Real-Time Network Latency Estimation With Pretrained Generative Models.

Abstract

Network latency estimation is critical for network performance monitoring and management. However, with the escalating demand for real-time performance monitoring and rapid network adjustments in contemporary networks, existing latency estimation methodologies fall short of meeting the need for instantaneous estimation. In this article, we propose a pretrained generative model-based scheme (PGM) for real-time network latency estimation. PGM operates in two stages. First, we employ a pretrained generative model to relax the low-rank constraint typically associated with latency matrix completion (MC). The pretrained generative model well learns the low-rank characteristics of latency matrices in the pretraining stage and can map a condensed latent representation to the matrix space. Second, instead of directly optimizing the matrix, we turn to optimizing the latent representation. Leveraging the low-rank structure achieved by the pretrained generative model simplifies our optimization process, enabling real-time estimation. We also provide a theoretical recovery guarantee to reveal the error bound of PGM. Experimental results on real-world datasets show that the proposed scheme can achieve accurate latency estimation within 50 ms while maintaining the relative squared error (RSE) of estimation at no more than 0.11 (as evidenced using the PlanetLab dataset).