Skip to content

Author

Gaogang Xie

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Book Open access Aug 2026

Dorado: Scaling SmartNIC Session Tables on Commodity DDRs

FPGA-based SmartNICs are widely deployed for cloud network function acceleration, but their memory subsystem is under increasing pressure because of large session tables. Conventional wisdom suggests that high packet processing performance relies on advanced memories (e.g., SRAM, HBM), but those are costly to add at cloud scale. Dorado is a novel design that scales SmartNIC session tables entirely on inexpensive DDR modules. At the heart of Dorado are three new techniques that extract commodity DDR performance by restructuring session table layout, decomposing processing pipelines to reduce locking, and scheduling memory accesses to minimize stalls. Our testbed results show that Dorado improves packet processing rates by 33%, even with fewer hardware resources. Further, we have deployed Dorado to millions of servers, processing network traffic from billions of users on a large public cloud for over three years. Our production results show that Dorado can accommodate up to 16M session entries, reduce memory cost by 80%, while enabling 50Mpps line-rate processing.

Heng Yu, Kai Ren, Jiajun Liang et al. · 0 citations
2026

Rethinking Virtual Network Construction for Network Emulation at Scale: Analysis, Modeling, and Optimization

Network emulation has become an indispensable methodology for evaluating next-generation network architectures, offering a critical balance between experimental fidelity and operational scalability. However, its effectiveness is fundamentally constrained by inefficiencies in emulating large-scale networks, particularly during virtual network construction. This bottleneck arises from mandatory serialization of virtual link instantiation and operating system (OS) kernel-level notification overheads, which collectively degrade performance by orders of magnitude on 10K-node topologies. Departure from the current practice that employs a multi-machine framework for improvements, we propose SplitNN (Split Network and Namespace), a novel single-machine network emulation paradigm that breaks the serialization constraint through multi-VM (virtual machines) partitioning, and reduces notification overheads via namespace segmentation. Extensive evaluations show that SplitNN constructs 10K-node virtual networks within 1–5 minutes on a single machine, achieving a 98.5%–99.2% reduction in construction time compared to state-of-the-art emulators. While primarily a single-machine solution, SplitNN seamlessly integrates with multi-machine deployments, complementing them by enabling cumulative gains in both scalability and efficiency.

Kaifei Peng, Yanbiao Li, Wenbin Li et al. · 0 citations