Xing Li

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.

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.