A Novel Approach to Temporal QoS Estimation via Extended Kalman Filter-Incorporated Latent Feature Analysis
arXiv:2606.23010v2 Announce Type: replace 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 Analy
The increasing complexity and scale of cloud computing and service-oriented systems necessitate more sophisticated methods for resource management and QoS prediction, driving innovation in this area.
This research directly impacts the efficiency and reliability of digital infrastructure by improving the prediction of network service quality, which is crucial for AI and data-intensive applications.
The proposed method offers a more robust approach to predicting fluctuating temporal Quality of Service data, potentially leading to more stable and optimized network services compared to purely data-driven methods.
- · Cloud Service Providers
- · AI/ML Infrastructure Developers
- · Network Operators
- · Large-scale software platforms
- · Legacy QoS prediction systems
- · Businesses reliant on static resource allocation models
More accurate QoS prediction enhances resource allocation and network optimization in cloud environments.
Improved network efficiency can reduce operational costs and energy consumption for data centers.
Enhanced foundational network stability could indirectly support the scaling and reliability of distributed AI systems and AI agents.
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Read at arXiv cs.LG