2026· IEEE Transactions on Network and Service Management· Vol 23, pp. 6569-6580· 0 citations· 26 references
Abstract
Routing optimization in cloud-edge collaborative networks faces a fundamental conflict between global strategic planning and local real-time responsiveness, further complicated by structural heterogeneity and stochastic traffic patterns. Traditional protocols lack adaptivity, while existing Deep Reinforcement Learning (DRL) approaches based on Graph Neural Networks (GNN) struggle with limited receptive fields and over-smoothing issues in large-scale topologies. In this paper, we propose HAT-Route, a Transformer-driven hierarchical routing framework supported by the Network Digital Twin (NDT). Our contributions are threefold: 1) We establish a cloud-edge collaborative architecture operating under the Centralized Training and Decentralized Execution paradigm. This architecture balances the trade-off between global optimization and real-time inference. 2) We introduce FlowFormer, a Spatiotemporal Transformer for the NDT. FlowFormer integrates a novel Edge-Conditioned Spatial Attention (EC-SAT) mechanism to capture physical link constraints and distinguish between congestion and Head-of-Line (HOL) blocking. 3) We design HAT-Route, a hierarchical DRL agent that utilizes Graph Transformers for global policy learning in the cloud, coupled with knowledge distillation to deploy lightweight policies at the network edge. Extensive experiments demonstrate that our framework outperforms traditional protocols and GNN-based baselines in terms of QoS optimization, training stability, scalability, and generalization capability on large-scale network topologies.
This work proposes Double-Channel Graph Attention (DCGA), an end-to-end reinforcement learning framework that isolates network reachability and demand-service logic into separate graph channels and constructs valid routes using a simulator-coupled, constraint-informed decoder.
The emergence of 5G and 6G advanced ecosystems demands highly adaptive resource management to orchestrate the specialised requirements of eMBB, URLLC, and mMTC network slices. In dense multi-cell environments, capturing complex spatial interdependencies and mitigating dynamic interference is paramount for maintaining Quality of Service (QoS). This paper introduces a robust GNN-DQN framework designed for Rate Splitting Multiple Access (RSMA) based networks. By representing the network topology as a graph, the framework leverages Graph Neural Networks (GNNs) to extract highdimensional spatial features and model inter-cell interference patterns. These insights enable a Deep Q-Network (DQN) agent to perform intelligent resource partitioning and dynamic power splitting of the RSMA common stream. Experimental results demonstrate that the proposed GNN-DQN framework achieves a connectivity success ratio exceeding 90% across all slices, representing an average improvement of over 60% compared to non-graph-based reinforcement learning and supervised baselines. Notably, the framework demonstrates exceptional spectral efficiency, maintaining near-total connectivity while utilising less than 10% of the normalised system bandwidth, a 4× reduction in resource overhead compared to traditional methods. Furthermore, the GNN-driven architecture ensures stable convergence during training, yielding a 1.6× higher system reward score. Our findings validate GNN-DQN as a high-performance, scalable, and resource-efficient paradigm for intelligent orchestration in 5G and 6G networks.
Aya Kh. Ahmed, Nadia Al-Aboody, Hamed S. Al-Raweshidy· International Conference on...· 0 citations
Space–Air–Ground Integrated Networks (SAGINs) have been envisioned to support next-generation communication networks.Due to their heterogeneity and dynamic link characteristics, routing in SAGIN is challenging. Existing shortest-path routing mechanisms do not adapt well to the varying bandwidth and latency, leading to poor quality of service (QoS). In this paper, we propose a deep reinforcement learning (DRL)-based adaptive routing scheme for maximizing throughput and minimizing end-to-end delay jointly in SAGIN. In the proposed model, an agent learns the policy of choosing the suitable path by interacting with the network environment and obtaining rewards. The network is modeled as a weighted graph with delay and bandwidth constraints. We compare our model with a traditional delay minimization baseline over multiple independent runs. Experimental results show that our DRL approach achieves a 6.51% improvement in average throughput and a 29.90% reduction in end-to-end delay compared to the baseline strategy. Statistical analysis confirms the robustness of the delay reduction, highlighting the effectiveness of reinforcement learning in dynamic HetNets. This indicates that adaptive policy learning enables better congestion avoidance and more efficient resource utilization. Overall, the proposed DRL-based routing framework offers a scalable and intelligent solution for optimizing performance in complex SAGIN architectures, with promising potential for next generation integrated communication systems.
