SHARP-QoS: Sparsely-gated Hierarchical Adaptive Routing for joint Prediction of QoS
Suraj Kumar, Arvind Kumar, Soumi Chattopadhyay
TL;DR
SHARP-QoS tackles the challenge of joint QoS prediction under extreme data sparsity and hierarchical dependencies by learning hierarchical features in hyperbolic space and enabling adaptive, task-aware feature sharing across QoS and contextual signals. It introduces a dual HFEB with HyGCN and HHGCN to capture QoS/context hierarchies, a Sub-Network Routing framework with Cross-SNR and gated fusion to flexibly share information, and an EMA-based loss balancing strategy to mitigate negative transfer. The approach is validated across three real-world datasets with two to four QoS parameters, showing consistent, substantial improvements over both single-task and multi-task baselines, along with robustness to outliers and cold-start conditions. The results suggest SHARP-QoS offers scalable, privacy-preserving, and efficient joint QoS prediction suitable for latency-sensitive service environments.
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 Poincaré 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.