Title: 搭配隨機預測模型於虛擬網路服務延遲且保證服務品質的服務功能鏈配置
Deploying QoS-assured Service Function Chains with Stochastic Prediction Models on VNF Latency
Authors: 雷宗翰
Lei, Tsung-Han
Wen, Hung-Pin
Keywords: 網路功能虛擬化;服務品質保證;服務功能鏈;機器學習;Network Function Virtualization;Quality of Service;Service Function Chain;Machine Learning
Issue Date: 2017
Abstract: 目前的網路功能虛擬化技術(NFV)藉由將虛擬化的網路功能(VNF)配置在伺服器的虛擬機器上,進而對服務功能鏈(SFC)帶來很高的彈性。一些特定的應用,例如影音的服務,有可能會需要端到端延遲的服務品質(QoS)的保證。然而,VNF的處理延遲以及佇列延遲會隨著虛擬機器的資源配置(虛擬CPU數量、虛擬記憶體大小)以及目前的使用情況(目前網路流量、目前CPU使用率)而有所變化。再者,封包的延遲會呈現一種機率分布,而並非是一個定值。為了要精準的描述VNF延遲的分布情形,我們提出了一個基於隨機森林回歸法(Random Forest regression)的延遲分布預測模型。實驗的結果顯示我們的模型可以在大約10\%的誤差之內預測封包的延遲分布。在這個模型的基礎下,我們進而提出了一個可以保證服務品質的服務功能鏈配置演算法,包含保證使用者的端到端延遲、以及使用網路頻寬。實驗顯示我們的演算法在保有高程度的可擴展性下,可以最大化使用者接受率,同時與混合整數規劃(MIP)所計算出的最佳解最多只有6\%的差異。
Current Network Function Virtualization (NFV) with Virtualized Network Functions (VNFs) running as virtual machines on commodity servers enables flexibility to Service Function Chaining (SFC). Specific applications may require Quality of Service (QoS) on end-to-end latency. However, the processing delay and the queuing delay of VNFs varies with virtual resource configurations (vCPU and vMemory), as well as physical usage (traffic amount, CPU utilization). Moreover, packet delays are randomly distributed, instead of a fixed value. To accurately model the latency distribution of one VNF, a prediction method using random-forest regression is proposed. Evaluation results show that our method can precisely predict the latency distribution of the two sample VNFs with only 10% errors. On the basis of the model, a QoS-assured SFC deployment algorithm is also presented to guarantee end-to-end latency and bandwidth consumption of users. Experiments show that our algorithm enables high degree of scalability, and meanwhile maximizes user acceptance rates with 6% difference from the optimal solution from the mixed integer programming.
URI: http://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070450721
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