TY - GEN
T1 - A Framework for Reproducible Data Plane Performance Modeling
AU - Scholz, Dominik
AU - Harkous, Hasanin
AU - Gallenmüller, Sebastian
AU - Stubbe, Henning
AU - Helm, Max
AU - Jaeger, Benedikt
AU - Deric, Nemanja
AU - Goshi, Endri
AU - Zhou, Zikai
AU - Kellerer, Wolfgang
AU - Carle, Georg
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/12/13
Y1 - 2021/12/13
N2 - Languages for programming data planes like P4 sparked a plethora of new applications in the data plane. The dynamic, evolving environment makes it challenging to understand what performance can be expected when running a program in a specific data plane target. However, knowing this is crucial for network operators when upgrading their networks. We present a framework for the reproducible analysis and modeling of P4 program components. By defining and generating precise specifications of the experiments, we separate fully auto-generated components from testbed- or target-specific parts. Measurement results are used to derive performance models automatically. These can then be used to compare the measured with the theoretical performance, or to model the cost of entire paths through the data plane. In two case studies, we use our framework to discover and model selected behavior for a DPDK-based software target and for the NFP-4000 SmartNIC platform.
AB - Languages for programming data planes like P4 sparked a plethora of new applications in the data plane. The dynamic, evolving environment makes it challenging to understand what performance can be expected when running a program in a specific data plane target. However, knowing this is crucial for network operators when upgrading their networks. We present a framework for the reproducible analysis and modeling of P4 program components. By defining and generating precise specifications of the experiments, we separate fully auto-generated components from testbed- or target-specific parts. Measurement results are used to derive performance models automatically. These can then be used to compare the measured with the theoretical performance, or to model the cost of entire paths through the data plane. In two case studies, we use our framework to discover and model selected behavior for a DPDK-based software target and for the NFP-4000 SmartNIC platform.
KW - Data Plane
KW - P4
KW - Performance Modeling Framework
UR - http://www.scopus.com/inward/record.url?scp=85124171752&partnerID=8YFLogxK
U2 - 10.1145/3493425.3502756
DO - 10.1145/3493425.3502756
M3 - Conference contribution
AN - SCOPUS:85124171752
T3 - ANCS 2021 - Proceedings of the 2021 Symposium on Architectures for Networking and Communications Systems
SP - 59
EP - 65
BT - ANCS 2021 - Proceedings of the 2021 Symposium on Architectures for Networking and Communications Systems
PB - Association for Computing Machinery, Inc
T2 - 16th ACM/IEEE Symposium on Architectures for Networking and Communications Systems, ANCS 2021
Y2 - 13 December 2021 through 16 December 2021
ER -