.Network performance estimation is a key enabler in achieving effi-cient network operation.It can assist in important tasks such as topology design,parameter tuning,and capacity planning.The most popular methods have r....Network performance estimation is a key enabler in achieving effi-cient network operation.It can assist in important tasks such as topology design,parameter tuning,and capacity planning.The most popular methods have recently been based on Convolutional Neural Network(CNN)or Recurrent Neural Net-work(RNN).However,many of these methods focus excessively on particular aspects of network features.They often overlook the diversity and complexity in network performance evaluation.This paper proposes a novel model TrafficNet for network performance estimation.This model uses aggregators to learn inter-flow correlations and intra-flow dependencies.By creating specialized feature extraction components for different types of network traffic and using the adap-tive mechanism to fuse these features,we aim to improve the accuracy of network performance evaluation.Furthermore,our extensive experiments have shown that TrafficNet can improve the Mean Squared Error(MSE)by 58.3%compared with the SOTA models.展开更多
文摘.Network performance estimation is a key enabler in achieving effi-cient network operation.It can assist in important tasks such as topology design,parameter tuning,and capacity planning.The most popular methods have recently been based on Convolutional Neural Network(CNN)or Recurrent Neural Net-work(RNN).However,many of these methods focus excessively on particular aspects of network features.They often overlook the diversity and complexity in network performance evaluation.This paper proposes a novel model TrafficNet for network performance estimation.This model uses aggregators to learn inter-flow correlations and intra-flow dependencies.By creating specialized feature extraction components for different types of network traffic and using the adap-tive mechanism to fuse these features,we aim to improve the accuracy of network performance evaluation.Furthermore,our extensive experiments have shown that TrafficNet can improve the Mean Squared Error(MSE)by 58.3%compared with the SOTA models.
