Since the inception of modern communication networks, the quest for operations automation has never ceased. Yet the evolution of network automation is difficult to characterize with a single maturity ladder. Throughout this history, network control systems have expanded their capabilities for observation, decision support, routine execution, and operator interaction, but these capabilities have not advanced uniformly. Such uneven progress makes the degree of automation an unreliable proxy for trustworthy network-side actuation. The unresolved question is not simply how much automation a system provides, but under what conditions it can be entrusted to change the network state. This paper examines that question through Network Control Intelligence (NCI), a five-axis framework spanning Decision Logic, Adaptability, Knowledge, Control Delegation, and Interface. We use NCI to organize the evolution of network-control systems into three eras: rule-based and scripted automation, programmable and data-driven control, and Large Language Model (LLM)-enabled network operations. Viewed through this framework, the three eras reveal a persistent asymmetry. None of these gains, however, automatically determines when network control should be trusted to change the network state. We frame trustworthy autonomy as a governed alignment between what a system can infer, what it can verify, and what it is authorized to execute. On that basis, the paper develops a reference architecture that separates proposal generation from governed execution, identifies recurring integration patterns for LLM-enabled operations, and derives a research agenda for higher network autonomy under explicit assurance, safety, and governance constraints.
Tianzhu Zhang, Changgang Zheng, Shanshan Wang et al.· 0 citations
While commodity NIC-based back-end AI networks offer ultra-high intra-cluster bandwidth for distributed training, their limited programmability and on-chip resources hinder the implementation of advanced VPC features such as fine-grained isolation and stateful security policies. Furthermore, access to resources within the VPC needs to be routed through the front-end DPU, which is shared by the scale-up domain. The mismatch between the front-end DPU’s bandwidth and the back-end requirements causes GPU underutilization when intensive VPC communication is required for content recommendation, AIGC, and federated learning workloads. We propose an architecture that decouples complex policy enforcement from high-speed packet forwarding to support VPC semantics on back-end NICs and enable front-end/back-end integration. Evaluations show near-full GPU utilization in our analytical model and 71 μ s P999 extra latency of the first packet, suggesting that commodity hardware can support both high-throughput AI training and flexible VPC features.
Yinhe Wang, Xing Li, Enge Song et al.· Asia-Pacific Workshop on Net...· 0 citations
Current NFVs assign packets to CPU cores at flow granularity, where each flow is pinned to a single CPU. This approach is efficient under most scenarios but has exposed limitations when handling elephant flows. These “heavy hitters” overwhelm single cores, creating bottlenecks that affect overall throughput and degrade service quality. As networks scale to higher-speed links and core-rich CPUs, these imbalances become more severe. In this paper, we propose ParaFlowO, an architecture that Parallelizes processing elephant Flows across multiple CPU cores while preserving in-Order delivery. ParaFlowO breaks elephant flows into flowlets and dynamically rotates them across multiple cores. It integrates a lightweight reordering mechanism to preserve packet order and controls parallelism to mitigate contention on shared state. Preliminary evaluations show that ParaFlowO offers a practical solution to mixed-grained parallelism in stateful middleboxes.
Changgang Zheng, Bowen Yang, Jin Ke et al.· Asia-Pacific Workshop on Net...· 1 citation