Accurately predicting end-to-end network latency is essential for enabling reliable task offloading in real-time edge computing applications. This paper introduces a lightweight latency prediction scheme based on rational modelling that uses features such as frame size, arrival rate, and link utilization, eliminating the need for intrusive active probing. The model achieves state-of-the-art prediction accuracy through extensive experiments and 5-fold cross-validation (MAE = 0.0115, R$^2$ = 0.9847) with competitive inference time, offering a substantial trade-off between precision and efficiency compared to traditional regressors and neural networks.

翻译：准确预测端到端网络延迟对于实现实时边缘计算应用中可靠的任务卸载至关重要。本文提出一种基于有理建模的轻量级延迟预测方案，该方案利用帧大小、到达率和链路利用率等特征，无需侵入式主动探测。通过大量实验和五折交叉验证（MAE = 0.0115，R$^2$ = 0.9847），该模型实现了最先进的预测精度，并具有竞争力的推理时间，相较于传统回归器和神经网络，在精度与效率之间提供了显著的权衡。