Multivariate Time Series characterization and forecasting of VoIP traffic in real mobile networks

TL;DR

This paper presents a multivariate time series analysis of VoIP traffic in real mobile networks, employing various forecasting models to predict QoS/QoE descriptors and improve network resource planning.

Contribution

It introduces a formal multivariate time series framework for VoIP traffic analysis in real mobile environments, comparing multiple forecasting techniques including machine learning methods.

Findings

Vector Autoregressive models and machine learning approaches achieve accurate forecasts.

Deep and tree-based models outperform traditional statistical methods in prediction accuracy.

Analysis reveals key relationships among QoS/QoE descriptors in mobile VoIP traffic.

Abstract

Predicting the behavior of real-time traffic (e.g., VoIP) in mobility scenarios could help the operators to better plan their network infrastructures and to optimize the allocation of resources. Accordingly, in this work the authors propose a forecasting analysis of crucial QoS/QoE descriptors (some of which neglected in the technical literature) of VoIP traffic in a real mobile environment. The problem is formulated in terms of a multivariate time series analysis. Such a formalization allows to discover and model the temporal relationships among various descriptors and to forecast their behaviors for future periods. Techniques such as Vector Autoregressive models and machine learning (deep-based and tree-based) approaches are employed and compared in terms of performance and time complexity, by reframing the multivariate time series problem into a supervised learning one. Moreover, a series of auxiliary analyses (stationarity, orthogonal impulse responses, etc.) are performed to discover the analytical structure of the time series and to provide deep insights about their relationships. The whole theoretical analysis has an experimental counterpart since a set of trials across a real-world LTE-Advanced environment has been performed to collect, post-process and analyze about 600,000 voice packets, organized per flow and differentiated per codec.