SHARP-QoS: Sparsely-gated Hierarchical Adaptive Routing for joint Prediction of QoS

TL;DR

SHARP-QoS is a novel hierarchical adaptive routing model that improves joint QoS prediction accuracy by addressing data sparsity, negative transfer, and feature sharing challenges through hyperbolic convolution, adaptive sharing, and EMA-based loss balancing.

Contribution

The paper introduces SHARP-QoS, a unified framework with hyperbolic convolution, adaptive feature sharing, and EMA-based loss balancing for improved joint QoS prediction.

Findings

Outperforms existing single- and multi-task models on multiple datasets.

Effectively handles data sparsity, outliers, and cold-start issues.

Maintains moderate computational overhead while improving accuracy.

Abstract

Dependable service-oriented computing relies on multiple Quality of Service (QoS) parameters that are essential to assess service optimality. However, real-world QoS data are extremely sparse, noisy, and shaped by hierarchical dependencies arising from QoS interactions, and geographical and network-level factors, making accurate QoS prediction challenging. Existing methods often predict each QoS parameter separately, requiring multiple similar models, which increases computational cost and leads to poor generalization. Although recent joint QoS prediction studies have explored shared architectures, they suffer from negative transfer due to loss-scaling caused by inconsistent numerical ranges across QoS parameters and further struggle with inadequate representation learning, resulting in degraded accuracy. This paper presents an unified strategy for joint QoS prediction, called SHARP-QoS, that addresses these issues using three components. First, we introduce a dual mechanism to extract the hierarchical features from both QoS and contextual structures via hyperbolic convolution formulated in the Poincar\'e ball. Second, we propose an adaptive feature-sharing mechanism that allows feature exchange across informative QoS and contextual signals. A gated feature fusion module is employed to support dynamic feature selection among structural and shared representations. Third, we design an EMA-based loss balancing strategy that allows stable joint optimization, thereby mitigating the negative transfer. Evaluations on three datasets with two, three, and four QoS parameters demonstrate that SHARP-QoS outperforms both single- and multi-task baselines. Extensive study shows that our model effectively addresses major challenges, including sparsity, robustness to outliers, and cold-start, while maintaining moderate computational overhead, underscoring its capability for reliable joint QoS prediction.