Predicting Bandwidth Utilization on Network Links Using Machine Learning

TL;DR

This paper demonstrates a machine learning approach, especially LSTM, for highly accurate real-time bandwidth utilization prediction on network links, aiding proactive congestion management.

Contribution

The paper introduces a novel application of LSTM for precise bandwidth prediction and integrates it with SDN for real-time network management.

Findings

LSTM achieves less than 3% prediction error.

ARIMA and MLP have higher errors of 40% and 20%.

The solution enables real-time network congestion detection.

Abstract

Predicting the bandwidth utilization on network links can be extremely useful for detecting congestion in order to correct them before they occur. In this paper, we present a solution to predict the bandwidth utilization between different network links with a very high accuracy. A simulated network is created to collect data related to the performance of the network links on every interface. These data are processed and expanded with feature engineering in order to create a training set. We evaluate and compare three types of machine learning algorithms, namely ARIMA (AutoRegressive Integrated Moving Average), MLP (Multi Layer Perceptron) and LSTM (Long Short-Term Memory), in order to predict the future bandwidth consumption. The LSTM outperforms ARIMA and MLP with very accurate predictions, rarely exceeding a 3\% error (40\% for ARIMA and 20\% for the MLP). We then show that the proposed solution can be used in real time with a reaction managed by a Software-Defined Networking (SDN) platform.