Dual Latent State Learning: Exploiting Regional Network Similarities for QoS Prediction

TL;DR

This paper introduces R2SL, a deep learning framework that leverages regional network similarities to improve QoS prediction accuracy, addressing data sparsity and label imbalance issues.

Contribution

R2SL uniquely captures regional network behaviors through city and AS latent states, integrating regional data and an enhanced loss function for superior QoS prediction.

Findings

R2SL outperforms state-of-the-art methods on real-world datasets.

The regional latent states improve prediction accuracy.

The enhanced Huber loss effectively mitigates label imbalance.

Abstract

Individual objects, whether users or services, within a specific region often exhibit similar network states due to their shared origin from the same city or autonomous system (AS). Despite this regional network similarity, many existing techniques overlook its potential, resulting in subpar performance arising from challenges such as data sparsity and label imbalance. In this paper, we introduce the regional-based dual latent state learning network(R2SL), a novel deep learning framework designed to overcome the pitfalls of traditional individual object-based prediction techniques in Quality of Service (QoS) prediction. Unlike its predecessors, R2SL captures the nuances of regional network behavior by deriving two distinct regional network latent states: the city-network latent state and the AS-network latent state. These states are constructed utilizing aggregated data from common regions rather than individual object data. Furthermore, R2SL adopts an enhanced Huber loss function that adjusts its linear loss component, providing a remedy for prevalent label imbalance issues. To cap off the prediction process, a multi-scale perception network is leveraged to interpret the integrated feature map, a fusion of regional network latent features and other pertinent information, ultimately accomplishing the QoS prediction. Through rigorous testing on real-world QoS datasets, R2SL demonstrates superior performance compared to prevailing state-of-the-art methods. Our R2SL approach ushers in an innovative avenue for precise QoS predictions by fully harnessing the regional network similarities inherent in objects.