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HaRP: Rapid Packet Classification via Hashing Round-Down Prefixes

机译：HaRP：通过散列舍入前缀对数据包进行快速分类

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摘要

Packet classification is central to a wide array of Internet applications and services, with its approaches mostly involving either hardware support or optimization steps needed by software-oriented techniques (to add precomputed markers and insert rules in the search data structures). Unfortunately, an approach with hardware support is expensive and has limited scalability, whereas one with optimization fails to handle incremental rule updates effectively. This work deals with rapid packet classification, realized by hashing round-down prefixes (HaRP) in a way that the source and the destination IP prefixes specified in a rule are rounded down to ȁC;designated prefix lengthsȁD; (DPL) for indexing into hash sets. HaRP exhibits superb hash storage utilization, able to not only outperform those earlier software-oriented classification techniques but also well accommodate dynamic creation and deletion of rules. HaRP makes it possible to hold all its search data structures in the local cache of each core within a contemporary processor, dramatically elevating its classification performance. Empirical results measured on an AMD 4-way 2.8 GHz Opteron system (with 1 MB cache for each core) under six filter data sets (each with up to 30 K rules) obtained from a public source unveil that HaRP enjoys up to some 3.6{times} throughput level achievable by the best known decision tree-based counterpart, HyperCuts (HC).
机译：数据包分类是各种Internet应用程序和服务的核心，它的方法主要涉及硬件支持或面向软件的技术所需的优化步骤（在搜索数据结构中添加预先计算的标记并插入规则）。不幸的是，具有硬件支持的方法昂贵且可扩展性有限，而具有优化的方法无法有效处理增量规则更新。这项工作涉及快速数据包分类，它是通过对舍入向下的前缀（HaRP）进行散列来实现的，这种方式是将规则中指定的源IP和目标IP前缀四舍五入为C；指定的前缀长度为D； （DPL）索引到哈希集。 HaRP具有出色的哈希存储利用率，不仅可以胜过那些早期的面向软件的分类技术，还可以很好地适应规则的动态创建和删除。 HaRP使得将所有搜索数据结构保存在现代处理器中每个内核的本地缓存中成为可能，从而大大提高了其分类性能。在从公开来源获得的六个过滤器数据集（每个过滤器具有最多30 K规则）下，在AMD 4路2.8 GHz Opteron系统（每个内核具有1 MB高速缓存）下测得的经验结果表明，HaRP最多可享受3.6倍{最著名的基于决策树的对策HyperCuts（HC）可达到的吞吐量级别。

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