TransMUSE: Transferable Traffic Prediction in MUlti-Service EdgeNetworks

TL;DR

TransMUSE is a novel deep learning framework that enables transfer learning for multi-service traffic prediction across edge network regions, significantly reducing measurement overhead while maintaining high accuracy.

Contribution

The paper introduces TransMUSE, a framework that clusters services and edge nodes, and employs a Transformer-based model that can be transferred within cohorts, reducing the need for region-specific training.

Findings

TransMUSE maintains high prediction accuracy with minimal performance degradation.

TMTPN outperforms state-of-the-art models with up to 43.21% lower MAE.

Model transfer within cohorts effectively reduces measurement overhead.

Abstract

The Covid-19 pandemic has forced the workforce to switch to working from home, which has put significant burdens on the management of broadband networks and called for intelligent service-by-service resource optimization at the network edge. In this context, network traffic prediction is crucial for operators to provide reliable connectivity across large geographic regions. Although recent advances in neural network design have demonstrated potential to effectively tackle forecasting, in this work we reveal based on real-world measurements that network traffic across different regions differs widely. As a result, models trained on historical traffic data observed in one region can hardly serve in making accurate predictions in other areas. Training bespoke models for different regions is tempting, but that approach bears significant measurement overhead, is computationally expensive, and does not scale. Therefore, in this paper we propose TransMUSE, a novel deep learning framework that clusters similar services, groups edge-nodes into cohorts by traffic feature similarity, and employs a Transformer-based Multi-service Traffic Prediction Network (TMTPN), which can be directly transferred within a cohort without any customization. We demonstrate that TransMUSE exhibits imperceptible performance degradation in terms of mean absolute error (MAE) when forecasting traffic, compared with settings where a model is trained for each individual edge node. Moreover, our proposed TMTPN architecture outperforms the state-of-the-art, achieving up to 43.21% lower MAE in the multi-service traffic prediction task. To the best of our knowledge, this is the first work that jointly employs model transfer and multi-service traffic prediction to reduce measurement overhead, while providing fine-grained accurate demand forecasts for edge services provisioning.