Modeling Method and Implementation Mechanism for QoS Prediction Based on Multi-Source Data Fusion

Research on Modeling Methods for Temporal Neural Networks in QoS Prediction

: 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.

Cloud Resource Workload Forecasting Method Based on the MST-iTransformer Model

With the widespread adoption of cloud computing technology, modern cloud platforms have become increasingly complex and dynamic, posing significant challenges for efficient resource management. Accurate forecasting of cloud resource loads has therefore become essential for improving service quality, optimizing resource utilization, and reducing operational costs. To address the intrinsic characteristics of cloud load time series, including nonlinear fluctuations, multi-scale temporal dependencies, and redundant high-dimensional features, this paper proposes the MST-iTransformer model, which integrates multi-scale temporal encoding, sparse attention, and adaptive feature selection mechanisms. Specifically, a multi-scale temporal encoding module is developed to capture and fuse temporal dependencies across multiple periodic scales. Furthermore, an adaptive feature selection module is introduced to dynamically assign importance weights to resource features, enhancing informative variables while suppressing redundant ones. Meanwhile, a sparse attention mechanism is incorporated to reduce computational overhead while maintaining forecasting accuracy. The proposed model is evaluated on the Alibaba Cluster Trace dataset. Experimental results demonstrate that MST-iTransformer achieves MSE, RMSE, and MAE values of 0.5559, 0.7456, and 0.4968, respectively. Compared with the original iTransformer, the proposed model achieves simultaneous reductions in prediction errors and inference latency, validating the effectiveness of the multi-scale temporal encoding, sparse attention mechanism, and adaptive feature selection modules in improving forecasting accuracy and computational efficiency. These improvements provide reliable prediction support for resource scheduling and elastic scaling in cloud data centers.

Cloud-Network Collaborative Architecture Design and Performance Optimization Based on SRv6 and Intent-Driven Approach

This study proposes an intent-driven cloud-network collaborative architecture based on Segment Routing over IPv6 (SRv6) that integrates intent parsing, intelligent control, and SRv6 forwarding mechanisms to achieve automated service-to-policy mapping and adaptive path orchestration.

QoS-Aware Intelligent Model Selection for Autonomous Vehicles under Dynamic Network and Resource Conditions

Autonomous-vehicle perception systems increasingly operate across heterogeneous compute and communication environments, where the most accurate model is not necessarily the model that provides the best end-to-end service quality. A high-capacity perception model may improve recognition quality while increasing inference time, CPU consumption, memory demand, and sensitivity to network conditions. This paper proposes a Quality-of-Service (QoS)-Aware Intelligent Model Selection framework that predicts the expected QoS of candidate perception models from model, network, and hardware context and dynamically selects a feasible model according to application priorities. The framework extends a network-aware autonomous-vehicle benchmarking foundation with a QoS prediction layer, multi-objective utility function, Pareto filtering, and a hysteresis-based switching controller. Three tabular prediction methods—linear regression, random forest, and gradient boosting—are compared. A synthetic dataset of 1,800 observations is generated from six candidate model profiles, five network conditions, three hardware conditions, and repeated measurements. The synthetic evaluation indicates that nonlinear predictors outperform the linear baseline and that QoS-aware selection can reduce mean response time by approximately 31.8–42.1% relative to an accuracy-only baseline in the modeled scenarios. Pareto and ablation analyses further show that latency and resource terms materially influence the selected model. Because no real AV testbed experiment has yet been conducted, these numerical results are explicitly treated as synthetic validation rather than empirical evidence. The paper therefore provides a reproducible research design and a publication-oriented methodology whose final claims should be revalidated with measured AV executions.

JointScaler: A Hierarchical Multi-Indicator Distribution Forecasting Approach for Uncertainty-Aware Joint Scaling in Cloud Services

A Novel Iterative Machine Learning-Driven Framework for Reliable and Adaptive Cloud Data Migration

AMF-CloudForge is presented, a unified machine learning-driven framework that integrates migration state analysis, intelligent scheduling, consistency preservation, and real-time adaptive management into a single end-to-end architecture and transforms cloud data migration from a static, tool-centric process into a reliable, adaptive, and continuously optimized cloud service.