Automatic Inference of High-Level Network Intents by Mining Forwarding Patterns
Automatic Inference of High-Level Network Intents by Mining Forwarding Patterns
复制标题
通过挖掘转发模式自动推断高级网络意图
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
A. Kheradmand
中科院分区：
文献类型：
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作者：
A. Kheradmand
网络运营商的高层意图与实现这些意图的底层配置之间存在语义鸿沟。先前的研究工作试图使用验证或综合技术来弥合这一鸿沟，这两种技术都需要对预期行为进行形式化规范，而在现实世界中，这种规范很少存在甚至不为人知。本文讨论了一种弥合这一鸿沟的替代方法，即从底层网络行为推断高层意图。具体来说，我们提供了Anime，这是一个框架和工具，它在给定一组观察到的转发行为的情况下，自动推断出一组最能描述所有观察结果的可能意图。我们的研究结果表明，Anime能够从底层转发行为中以可接受的性能推断出高质量的意图。
There is a semantic gap between the high-level intents of network operators and the low-level configurations that achieve the intents. Previous works tried to bridge the gap using verification or synthesis techniques, both requiring formal specifications of the intended behavior which are rarely available or even known in the real world. This paper discusses an alternative approach for bridging the gap, namely to infer the high-level intents from the low-level network behavior. Specifically, we provide Anime, a framework and a tool that given a set of observed forwarding behavior, automatically infers a set of possible intents that best describe all observations. Our results show that Anime can infer high-quality intents from the low-level forwarding behavior with acceptable performance.