電機資訊學院: 電信工程學研究所指導教授: 陳銘憲王凡熙Wang, Fan-HsiFan-HsiWang2017-03-062018-07-052017-03-062018-07-052015http://ntur.lib.ntu.edu.tw//handle/246246/276567隨著巨量資料產生，資料中心投入龐大的成本建構實體網路，而為了確實利用這些網路頻寬，需要有效地流量管理。近來的研究主要針對偵測網路中之巨量資料流，為它們安排最佳傳輸路徑，以增進網路頻寬利用率。然而，當前的偵測方法仍無法準確的判斷巨量資料流量。因此，本篇論文利用串流探勘技術來改善巨量資料流量預測準確度，並提出一套新的排程機制來避免預測誤差所造成的網路擁塞，系統先由每條資料流中最初數個封包所抽取特徵建立串流探勘模型，根據此模型對網路中新產生的資料流進行流量及持續時間的預測。軟體定義網路中的控制層可監控流量預測之誤差範圍，因此可綜合考慮預測結果及其誤發，對巨量資料流做動態且可靠的排程，以降低網路擁塞機率。As a huge volume of data are generated everyday, data centers have invested a huge amount of resources to construct network infrastructures. To efficiently utilize the bandwidth of the networks, effective flow control mechanisms are mandates. Recent researches mainly focus on detecting elephant flows and allocating optimal transmission paths for them to increase the network utilization. However, the state-of-the-art approaches are still unable to precisely detect the elephant flows. This is because the traffic engineering usually considers only the average values, but pays little attention on the variance of the flows. To address the above mentioned critical issue, this paper proposes a data stream mining approach to boost the performance of elephant flows detection. We also propose a scheduling mechanism to avoid the congestion produced by the prediction error. Our system first extracts specific features from the first few packets of flows to build a stream mining model. Then, based on this model, our system is able to predict the flow demand and duration of the newly-generated flows in the network. The control layer in the Software Defined Network (SDN) is able to monitor the interval of the predicted flow. Therefore, our system can both consider mean and variance of the demand prediction results simultaneously to dynamically and reliably schedule the elephant flows, and the probability of network congestion is greatly reduced.1943176 bytesapplication/pdf論文公開時間: 2021/2/15論文使用權限: 同意有償授權(權利金給回饋學校)流量排程資料串流探勘大數據巨量資料流偵測資料中心網路軟體定義網路Flow schedulingData stream miningBig-dataElephant flow detectionDatacenter networkingSoftware defined networkingthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/276567/1/ntu-104-R02942059-1.pdf