This survey delivers the first systematic exploration of how artificial intelligence can bolster SR, from traffic classification and segment-list computation to fast reroute, service-function chaining, and multi-domain orchestration by spanning supervised and unsupervised learning, reinforcement learning, and hybrid pipelines that fuse forecasting, neural optimization, and heuristic search.
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 Neu...
Ramani Kasarapu, C. Kushwanth, N. Kumar 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
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...
Zhaoxing Yang, Guiyun Fan, An-Jie Cao et al.· IEEE Transactions on Network...· 0 citations
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.
Hao Sun, Fang He, Congyuan Ji et al.· arXiv.org· 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 firewal...
Jonnalagadda Syam Babu, K. V. Shilpa, M.Hanish Kumar et al.· 2026 7th International Confe...· 0 citations
Future sixth-generation (6G)-oriented networks require programmable control that can adapt routing to latency and congestion without unsafe online exploration. This study evaluates offline multi-agent deep deterministic policy gradient (MADDPG) with behavior-adjusted training rewards for latency-aware path control in s...
A. Kyzyrkanov, Y. Nurakhov, Zhenis Otarbay et al.· Technologies· 0 citations
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