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.

翻译：在延迟容忍网络（DTN）的维护与运营中，主动预测服务质量（QoS）指标可有效提升网络在延迟、吞吐量、能耗及可靠性方面的性能。该问题通常被构建为多元时间序列预测任务，并已吸引了大量研究关注。传统的均值回归方法难以充分捕捉数据复杂性，导致在DTN路由等运营任务中性能下降。本文将DTN中的QoS指标预测构建为多元时间序列的概率性预测问题，通过刻画样本分布来量化预测的不确定性。所提方法采用扩散模型，并将非平稳、多模态数据的潜在时序动态特征融入其中。大量实验表明，该方法优于当前主流的概率性时间序列预测方法，验证了其有效性。