SHARP-QoS: Sparsely-gated Hierarchical Adaptive Routing for joint Prediction of QoS
Suraj Kumar, Arvind Kumar, Soumi Chattopadhyay

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…
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Taxonomy
TopicsSoftware System Performance and Reliability · Cloud Computing and Resource Management · Service-Oriented Architecture and Web Services
