The traffic scheduling mechanism in Time-Sensitive Networking (TSN) is the key to guaranteeing the deterministic transmission of traffic. However, when time-sensitive traffic and non-time-sensitive traffic are transmitted together, traffic scheduling conflicts are easy to occur in TSN. As a result, the deterministic transmission of time-sensitive traffic will be disrupted, and non-time-sensitive traffic may be preempted for a long time. To optimize the performance of multi-type hybrid traffic scheduling in TSN, we firstly establish a collaborative scheduling framework that incorporates Time Aware Shaping (TAS) and Cyclic Queuing and Forwarding (CQF) mechanisms. We then design a traffic shaping method in this framework based on Least Laxity First (LLF), which considers traffic characteristics to dynamically arrange the time slot injection sequence for different types of traffic. Finally, the traffic schedulability is evaluated based on the scheduling constraints of different types of traffic. Compared with the existing scheduling strategies, the proposed hybrid traffic scheduling strategy can schedule more non-time-sensitive traffic and achieve better delay performance of rate-constrained traffic in different hybrid traffic scenarios. When the number of flows is 100, the time slot injection ratio is increased by 24.3% compared with the LLF_TAS method.

Network Function Virtualization (NFV) improves the flexibility and scalability of network services and reduces operating costs. As the core of research on network virtualization, Virtual Network Embedding (VNE) aims to effectively deploy service requests on physical network components and allocate underlying physical resources. However, network services can be complex and diverse, which makes it difficult for existing embedding methods to effectively utilize the graph structure of services, tackle the complexity of dynamic networks, and provide effective embedding solutions. To this end, we propose GPRL, an online VNE method based on graph pointer network and Deep Reinforcement Learning (DRL). By combining the graph neural network and pointer network, we design a novel graph pointer network as the DRL agent. It employs the graph attention network to encode graph feature data and decodes to output the embedding policy via the pointer network architecture. Furthermore, the Proximal Policy Optimization (PPO) algorithm is used to effectively train the designed agent. The effectiveness and superiority of GPRL are verified by simulation experiments, and GPRL is shown to perform better than existing embedding methods.

Cloud-network convergence represents a pivotal architectural evolution in modern communication networks, enabling unified orchestration of computing, storage, and network resources. Within such environment, trust evaluation plays an essential role in ensuring secure resource scheduling and reliable service deployment. While graph representation learning (GRL) has emerged as a powerful paradigm for trust evaluation by capturing complex relational patterns among entities, existing approaches still face three fundamental challenges: resource heterogeneity, data sparsity, and insufficient interpretability. To address these challenges, we propose LAGTrust, a Language-Augmented Graph representation learning framework for trust evaluation in cloud-network convergence. LAGTrust integrates large language models (LLMs) with graph neural networks through three synergistic components: (i) a Heterogeneous Feature Representation module that leverages LLMs to project multi-dimensional resource attributes into a unified semantic space while employing spatio-temporal graph neural networks to capture structural and behavioral dynamics; (ii) an Adaptive Feature Enhancement module that performs hierarchical LLM-driven augmentation for semantic completion, relationship inference, and profile enrichment; and (iii) a Semantic Explanation Generation module that synthesizes multi-source evidence through causal reasoning to produce interpretable trust assessments. Extensive experiments on real-world and synthetic topologies demonstrate that LAGTrust consistently outperforms baseline methods across multiple evaluation metrics, particularly under cold-start scenarios, validating its superior accuracy, robustness, and interpretability for trust evaluation in cloud-network convergence environments.