Aug 2026· Applied AI Letters· Vol 7· 0 citations· 17 references
TL;DR
Results affirm that the combination of structural learning, adaptive decision‐making, and automated evaluation on a cloud platform offers a realisable, scalable route to intelligent, autonomous, programmable network management that can be used in next‐generation communication infrastructures.
Abstract
Software‐Defined Networking (SDN) and Network Function Virtualisation (NFV) offer the benefit of dynamic control and flexible resources that can be managed with ease. However, the growing complexity of network topologies and heterogeneous traffic patterns, combined with high‐quality‐of‐service (QoS) demands, requires innovative routing schemes and existing AI‐based optimisation procedures. Most existing methods model network structures using Graph Neural Networks (GNNs) or implement Reinforcement Learning (RL) to route traffic adaptively, but rarely combine the two in a single, more representative framework. This paper proposes a hybrid GNN‐RL system coupled with a cloud‐based automated experimentation system to optimise topology‐aware, multi‐objective, and scalable programs in the network. Graph representations were created based on network flows of two heterogeneous datasets, namely NetBench and SDNFLow and a synchronised hybrid model. A normalised, multi‐objective reward function that includes throughput, latency, packet loss, and a congestion penalty was proposed to address the reward imbalance, a frequent issue in RL routing. The RL component is trained and evaluated within the abstracted simulated environment and policy‐derived QoS metrics are generated by routing actions. The controlled environment enables reproducible evaluation of the hybrid‐GNN framework. Experimental analysis indicates that the optimised framework is more effective at QoS than standalone RL and GNN baselines. Latency was reduced by more than 70.7 ms in the original experiments to 17.7 ms and further to 9.6 ms with reward optimisation. Packet loss dropped by 6.3% to 1.49%, and throughput remained steady at 151–231 Mbps, improving over the baseline (85–88 Mbps). The coordinated training scheme enhanced convergence rate and routing stability in dynamic traffic environments. These results affirm that the combination of structural learning, adaptive decision‐making, and automated evaluation on a cloud platform offers a realisable, scalable route to intelligent, autonomous, programmable network management that can be used in next‐generation communication infrastructures.
Adaptive routing techniques that go beyond the constraints of conventional algorithms are required due to the extraordinary increase in network traffic. The advancements in deep reinforcement learning (DRL) techniques enforce the dynamic learning of the paths in network. This paper presents a hybrid DRL based Graph Neural Network combined with Soft Actor Critic(GNN-SAC) method in a dynamic Software Defined Network(SDN) environment using M/M/1 queuing model. Its performance is compared with other four DRL techniques-Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC). The experimental results demonstrate that the hybrid GNN-SAC achieves the best path cost of 4, highest throughput of 25.18 Mbps, and lowest packet loss of 6.02\%, whereas SAC achieves the lowest delay of 14.74 ms and jitter of 1.16 ms, however, A2C achieves the fastest training time of $0.23 s$ and best link utilization of 45.58\%. This platform also provides real-time animated packet routing visualization with live failure simulation capabilities.
Ramani Kasarapu, N. K. Kumar, A.G.Srideep Reddy et al.· International Conference on...· 0 citations
A QoE-aware framework for Multi-Access Edge Computing-enabled Open Radio Access Network (O-RAN) architectures, combining a graph attention network (GAT) encoder, distributed multi-agent DRL, and privacy-preserving FL, while transitioning control from Quality of Service (QoS) to QoE metrics is proposed.
Manoj Prasad Kunasegran, Wai Leong Pang, S. K. Phang· IEEE Access· 0 citations
This review analyses topology-aware learning-based routing for STINs, concentrating on Graph Neural Networks (GNNs) and hybrid GNN–Reinforcement Learning (GNN–RL) frameworks.
Eyeneka J. Ntuen, A. Obot, K. Udofia et al.· International journal of re...· 0 citations
This paper presents a comprehensive framework for artificial intelligence (AI)-enabled autonomous network slicing optimization in 6G systems and investigates the application of advanced machine learning paradigms specifically deep reinforcement learning, federated learning, and generative AI to orchestrate dynamic resource provisioning, cross-slice isolation, and proactive SLA (Service Level Agreement) enforcement.
N. P J, Jeeva Jothi· International Journal of Com...· 0 citations
This work proposes EAGLE, a Multi-Agent Reinforcement Learning (MARL) system that autonomously orchestrates the scaling of network resources to meet flow demands and reduce power consumption, and shows that the trained model can “zero-shot generalize” to unseen network topologies that share structural or statistical similarity with the training domain, hence reducing training time and associated energy costs.
Doriana Monaco, Alessio Sacco, Flavio Esposito et al.· IEEE Transactions on Network...· 0 citations
The integration of communication networks into smart grids introduces stringent requirements for reliability, low latency,
scalability, and energy efficiency. Existing routing protocols — the Routing Protocol for Low-Power and Lossy Networks
(RPL) and Greedy Perimeter Stateless Routing (GPSR) exhibit complementary strengths and weaknesses across varying
network conditions. This paper proposes an Adaptive Hybrid Routing Framework (AHRF) that integrates RPL and GPSR
under a machine learning (ML)-driven decision engine. The system dynamically selects the most suitable protocol
based on real-time network features including link quality, node degree, residual energy, queue occupancy, and traffic
load. We present rigorous mathematical models of both protocols, formulate a composite utility function capturing
trade-offs among packet delivery ratio (PDR), end-to-end delay, throughput, and energy consumption, and integrate a
Random Forest classifier for adaptive protocol selection. The framework is validated through a custom discrete-event
packet-level simulator implementing log-distance path loss with shadowing over a 500×500 m wireless mesh with up
to 200 randomly deployed nodes across four operational scenarios. Results demonstrate that the proposed Hybrid-ML
framework achieves PDR improvements of up to 8.6% over standalone RPL in the density scenario and up to 110.7%
over GPSR under node failure conditions, while achieving 27.4% lower energy consumption per packet than GPSR in
dense deployments. The Random Forest classifier achieves 95.6% cross-validation accuracy. Feature importance analysis
reveals that average SNR (29.9%), SNR standard deviation (20.8%), and path diversity (15.9%) are the dominant
predictors of optimal protocol selection, providing interpretability to the ML component. The findings demonstrate that
ML-based hybridization of complementary routing protocols offers a resilient and energy-efficient routing solution for
next-generation smart grid neighborhood area networks.
Teslim Komolafe, Enoch Owoeye, Samuel A. Adegbola et al.· Energy Science, Engineering,...· 0 citations