Learning-Based Sketch for Adaptive and High-Performance Network Measurement

作者：Fuliang Li, Yiming Lv, Yangsheng Yan, Chengxi Gao, Xingwei Wang, Jiannong Cao · 发表于：IEEE/ACM Transactions on Networking · 年份：2024 · DOI：10.1109/tnet.2024.3364176 · 被引用次数：6 · 研究领域：Network Security and Intrusion Detection、Software-Defined Networks and 5G、Software System Performance and Reliability

With the development of network measurement technologies, a hybrid measurement architecture can effectively optimize the sketch structure in switches, making it more adaptable to the current complex and volatile network environment. However, current optimization technologies based on hybrid measurement architectures generally suffer from insufficient automation, difficulty of learning effective numerical features, and lack of generality, resulting in poor scalability in real deployment. To solve these problems, we propose theTalentSketchframework, based on which we further developDeepSketchfor effective sketch optimization. First, we useSeq2Seqto automatically identify target flows instead of relying on manual thresholds. Second, we propose a new training strategy that extracts low-precision flows for models with weak learning capabilities. Last, we develop a new sketch optimization framework that can optimize different kinds of sketches only by changing the training data for generality. A large number of experimental results show thatDeepSketchexhibits superior performance. For example: (1) the accuracy of optimized sketches has increased by 20% to 73%, (2) Without replacing the model structure, the accuracy of the optimized sketches can generally reach over 80%. (3) The impact of low sampling rates on accuracy is less than 1% on various sketches.