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.

With the massive and rapid growth of network traffic, sketching techniques are widely used to estimate a variety of network flow-level metrics, e.g., flow size, top-k flows, and the number of flows. However, how to cope with the constantly changing network environment and achieve high accuracy with limited switch resources is still a challenging problem. Existing studies do not well address the issues of identifying and correcting the inaccurately estimated flows. Therefore, we propose TalentSketch, an adaptive and high-precision hybrid measurement framework based on LSTM. TalentSketch uses LSTM to learn the sketch features with the captured traffic information. It could identify the inaccurately estimated flows with an error-prone flow model and correct them with a well-trained regression model. We conduct extensive experiments to verify the performance of TalentSketch. Experimental results show that, without increasing switch resource overhead, TalentSketch improves the measurement accuracy of different kinds of sketches by 12%∼23%. More importantly, it can track network fluctuations and provide feedback on the overall accuracy of a sketch in real-time.