。我們使用避免壅塞最小帶寬利用率路由機制,控制器定期監視網路中每個鏈路的流量利用率,過度使用的鏈路即識別為瓶頸鏈路。藉由埠利用率去除瓶頸鏈路,將路由演算法要經過的剩餘帶寬計算是否成為備用選擇路徑。當網路流量增加時,提出的動態調整埠利用率方法-長短期記憶網路可以有效地預測流量,提高網路效率和網路服務品質。
There are a lot of network monitoring technologies existed so far. Network administrators must have accurate monitoring to operate efficiently. In this paper, we propose a dynamic adjustment threshold method – Long short term memory network. In a resource-constrained network, SDN traffic engineering (SDN TE) can improve network utilization and service quality. we use a minimum bandwidth utilization routing mechanism to avoid congestion. The controller periodically monitors the traffic utilization of each link in the network. The overused links are identified as a bottleneck link. Removing the bottleneck links by the utilization rate, the remaining bandwidth calculation to be passed by the routing algorithm becomes the alternate selection path. When network traffic increases, the proposed dynamic adjustment utilization method - long-term and short-term memory networks can effectively predict traffic and improve network efficiency and network service quality.
摘要 i
Abstract ii
誌謝 iii
Contents iv
List of Tables v
List of Figures vi
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Problem Statement 2
1.3 Research Method 3
1.4 Research Process 4
Chapter 2 Related Work 5
2.1 Software Defined Network 5
2.2 OpenFlow 7
2.3 Controller mechanisms in Ryu 13
2.4 Long short-term memory 14
2.5 QoS in SDN 17
Chapter 3 Proposed Framework 20
3.1 Problem definition 20
3.2 Overview the architecture of modules 20
3.3 Proposed system design 28
Chapter 4 Evaluation 34
4.1 Experiment Setting 34
4.2 Scenario Analysis 39
4.3 Discussion and Analysis 49
Chapter 5 Conclusion and Future Work 51
Reference 53
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