Convolutional LSTM models to estimate network traffic

TL;DR

This paper introduces CNN-LSTM and Conv-LSTM models to predict future network traffic, enabling dynamic routing adjustments to improve network utilization efficiency based on scheduled transfer data.

Contribution

The paper presents novel LSTM-based models specifically designed for network traffic prediction, leveraging scheduled transfer information for better network management.

Findings

Models effectively predict future network traffic.

Improved network configuration planning based on predictions.

Enhanced detection of potential overloads.

Abstract

Network utilisation efficiency can, at least in principle, often be improved by dynamically re-configuring routing policies to better distribute on-going large data transfers. Unfortunately, the information necessary to decide on an appropriate reconfiguration - details of on-going and upcoming data transfers such as their source and destination and, most importantly, their volume and duration - is usually lacking. Fortunately, the increased use of scheduled transfer services, such as FTS, makes it possible to collect the necessary information. However, the mere detection and characterisation of larger transfers is not sufficient to predict with confidence the likelihood a network link will become overloaded. In this paper we present the use of LSTM-based models (CNN-LSTM and Conv-LSTM) to effectively estimate future network traffic and so provide a solid basis for formulating a sensible network configuration plan.