A Framework for Reproducible Data Plane Performance Modeling

A Framework for Reproducible Data Plane Performance Modeling
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可重复数据平面性能建模框架

DOI:
10.1145/3493425.3502756
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发表时间:
2021
期刊:
Proceedings of the Symposium on Architectures for Networking and Communications Systems
影响因子:
--
通讯作者:
G. Carle
G. Carle
中科院分区:
--
文献类型:
--
作者:
D. Scholz;H. Harkous;S. Gallenmüller;H. Stubbe;M. Helm;B. Jaeger;N. Deric;E. Goshi;Z. Zhou;W. Kellerer;G. Carle

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像P4这样的数据平面编程语言在数据平面引发了过多的新应用。动态、不断发展的环境使理解在特定数据平面目标中运行程序时的预期性能变得具有挑战性。然而,了解这一点对于网络运营商升级网络是至关重要的。我们提出了一个框架,用于P4程序组件的可重复性分析和建模。通过定义和生成实验的精确规范,我们将完全自动生成的组件从试验台或目标特定的部件中分离出来。测量结果用于自动派生性能模型。然后,可以使用这些数据将测量的性能与理论性能进行比较,或对通过数据平面的整个路径的成本进行建模。在两个案例研究中,我们使用我们的框架发现基于DPDK的软件目标和NFP-4000 SmartNIC平台的选定行为并对其进行建模。
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.
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