Jul 2026· 2026 6th International Conference on Intelligent Communications and Computing (ICICC)· pp. 201-204· 0 citations· 15 references
Abstract
Efficient Service Function Chaining (SFC) orchestration in large-scale NFV environments is often bottlenecked by the non-linear overhead of traditional Graph Convolutional Networks (GCNs). This paper proposes GraphSAGE-DQN, a scalable algorithm that leverages inductive neighbor sampling to decouple state extraction complexity from network size, achieving linear computational complexity. By integrating GraphSAGE embeddings with a Deep Q-Network (DQN), the model optimizes deployment strategies to maximize request acceptance rates. Simulations show that GraphSAGE-DQN outperforms traditional DQN and SECA algorithms, particularly in high-load scenarios where it improves acceptance rates by $\mathbf{1. 6 4 \%}$ and $\mathbf{1 7. 0 \%}$, respectively. These results confirm the model's efficiency and scalability for dynamic SFC orchestration in large-scale networks.
As Software-Defined Networking (SDN) and Network Function Virtualization (NFV) enabled networks scale in size and complexity, monitoring and managing Service Function Chains (SFCs) under stringent latency and resource constraints becomes increasingly challenging. Although Deep Reinforcement Learning (DRL) is widely applied to SFC provisioning and Virtual Network Function (VNF) placement, enhanced network state monitoring is crucial to capture unexpected network conditions and guide DRL agents toward more adaptive decisions. In this context, Language Models (LMs) enable flexible, natural-language (NL)–based, query-driven network monitoring; however, directly processing complex multi-metric NL queries is computationally expensive and error-prone. This paper proposes an end-to-end (E2E) edge-based query translation pipeline that decomposes multi-metric NL queries into simpler single-metric sub-queries. Query decomposition is performed using a retrieval-augmented language model (RAG-LLM) and compared with a lightweight rule-based decomposition baseline. The resulting sub-queries are translated into Structured Query Language (SQL) using FLAN-T5. A cloud-only baseline, which directly translates NL queries to SQL without decomposition, is also evaluated. The results show that the rule-based edge pipeline achieves the lowest latency, reducing E2E latency by up to 78% compared to RAG-LLM and 18% compared to cloud execution under high workloads. Under increasing arrival rates for the largest workload, the rule-based edge pipeline maintains superior performance over cloud, reducing total E2E latency by 57% at $\lambda = 0.8$ . While RAG-LLM provides greater flexibility for unseen query patterns, both edge-based approaches achieve 100% NL2SQL accuracy with zero decomposition failures, outperforming the cloud-only baseline (95% accuracy).
Parisa Fard Moshiri, Xinyu Zhu, Poonam Lohan et al.· IEEE Transactions on Network...· 0 citations
This paper employs five lightweight machine learning models to predict query-specific latency, accuracy, resource usage, and response size for each model-size-quantization-device combination, and design a lightweight heuristic that approximates the BILP solution.
Reza Farahani, Zoha Azimi, Mario Colosi et al.· 0 citations
A compact reasoning model trained with verifier-based self-verification and periodically refined online via shadow updates is deployed, showing manageable, near-linear control-plane overhead as domains scale and during domain joins, and robust decision quality, including recovery after objective changes.
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
Network slicing enables heterogeneous services to share a common 5G infrastructure, but reactive slice elasticity often scales resources only after service degradation has occurred. We propose predictive hierarchical orchestration for multi-domain network slicing, combining short term workload prediction, SLA feedback, and coordinated scaling across RAN, core, and transport domains. Preliminary numerical simulations with synthetic multi-slice workloads show that, in a mixed scenario, the proposed controller reduces the SLA violation rate from 56.7% to 5.0% compared with reactive-only scaling, while reducing reaction time from 4.86 to 1.50 control intervals. These initial results suggest that predictive orchestration can mitigate delayed adaptation, at the cost of controlled overprovisioning.
Paulo Eugênio da Costa Filho, A. V. Venâncio Neto, Dario Vieira· 0 citations
Network functions virtualization (NFV) is an emerging technology that enables flexible service deployment for supporting the Beyond 5G/6G network. NFV transforms physical network devices into virtual network functions (VNF) over Edge Computing capabilities, thereby facilitating the agility of network services and reducing management costs. To effectively monitor Internet of Things (IoT) network resources, service function chaining (SFC) is used for its virtualizations to ensure the multi-service requirements are sufficiently in capability, scalability, and flexibility for computation workloads alignments. However, to satisfy the resource availability requirements and efficiency under several conditions, SFC reconfiguration methods face the challenges in meeting significant latency requirement of delay-sensitive applications while reaching the importance of energy saving on orchestration timespan. In this paper, we propose task management-aware SFC and orchestrating schemes, namely GNN-PPO. In this framework, we utilize the Graph Neural Network (GNN), which relies on the message-passing neural network (MPNN), to capture all the abstraction of physical resource nodes and link capabilities over MEC node states. In particularly, GNN is divided construction into two phrases: (1) GNN represents nodes for all the Mobile edge computing (MEC) nodes, which have a global view on resources of computation and communicational capabilities that could serve as carriers; (2) VNFs are transferred into graph networks by using feature-extraction MPNN to manage each VIM that seeks an optimal and reliable analysis of traffic fluctuations. Lastly, Deep Reinforcement Learning (DRL) is used to embrace the network determination in policy strategy, which utilizes a Proximal Policy Gradient (PPO). On the other hand, we propose a novel network architecture based on PPO to perform the design for the optimization of resource utilization and facilitate energy consumption on MEC servers under diverse setting scenarios, which enables continuous policy enforcement for our system. With the experimental results, we compare our proposed solution with reference schemes in terms of rewards with learning rate and batch size, average request acceptance, SFC success, packet delivery, throughput, and resource utilization ratio that confirm the scheme’s scalability and practical suitability for IoT network deployment.
Seyha Ros, Taikuong Iv, Intae Ryoo et al.· Italian National Conference...· 0 citations