Probabilistic QoS Metric Forecasting in Delay-Tolerant Networks Using Conditional Diffusion Models on Latent Dynamics
This work addresses network performance enhancement in Delay-Tolerant Networks, representing an incremental improvement over existing probabilistic forecasting methods.
This paper tackles the problem of predicting Quality of Service metrics in Delay-Tolerant Networks by formulating it as a probabilistic forecasting task on multivariate time series, and demonstrates that their approach using conditional diffusion models with latent temporal dynamics outperforms existing probabilistic time series forecasting methods.
Active QoS metric prediction, commonly employed in the maintenance and operation of DTN, could enhance network performance regarding latency, throughput, energy consumption, and dependability. Naturally formulated as a multivariate time series forecasting problem, it attracts substantial research efforts. Traditional mean regression methods for time series forecasting cannot capture the data complexity adequately, resulting in deteriorated performance in operational tasks in DTNs such as routing. This paper formulates the prediction of QoS metrics in DTN as a probabilistic forecasting problem on multivariate time series, where one could quantify the uncertainty of forecasts by characterizing the distribution of these samples. The proposed approach hires diffusion models and incorporates the latent temporal dynamics of non-stationary and multi-mode data into them. Extensive experiments demonstrate the efficacy of the proposed approach by showing that it outperforms the popular probabilistic time series forecasting methods.