QoSBERT: An Uncertainty-Aware Approach based on Pre-trained Language Models for Service Quality Prediction

TL;DR

QoSBERT introduces a semantic regression framework using pre-trained language models and uncertainty estimation for more accurate and trustworthy cloud service quality prediction, outperforming existing models.

Contribution

It is the first to reformulate QoS prediction as a semantic regression task with integrated uncertainty estimation using pre-trained language models.

Findings

Achieves 11.7% reduction in MAE for response time prediction.

Provides well-calibrated confidence intervals for predictions.

Improves robustness in low-resource settings through uncertainty-based sample selection.

Abstract

Accurate prediction of Quality of Service (QoS) metrics is fundamental for selecting and managing cloud based services. Traditional QoS models rely on manual feature engineering and yield only point estimates, offering no insight into the confidence of their predictions. In this paper, we propose QoSBERT, the first framework that reformulates QoS prediction as a semantic regression task based on pre trained language models. Unlike previous approaches relying on sparse numerical features, QoSBERT automatically encodes user service metadata into natural language descriptions, enabling deep semantic understanding. Furthermore, we integrate a Monte Carlo Dropout based uncertainty estimation module, allowing for trustworthy and risk-aware service quality prediction, which is crucial yet underexplored in existing QoS models. QoSBERT applies attentive pooling over contextualized embeddings and a lightweight multilayer perceptron regressor, fine tuned jointly to minimize absolute error. We further exploit the resulting uncertainty estimates to select high quality training samples, improving robustness in low resource settings. On standard QoS benchmark datasets, QoSBERT achieves an average reduction of 11.7% in MAE and 6.7% in RMSE for response time prediction, and 6.9% in MAE for throughput prediction compared to the strongest baselines, while providing well calibrated confidence intervals for robust and trustworthy service quality estimation. Our approach not only advances the accuracy of service quality prediction but also delivers reliable uncertainty quantification, paving the way for more trustworthy, data driven service selection and optimization.