论文标题
通过挖掘转发模式来自动推断高级网络意图
Automatic Inference of High-Level Network Intents by Mining Forwarding Patterns
论文作者
论文摘要
网络运营商的高级意图与实现意图的低级配置之间存在语义差距。以前的作品试图使用验证或合成技术弥合差距，这两者都需要对预期行为的形式规格，这些规范在现实世界中很少可用，甚至不知所措。本文讨论了一种弥合差距的替代方法，即从低级网络行为中推断出高级意图。具体而言，我们提供动漫，框架和工具，以一组观察到的转发行为，自动渗透一组最能描述所有观察结果的可能意图。我们的结果表明，动漫可以从低级转发行为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.
