Traffic Matrix Estimation based on Denoising Diffusion Probabilistic Model
This is an incremental improvement for network traffic management, applying a known deep generative model to a specific domain problem.
The paper tackles the traffic matrix estimation problem by leveraging denoising diffusion probabilistic models for the first time, achieving superior performance in synthesis and estimation compared to state-of-the-art methods on two real-world datasets.
The traffic matrix estimation (TME) problem has been widely researched for decades of years. Recent progresses in deep generative models offer new opportunities to tackle TME problems in a more advanced way. In this paper, we leverage the powerful ability of denoising diffusion probabilistic models (DDPMs) on distribution learning, and for the first time adopt DDPM to address the TME problem. To ensure a good performance of DDPM on learning the distributions of TMs, we design a preprocessing module to reduce the dimensions of TMs while keeping the data variety of each OD flow. To improve the estimation accuracy, we parameterize the noise factors in DDPM and transform the TME problem into a gradient-descent optimization problem. Finally, we compared our method with the state-of-the-art TME methods using two real-world TM datasets, the experimental results strongly demonstrate the superiority of our method on both TM synthesis and TM estimation.