We currently see a shift from fixed-function network devices with limited configurability towards network devices with a fully programmable processing pipeline. A prominent example of this development is P4 that provides a language and reference architecture model to design and program network devices. The core element of this reference model is the programmable matchaction table that defines the processing steps for the network packets. In this paper, we demonstrate that these tables, which we use to create our own modeling framework, are the key driver of device performance. P4-programmable devices come in a wide variety regarding their underlying hardware architecture, such as CPU-based systems or ASICs, as representatives of both ends of the spectrum. CPU-based P4 target platforms offer limited performance but are easily extensible. ASIC P4 targets have dedicated P4 processing pipelines with limited programmability but offer highly optimized performance. To reflect these fundamental differences, our modeling framework incorporates different approaches to accurately model and predict the performance of P4-enabled devices.
%0 Conference Paper
%1 dom20ITC32
%A Scholz, Dominik
%A Stubbe, Henning
%A Gallenmüller, Sebastian
%A Carle, Georg
%B 32th International Teletraffic Congress (ITC 32)
%C Osaka, Japan
%D 2020
%K Data_Plane_Programming Device_Benchmarking Key_Performance_Indicator Model P4 itc itc32
%T Key Properties of Programmable Data Plane Targets
%U https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc32/dom20ITC32.pdf?inline=true
%X We currently see a shift from fixed-function network devices with limited configurability towards network devices with a fully programmable processing pipeline. A prominent example of this development is P4 that provides a language and reference architecture model to design and program network devices. The core element of this reference model is the programmable matchaction table that defines the processing steps for the network packets. In this paper, we demonstrate that these tables, which we use to create our own modeling framework, are the key driver of device performance. P4-programmable devices come in a wide variety regarding their underlying hardware architecture, such as CPU-based systems or ASICs, as representatives of both ends of the spectrum. CPU-based P4 target platforms offer limited performance but are easily extensible. ASIC P4 targets have dedicated P4 processing pipelines with limited programmability but offer highly optimized performance. To reflect these fundamental differences, our modeling framework incorporates different approaches to accurately model and predict the performance of P4-enabled devices.
@inproceedings{dom20ITC32,
abstract = {We currently see a shift from fixed-function network devices with limited configurability towards network devices with a fully programmable processing pipeline. A prominent example of this development is P4 that provides a language and reference architecture model to design and program network devices. The core element of this reference model is the programmable matchaction table that defines the processing steps for the network packets. In this paper, we demonstrate that these tables, which we use to create our own modeling framework, are the key driver of device performance. P4-programmable devices come in a wide variety regarding their underlying hardware architecture, such as CPU-based systems or ASICs, as representatives of both ends of the spectrum. CPU-based P4 target platforms offer limited performance but are easily extensible. ASIC P4 targets have dedicated P4 processing pipelines with limited programmability but offer highly optimized performance. To reflect these fundamental differences, our modeling framework incorporates different approaches to accurately model and predict the performance of P4-enabled devices.},
added-at = {2021-03-02T15:22:25.000+0100},
address = {Osaka, Japan},
author = {Scholz, Dominik and Stubbe, Henning and Gallenmüller, Sebastian and Carle, Georg},
biburl = {https://www.bibsonomy.org/bibtex/21ca2153570194ad3af644c03ad841268/itc},
booktitle = {32th International Teletraffic Congress (ITC 32)},
interhash = {19d7ce1bc835c2e84d619588a45cefe8},
intrahash = {1ca2153570194ad3af644c03ad841268},
keywords = {Data_Plane_Programming Device_Benchmarking Key_Performance_Indicator Model P4 itc itc32},
timestamp = {2021-03-02T15:22:25.000+0100},
title = {Key Properties of Programmable Data Plane Targets},
url = {https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc32/dom20ITC32.pdf?inline=true},
year = 2020
}