Enhancing Stateful Processing in Programmable Data Planes: Model and Improved Architecture

Stateful data plane network applications are indispensable but their efficiency is hindered by prevalent network device architectures which utilize a Blocking Scheme to maintain state consistency. The Blocking Scheme results in poor throughput and latency performance due to its frequent halts during packet processing. In response to this issue, we propose an innovative Non-Blocking Scheme and construct a theoretical model based on the G/GI/m queueing model. The new scheme leverages the speculative execution method, avoiding unnecessary blocking by taking advantage of the fact that the state update ratio is usually much smaller than the incoming packet rate. In case of speculation failures, the affected packets are reprocessed to guarantee state consistency. We provide an approximate model for the Blocking Scheme, and show that, even with relaxed approximations, the Blocking Scheme still performs worse than the Non-Blocking Scheme. The superior performance of the Non-Blocking Scheme is further corroborated through rigorous simulations, conducted under both realistic and synthetic traces. Based on our model, we propose an enhanced architecture: SN_RAPID (Sequence Number and unidirectional Reverse path-Augmented PIpeline Dataplane), to support speculative execution. The architecture is simpler than the other architectures supporting stateful network functions. Serving as a design foundation for future iterations, we implement a prototype of SN_RAPID in FPGA which can run at line speed, and also develop a software ASIC emulator. The experiments show the superiority of the improved architecture.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Networking
Published
2024-12-11
DOI
https://doi.org/10.1109/tnet.2024.3510638
Citations
1
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
0.35
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing Stateful Processing in Programmable Data Planes: Model and Improved Architecture

Haoyu Song, Shuxin Liu, Jia Liu, Yong Feng et al.
1 citations
IEEE Transactions on Networking
Distributed and Parallel Computing Systems
0.35
article

Enhancing Stateful Processing in Programmable Data Planes: Model and Improved Architecture

Haoyu Song, Shuxin Liu, Jia Liu, Yong Feng, Zhikang Chen, Hanyi Zhou
article en
1 citations

Abstract

Stateful data plane network applications are indispensable but their efficiency is hindered by prevalent network device architectures which utilize a Blocking Scheme to maintain state consistency. The Blocking Scheme results in poor throughput and latency performance due to its frequent halts during packet processing. In response to this issue, we propose an innovative Non-Blocking Scheme and construct a theoretical model based on the G/GI/m queueing model. The new scheme leverages the speculative execution method, avoiding unnecessary blocking by taking advantage of the fact that the state update ratio is usually much smaller than the incoming packet rate. In case of speculation failures, the affected packets are reprocessed to guarantee state consistency. We provide an approximate model for the Blocking Scheme, and show that, even with relaxed approximations, the Blocking Scheme still performs worse than the Non-Blocking Scheme. The superior performance of the Non-Blocking Scheme is further corroborated through rigorous simulations, conducted under both realistic and synthetic traces. Based on our model, we propose an enhanced architecture: SN_RAPID (Sequence Number and unidirectional Reverse path-Augmented PIpeline Dataplane), to support speculative execution. The architecture is simpler than the other architectures supporting stateful network functions. Serving as a design foundation for future iterations, we implement a prototype of SN_RAPID in FPGA which can run at line speed, and also develop a software ASIC emulator. The experiments show the superiority of the improved architecture.

IEEE Transactions on NetworkingVol. 33(2)
Tsinghua University (CN)
Openalex Percentile: Top 36%
Distributed and Parallel Computing Systems
0.35
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.