ROAR is proposed, a novel architectural solution that implements Deep Reinforcement Learning (DRL) inside P4 programmable switches to perform adaptive routing policies based on network conditions and traffic patterns that show both a throughput and delay improvement in the transmission compared to traditional approaches.
Networks with highly dynamic data transmission demands and network topologies are common in real world. A fundamental problem in such networks is achieving scalable traffic allocation to maximize long-term total throughput under link capacity constraints. However, state-of-the-art (SOTA) works lack scalability. This is primarily due to two reasons in large-scale networks: first, they require solving constrained optimization problems online, which leads to high decision latency; second, they rely on reinforcement learning algorithms for policy optimization, which are inefficient in exploration and challenging to train effectively. To address these issues, we propose the Fast Networked Control (FNC) policy framework, which firstly utilizes parallelizable neural network modules to process the state and generate raw decisions, followed by basic operations such as normalizations and comparisons, which do not require iteration or optimization, to obtain decisions that satisfy the constraints. Hence, FNC policy avoids solving constrained optimization problems and supports parallel execution, significantly reducing decision latency. Furthermore, this policy preserves gradient flow and supports backpropagation, which enable us to design an imitation learning algorithm to efficiently train the policy in an end-to-end manner. Experiments in large-scale networks show that our FNC policy achieves an average 8% improvement in demands satisfaction and 10 times reduction in decision latency versus SOTA works.
Zhaoxing Yang, Guiyun Fan, Anjie Cao et al.· IEEE Transactions on Network...· 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
Future 6G services will require strict performance guarantees, especially in terms of delay, end-to-end (e2e) across multiple network domains including packet and radio segments. While deterministic transport and slice-based capacity allocation can improve segment-level performance, ensuring e2e Network Service (NS) performance remains challenging as it requires making decisions Near–Real-Time (Near-RT) on a per-service basis, which does not fit well within the typical centralized control and orchestration hierarchy. Multi-agent systems (MAS), where a number of distributed agents collaborate, has demonstrated its capabilities for such Near-RT control. Agents equipped with Deep Reinforcement Learning (DRL) engines autonomously made traffic routing decisions based on e2e telemetry measurements. In this paper, we extend such MAS solutions for NS traffic routing focused on covering several issues that appear under frequent NS reconfiguration, e.g., caused by end device mobility. In addition, we define a lifecycle for NS operation that includes the initial MAS deployment, model reconfiguration during operation, and NS reconfiguration. The proposed lifecycle requires the definition of DRL training and validation procedures to produce models ready to be deployed with guaranteed performance under certain network conditions. In addition, model selection algorithms are defined for the lifecycle scenarios. In case of NS reconfiguration, a procedure for probe testing the actual network conditions is proposed to improve model selection. Evaluation across a meaningful set of network and traffic scenarios shows that the MAS is able to maintain e2e delay guarantees under all the lifecycle scenarios.
H. Shakespear-Miles, S. Barzegar, M. Ruiz et al.· IEEE Transactions on Network...· 0 citations
As the modern network infrastructure continue to become more and more complex, massive set of firewall rules have been produced, which causes large package classification latency and poor resource utilization. Firewall rule ordering is an NP-hard mathematical programming problem in which the wrong sequencing of firewall rules maximizes the cost of matching, as well as presents the possibility of policy violation. The current meta-heuristic algorithms are said to be constrained by predetermined traffic patterns and unresponsive to real time. In this paper, an adaptive Reinforcement Learning (RL) framework is introduced to the dynamic optimization of firewall rules. The issue is presented as a Markov Decision Process (MDP) and Deep Q-Network (DQN) agent can observe the frequency of rule hits and dependency restrictions and propose the best reorganization. In order to solve the scalability issue, A state-space dimensionality reduction algorithm is adopted, where we target high-traffic rule sets. The Directed Acyclic Graph (DAG) is used to make sure that the reordering of rules does not compromise the semantics of security policies. The experimental results prove that the throughput is increased and the cost of rule comparisons are decreased as compared to fixed configurations. Our framework is better in changing traffic dynamics.
Jonnalagadda Syam Babu, K. V. Shilpa, M.Hanish Kumar et al.· 2026 7th International Confe...· 0 citations
The research findings indicate that the key to enhancing real-time cloud network intelligence lies in the architecture based on Deep Reinforcement Learning (DRL), and provide useful guidelines for future engineering projects to ensure that network management infrastructure possesses autonomy, flexibility, and efficiency.
Shuyao Jia· The 2026 International Confe...· 0 citations
