Anomaly Resilient Temporal QoS Prediction using Hypergraph Convoluted Transformer Network

TL;DR

This paper presents HCTN, a novel deep learning framework combining hypergraph structures and transformer networks to improve real-time QoS prediction accuracy amid data sparsity, outliers, and diverse feature integration.

Contribution

The paper introduces HCTN, a new end-to-end deep architecture that effectively captures high-order correlations and handles data issues in temporal QoS prediction.

Findings

Achieved state-of-the-art results on WSDREAM-2 datasets.

Effectively mitigated data sparsity and outlier effects.

Improved prediction accuracy for QoS metrics.

Abstract

Quality-of-Service (QoS) prediction is a critical task in the service lifecycle, enabling precise and adaptive service recommendations by anticipating performance variations over time in response to evolving network uncertainties and user preferences. However, contemporary QoS prediction methods frequently encounter data sparsity and cold-start issues, which hinder accurate QoS predictions and limit the ability to capture diverse user preferences. Additionally, these methods often assume QoS data reliability, neglecting potential credibility issues such as outliers and the presence of greysheep users and services with atypical invocation patterns. Furthermore, traditional approaches fail to leverage diverse features, including domain-specific knowledge and complex higher-order patterns, essential for accurate QoS predictions. In this paper, we introduce a real-time, trust-aware framework for temporal QoS prediction to address the aforementioned challenges, featuring an end-to-end deep architecture called the Hypergraph Convoluted Transformer Network (HCTN). HCTN combines a hypergraph structure with graph convolution over hyper-edges to effectively address high-sparsity issues by capturing complex, high-order correlations. Complementing this, the transformer network utilizes multi-head attention along with parallel 1D convolutional layers and fully connected dense blocks to capture both fine-grained and coarse-grained dynamic patterns. Additionally, our approach includes a sparsity-resilient solution for detecting greysheep users and services, incorporating their unique characteristics to improve prediction accuracy. Trained with a robust loss function resistant to outliers, HCTN demonstrated state-of-the-art performance on the large-scale WSDREAM-2 datasets for response time and throughput.