在軟體定義網路中利用長短期記憶演算法進行網路控管

Using LSTM algorithm to improve network management in SDN

劉思妤(Szu-Yu Liu)

指導教授 ： 古政元

國立交通大學/管理學院/資訊管理研究所/碩士(2019年)

摘要

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。

關鍵字

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

並列摘要

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.

並列關鍵字

Traffic engineering ； SDN ； Congestion control ； QoS ； LSTM

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