419 research outputs found
NeuTM: A Neural Network-based Framework for Traffic Matrix Prediction in SDN
This paper presents NeuTM, a framework for network Traffic Matrix (TM)
prediction based on Long Short-Term Memory Recurrent Neural Networks (LSTM
RNNs). TM prediction is defined as the problem of estimating future network
traffic matrix from the previous and achieved network traffic data. It is
widely used in network planning, resource management and network security. Long
Short-Term Memory (LSTM) is a specific recurrent neural network (RNN)
architecture that is well-suited to learn from data and classify or predict
time series with time lags of unknown size. LSTMs have been shown to model
long-range dependencies more accurately than conventional RNNs. NeuTM is a LSTM
RNN-based framework for predicting TM in large networks. By validating our
framework on real-world data from GEEANT network, we show that our model
converges quickly and gives state of the art TM prediction performance.Comment: Submitted to NOMS18. arXiv admin note: substantial text overlap with
arXiv:1705.0569
Application of learning algorithms to traffic management in integrated services networks.
SIGLEAvailable from British Library Document Supply Centre-DSC:DXN027131 / BLDSC - British Library Document Supply CentreGBUnited Kingdo
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