ConnSched: Selective Connection Offloading Framework for Accelerating Stateful NFs with DPU
Songlin Chen, Fuliang Li, Qin Chen, Chengxi Gao, Man Hou, Jiaxing Shen
摘要
The escalating volume of network traffic drives the offloading of stateful Network Functions (NFs) to Data Processing Units (DPUs) for enhanced performance and host CPU relief. However, current DPU offloading approaches, while often boosting forwarding throughput, frequently suffer from poor concurrency performance. Effectively addressing this requires overcoming challenges inherent to DPU architectures, such as the Arm subsystem’s limitations in rapid connection setup and the intricate management of hardware flow table resources, which can otherwise cap connection processing rates (e.g., below 100k connections per second (CPS) with conventional methods). To address these limitations, we present ConnSched, a high-performance software framework for efficient and robust offloading of connection processing in stateful NFs to DPUs. ConnSched incorporates two key mechanisms: ConnSched-Filter adaptively identifies and filters out short-lived connections by monitoring their lifecycle and current DPU load, thereby reserving hardware resources for long-lived connections and enhancing robustness against concurrent traffic bursts. Complementing this, ConnSched-QoS implements a prioritized offloading mechanism, managing DPU hardware flow table entries by giving precedence to critical flows, especially when resources are constrained. Experimental results demonstrate that ConnSched achieves a 6.4× higher new connection creation rate compared to standard DPU offloading methods and 38.7% higher throughput than CPU-based approaches. ConnSched simultaneously improves concurrent connection handling capability while maintaining high packet forwarding performance.
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