Mixture-of-Experts (MoE) models have become a dominant architecture for large-scale AI services, yet deploying them over geo-distributed heterogeneous edge servers remains challenging. When the Top-k activated experts of a token are spread across multiple servers, the optimal routing depends jointly on cross-server link bandwidth, heterogeneous GPU computing capability, GPU-CPU expert loading delay, instantaneous queueing backlog, and replica-level quantization quality loss. Existing distributed inference and MoE serving methods address these factors separately and do not provide a unified framework for online multi-server collaborative routing. In this paper, we propose HetRoute, a heterogeneous-cost-aware collaborative routing framework for distributed edge MoE inference. HetRoute introduces a unified per-assignment cost model that explicitly captures four cost components: cross-server transmission, GPU-CPU offloading, GPU computation with queueing, and quantization-induced quality penalty. Guided by this model, the offline stage determines expert server placement, GPU-CPU residency, and replica precision through a routing-cost-coupled deployment algorithm, while the online stage routes the Top-k activated expert set as a whole by minimizing the bottleneck layer cost via exact enumeration or beam search. Theoretical analysis establishes fallback feasibility, a bound on the number of participating servers, per-layer optimality for small candidate domains, and online computational complexity. Trace-driven evaluation on three MoE models over a heterogeneous 10-server edge testbed shows that HetRoute reduces average inference latency by up to 59.0% and P99 latency by up to 58.0%, cuts cross-server traffic by up to 72.1%, and achieves 2.13x throughput improvement compared with representative baselines, while keeping quality degradation within the configured budget.
Deploying Large Language Models (LLMs) over the edge-cloud continuum faces severe stability challenges due to the conflict between stochastic network topology and complex workflow dependencies. Existing schedulers, relying either on computationally prohibitive Graph Neural Networks (GNNs) or topology-agnostic heuristics, fail to reconcile this tension. To bridge these gaps, we propose STEM, a service-level and topology-aware orchestration framework that formulates distributed LLM serving as a workflow-aware routing problem over a monitored service overlay, in which heterogeneous service instances act as specialized experts. At the core of STEM lies the STAR-PPO algorithm, utilizing a lightweight graph-free perception mechanism. By leveraging Squeeze-and-Excitation attention, it extracts critical bottleneck features from raw telemetry with linear complexity, bypassing the scalability limits of message-passing paradigms. To further achieve Pareto-efficient trade-offs, we develop a Dynamic Weight Adaptation (DWA) mechanism that autonomously recalibrates optimization preferences based on entropy-regularized metric drift. Extensive experiments on real-world datasets spanning 2,000 nodes demonstrate that our framework significantly outperforms state-of-the-art baselines. Specifically, STAR-PPO reduces network transmission costs by 96.8% and improves comprehensive inference efficiency by 24.4%, while sustaining robust zero-shot generalization across regions, with average latency within $1.09\times $ of a target-domain-retrained reference under a strict cross-region protocol. Code and data are available at https://github.com/gymorsiback/STARPPO
Yan Gao, Shaoyuan Huang, Yonghui Ye et al.· IEEE Transactions on Cogniti...· 0 citations
Low Earth Orbit (LEO) satellite networks face challenging routing conditions due to dynamic topology evolution and uneven spatio-temporal traffic distributions. To address the difficulty of jointly supporting network-wide path adaptation and localized congestion mitigation, this paper proposes STG-SR, a cohesive load-balancing routing framework for softwaredefined satellite networks. Specifically, STG-SR combines a global spatio-temporal graph with deep reinforcement learning for basic path provisioning, while employing a local spatio-temporal graph for forwarding-table reconstruction and congestion-aware multipath traffic splitting. Simulation results under low-density and high-density spatio-temporal traffic patterns show that STG-SR achieves lower delay, reduces the congestion node ratio, and improves traffic distribution compared with Dijkstra, DBPR, and CGR, demonstrating the effectiveness of coordinated global-local routing in dynamic LEO satellite networks.
Wenting Wei, Liying Fu, Xiaoming Yuan et al.· 2026 6th International Confe...· 0 citations