A Novel Approach to Temporal QoS Estimation via Extended Kalman Filter-Incorporated Latent Feature Analysis

2026-06-22

AI Summary

Existing purely data-driven approaches struggle to effectively model the dynamic variations inherent in non-stationary time-series QoS data, leading to limited prediction accuracy. To address this challenge, this work proposes a bidirectional model-data-driven learning framework that integrates a model-driven temporal latent feature generator based on the extended Kalman filter to capture system dynamics, while leveraging alternating least squares to uncover intrinsic user-service characteristics. Furthermore, a density-aware parallelization mechanism is introduced to enhance computational efficiency. Evaluated on real-world QoS datasets, the proposed method significantly outperforms state-of-the-art models in both prediction accuracy and scalability, and it comes with theoretical convergence guarantees.

Abstract

Predicting temporal Quality of Service (QoS) data is critical for optimizing network services and rationalizing resource allocation in cloud computing and service-oriented systems. Existing mainstream methods have achieved promising predictive performance. However, their purely data-driven manner limits their ability to capture non-stationary temporal patterns, thereby leading to accuracy degradation when temporal QoS data exhibits fluctuations. To tackle this limitation, we propose a novel Extended Kalman Filter-Enhanced Latent Feature Analysis (EKL) model to perform efficient and accurate temporal QoS prediction from the perspective of bidirectional model-data-driven learning. Its main idea is three-fold: a) designing a model-driven feature producer to obtain the temporal latent features to capture the intricate temporal pattern following the principle of an Extended Kalman Filter; b) building a data-driven feature producer based on the alternating least squares algorithm to identify time-invariant latent features describing intrinsic user-service characteristics; c) exploiting a density-oriented parallel strategy that achieves workload balancing by sorting users in accordance with their service invocation density, which effectively elevates computational efficiency. In addition, we provide a rigorous theoretical analysis to formally prove the convergence of the proposed EKL. Experimental evaluations conducted on real-world temporal QoS datasets reveal that our proposed EKL surpasses existing state-of-the-art models with respect to both computational efficiency and prediction accuracy for missing temporal QoS data.

Problem

• Temporal QoS prediction
• Non-stationary temporal patterns
• Quality of Service
• Cloud computing
• Service-oriented systems

Innovation

• Extended Kalman Filter
• Latent Feature Analysis
• Temporal QoS Prediction
• Model-Data-Driven Learning
• Density-Oriented Parallel Strategy