Research on Modeling Methods for Temporal Neural Networks in QoS Prediction
TL;DR
Experimental results demonstrate that the proposed framework outperforms existing baseline methods in both response time and throughput prediction tasks, and the multi-task learning strategy enhances prediction accuracy while significantly improving computational efficiency.
Abstract
: The dynamic variations in Quality of Service (QoS) within cloud computing environments pose significant challenges for accurate prediction. Addressing the issues of inadequate multi-source feature modeling and low prediction efficiency in temporal QoS prediction, this study integrates Long Short-Term Memory (LSTM) networks, Graph Attention Network (GAT), and attention mechanisms to develop a hybrid neural network modeling framework specifically designed for QoS prediction. The framework sequentially performs three key tasks: constructing temporal graph structures, integrating heterogeneous multi-source features, and enabling multi-task collaborative prediction, thereby effectively capturing the dynamic patterns of user preferences, network states, and service loads during service invocation. Experimental results demonstrate that the proposed framework outperforms existing baseline methods in both response time and throughput prediction tasks, and the multi-task learning strategy enhances prediction accuracy while significantly improving computational efficiency.