Real-Time Network Traffic Forecasting with Missing Data: A Generative Model Approach

TL;DR

This paper introduces a generative model-based method for real-time network traffic forecasting that effectively handles missing data by modeling the task as tensor completion and leveraging a pre-trained generative model for efficient, accurate predictions.

Contribution

It presents a novel approach combining tensor completion with generative models to enable fast and accurate network traffic forecasting with incomplete data.

Findings

Achieves forecasting within 100 ms

Maintains MAE below 0.002 on Abilene dataset

Effectively handles missing data in real-world scenarios

Abstract

Real-time network traffic forecasting is crucial for network management and early resource allocation. Existing network traffic forecasting approaches operate under the assumption that the network traffic data is fully observed. However, in practical scenarios, the collected data are often incomplete due to various human and natural factors. In this paper, we propose a generative model approach for real-time network traffic forecasting with missing data. Firstly, we model the network traffic forecasting task as a tensor completion problem. Secondly, we incorporate a pre-trained generative model to achieve the low-rank structure commonly associated with tensor completion. The generative model effectively captures the intrinsic low-rank structure of network traffic data during pre-training and enables the mapping from a compact latent representation to the tensor space. Thirdly, rather than directly optimizing the high-dimensional tensor, we optimize its latent representation, which simplifies the optimization process and enables real-time forecasting. We also establish a theoretical recovery guarantee that quantifies the error bound of the proposed approach. Experiments on real-world datasets demonstrate that our approach achieves accurate network traffic forecasting within 100 ms, with a mean absolute error (MAE) below 0.002, as validated on the Abilene dataset.