A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction

TL;DR

This paper introduces an LSTM-based RNN framework for predicting network traffic matrices, demonstrating its effectiveness on real-world data with quick convergence and high accuracy.

Contribution

The paper presents a novel LSTM RNN framework specifically designed for short and long-term network traffic matrix prediction, outperforming existing methods.

Findings

LSTM models converge quickly on real network data.

The framework achieves state-of-the-art prediction accuracy.

Effective for small-sized models in large networks.

Abstract

Network Traffic Matrix (TM) prediction is defined as the problem of estimating future network traffic 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 experience to classify, process and predict time series with time lags of unknown size. LSTMs have been shown to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we propose a LSTM RNN framework for predicting short and long term Traffic Matrix (TM) in large networks. By validating our framework on real-world data from GEANT network, we show that our LSTM models converge quickly and give state of the art TM prediction performance for relatively small sized models.