论文标题
多重尝试通过深度加固学习进行分组分类
Multibit Tries Packet Classification with Deep Reinforcement Learning
论文作者
论文摘要
高性能数据包分类是支持可扩展网络应用程序（例如防火墙，入侵检测和差异化服务）的关键组件。随着核心网络中的线路率的不断增加，使用手工调整的启发式方法设计可扩展和高性能分类分类解决方案成为一个巨大的挑战。在本文中，我们提出了可扩展的基于学习的数据包分类引擎及其性能评估。通过利用规则集的稀疏性，我们的算法使用一些有效的位（EBS）来提取只有几个内存访问的大量候选规则。这些有效的位是通过深入的强化学习来学习的，它们用于创建位图来滤除大多数规则，而这些规则不需要完全匹配以提高在线系统性能。此外，我们的基于EBS学习的选择方法与规则集无关，该规则集可以应用于不同规则集。与没有EBS的传统决策树相比，我们的多重尝试分类引擎在最坏和平均情况下的查找时间都优于55％的查找时间，并减少了记忆足迹。
High performance packet classification is a key component to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing in the line-rate in core networks, it becomes a great challenge to design a scalable and high performance packet classification solution using hand-tuned heuristics approaches. In this paper, we present a scalable learning-based packet classification engine and its performance evaluation. By exploiting the sparsity of ruleset, our algorithm uses a few effective bits (EBs) to extract a large number of candidate rules with just a few of memory access. These effective bits are learned with deep reinforcement learning and they are used to create a bitmap to filter out the majority of rules which do not need to be full-matched to improve the online system performance. Moreover, our EBs learning-based selection method is independent of the ruleset, which can be applied to varying rulesets. Our multibit tries classification engine outperforms lookup time both in worst and average case by 55% and reduce memory footprint, compared to traditional decision tree without EBs.
