# Accelerating Stateful Network Applications with Performance Prediction on SoC SmartNICs

arXiv: https://arxiv.org/abs/2410.22229 · HTML: https://mlsanity.com/paper/2410.22229/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 51 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [Programming Protocol-Independent Packet Processors](https://mlsanity.com/paper/1312.1719/) (2013)
- [FlexTOE: Flexible TCP Offload with Fine-Grained Parallelism](https://mlsanity.com/paper/2110.10919/) (2021)
- [Packet Transactions: High-level Programming for Line-Rate Switches](https://mlsanity.com/paper/1512.05023/) (2015)
- [Performance Prediction of On-NIC Network Functions with Multi-Resource Contention and Traffic Awareness](https://mlsanity.com/paper/2405.05529/) (2024)

## Abstract (arXiv)

Offloading stateful network functions to multi-threaded SoC SmartNICs promises significant performance and cost benefits. However, realizing this potential is hindered by two fundamental challenges. First, without performance guidance, developers are forced into a slow, manual trial-and-error cycle of deploying and testing to find a feasible resource allocation. Second, sustaining performance under changing traffic requires adapting state residency and handling overload within memory layouts fixed at compile time. This paper introduces Vela, a framework that addresses both challenges through a model-driven, compile-time/runtime co-design. Its core is a predictive compiler that replaces the manual tuning loop with fast, automated analysis, using a novel, state-centric analytical model to estimate the throughput ceiling of any given resource allocation plan. This is complemented by a lightweight runtime that dynamically manages cache contents within the compiled memory layout and mitigates overload. We implement and evaluate Vela on Netronome Agilio and NVIDIA BlueField 3 SmartNICs across four NFs. Vela reduces host CPU or on-board Arm core usage by 62.9%-91.9% relative to the best baseline achieving the same throughput. For the NATLB workload, vela also improves throughput by 15.2%-120.8% over the best baseline at the same host/Arm core count.