Runtime Verification of P4 Switches with Reinforcement Learning

Apoorv Shukla, K. Hudemann, A. Hecker, S. Schmid
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引用次数: 31

Abstract

We present the design and early implementation of p4rl, a system that uses reinforcement learning-guided fuzz testing to execute the verification of P4 switches automatically at runtime. p4rl system uses our novel user-friendly query language, p4q to conveniently specify the intended properties in simple conditional statements (if-else) and check the actual runtime behavior of the P4 switch against such properties. In p4rl, user-specified p4q queries with the control plane configuration, Agent, and the Reward System guide the fuzzing process to trigger runtime bugs automatically during Agent training. To illustrate the strength of p4rl, we developed and evaluated an early prototype of p4rl system that executes runtime verification of a P4 network device, e.g., L3 (Layer-3) switch. Our initial results are promising and show that p4rl automatically detects diverse bugs while outperforming the baseline approach.
基于强化学习的P4交换机运行时验证
我们介绍了p4rl的设计和早期实现,该系统使用强化学习引导的模糊测试在运行时自动执行P4开关的验证。p4rl系统使用我们新颖的用户友好查询语言p4q,方便地在简单的条件语句(if-else)中指定预期的属性,并根据这些属性检查P4交换机的实际运行时行为。在p4rl中,用户指定的带有控制平面配置、Agent和奖励系统的p4q查询指导模糊测试过程,从而在Agent训练期间自动触发运行时错误。为了说明p4rl的强度,我们开发并评估了p4rl系统的早期原型,该系统执行P4网络设备的运行时验证,例如L3(第三层)交换机。我们的初步结果很有希望,并表明p4rl在优于基线方法的同时自动检测各种错误。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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