Transparent TSN for Agnostic End-hosts via P4-based Traffic Characterization at Switches

Mission-critical networks currently face a transition from legacy network protocols to advanced time-sensitive networking (TSN) standards. TSN guarantees reliable and deterministic communication using off-the-shelf Ethernet equipment. However, end-hosts must be TSN-aware and may pose security risks by arbitrarily over-allocating resources. Integrating central instances like a software-defined networking (SDN) controller into TSN networks to streamline network management presents a promising solution. This raises concerns regarding latency in communication between switches and the controller, as well as among switches themselves. To address this, we propose an approach that renders TSN transparent to end-hosts, eliminating the need for their involvement in resource reservations. We embed packet processing logic in P4-enabled TSN switches to characterize network traffic intelligently. This enables switches to allocate network resources autonomously and adjust real-time traffic handling mechanisms. Leveraging P4 storage structures introduces statefulness for traffic characterization computing within the inherently stateless P4 language. Our experiments demonstrate that our P4-enhanced switches require a minimal 0.014 MB of switch memory to distinguish between periodic and non-periodic traffic with an 80% precision while incurring a mere 0.2 ms forwarding latency per packet.

Authors

Institutions

Publication Details

Published
2024-09-09
DOI
https://doi.org/10.1109/lcn60385.2024.10639713
Citations
1
Primary Topic
Software-Defined Networks and 5G
Type
article
Field-Weighted Citation Impact
0.35
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Transparent TSN for Agnostic End-hosts via P4-based Traffic Characterization at Switches

Nurefşan Sertbaş Bülbül, Mathias Fischer, Cornelia Brülhart, Nils Ole Tippenhauer
1 citations
Software-Defined Networks and 5G
0.35
article

Transparent TSN for Agnostic End-hosts via P4-based Traffic Characterization at Switches

Nurefşan Sertbaş Bülbül, Mathias Fischer, Cornelia Brülhart, Nils Ole Tippenhauer
article en
1 citations

Abstract

Mission-critical networks currently face a transition from legacy network protocols to advanced time-sensitive networking (TSN) standards. TSN guarantees reliable and deterministic communication using off-the-shelf Ethernet equipment. However, end-hosts must be TSN-aware and may pose security risks by arbitrarily over-allocating resources. Integrating central instances like a software-defined networking (SDN) controller into TSN networks to streamline network management presents a promising solution. This raises concerns regarding latency in communication between switches and the controller, as well as among switches themselves. To address this, we propose an approach that renders TSN transparent to end-hosts, eliminating the need for their involvement in resource reservations. We embed packet processing logic in P4-enabled TSN switches to characterize network traffic intelligently. This enables switches to allocate network resources autonomously and adjust real-time traffic handling mechanisms. Leveraging P4 storage structures introduces statefulness for traffic characterization computing within the inherently stateless P4 language. Our experiments demonstrate that our P4-enhanced switches require a minimal 0.014 MB of switch memory to distinguish between periodic and non-periodic traffic with an 80% precision while incurring a mere 0.2 ms forwarding latency per packet.

Universität Hamburg (DE), Helmholtz Center for Information Security (DE)
Openalex Percentile: Top 39%
Software-Defined Networks and 5G
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.