# A Survey: Network Feature Measurement Based on Machine Learning
## 3 Network Measurement Based on ML Method
### Bandwidth  
The amount of data that may be transmitted in a given length of time is referred to as network bandwidth. The fundamental element of digital communication, particularly in packet networks, is bandwidth, which refers to the quantity of data that can be carried in a given length of time through links or network channels. The three indicators, capacity, available bandwidth, and batch transmission capacity, are all measured by existing bandwidth estimate techniques. When transmitting data, a reliable bandwidth should ensure that there is no congestion and that it can be cleared quickly by predetermined standards [70]. The statistical cross-flow model and the self-induced congestion model, two frequently used bandwidth assessment techniques, now have issues. When the network scenario is complex, there may be inaccuracy or behavioral interference. To assess the bandwidth of Internet links, new techniques must be used. The majority of academics now favor ML. [PERSON] et al. [29] suggested a system for anticipating traffic bandwidth. A support vector machine can interpolate or extrapolate the system output based on the characteristics of the test input, even if the system output is not present in the training dataset. A support vector machine also has a reasonable computational cost. A simulation was used to determine the available bandwidth. In all test instances, the results demonstrate that the pathChirp model is superior to the package sequence model. Additionally, this approach has more precise estimates than the two widely used tools, pathChirp and Spruce, by combining the pathChirp-like model with an SVM. The original dichotomy approach was replaced by a neural network by [PERSON] et al. [71] to decide the next detection rate, and the available bandwidth was calculated using the information already present in the program between them. To forecast the output bandwidth and an input vector that is not filled up, two neural networks were trained. To reduce prediction difficulty, [PERSON] et al. [28] simplified the prediction problem of the actual available bandwidth into a classification problem. The prediction results were filtered through training with fewer data. A k-dimensional vector was used as the input. The process was, detect, collect relevant information, combine them into a complete k-dimensional vector, and then input them into the classifier. The SVM, K-NN, AddBoost, and other ML methods were selected. The range to which the actual available bandwidth belongs is the output, and the midpoint of the range is set as the estimated value; median filtering was applied to the estimated values to improve the accuracy. [PERSON] et al. [30] unveiled a neural network-based empirical bandwidth estimation tool. The neural network was trained using simulation data, and it is capable of accurately estimating the physical and available bandwidth for single-hop and multi-hop networks in both the lab setting and under real-world settings on the ETOMIC test bench. It has been established that the input data are the only factor limiting the accuracy of bandwidth estimates. Other network analysis issues can simply be addressed using this approach. [PERSON] et al. [72] trained the packet dispersion vector as a characteristic of the available bandwidth using a neural network. To choose the next detection rate, an iterative neural network rather than a binary search approach was suggested. The issue of estimating the available bandwidth was described as a classification challenge. The suggested method is assessed using support vector regression, Gaussian process regression, and random forest. The findings demonstrate that by lowering the bias and variability, the neural network may greatly enhance the estimation of the available bandwidth. [PERSON] et al. [73] evaluated three machine-learning techniques to forecast bandwidth usage 15 seconds in advance. Network link use can be predicted using LSTM models with good accuracy (the error is less than 3%).