Policy broker-centric traffic classifier architecture for deep packet inspection systems with route asymmetry

Mehmet Akif Yazici, Kasim Oztoprak
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引用次数: 1

Abstract

If the packets of a flow are carried on separate physical paths for forward and reverse directions, the flow is said to be asymmetrically routed. Routing asymmetry causes problems regarding flow identification, traffic shaping, policy enforcement, quota management etc. in DPI systems. Clustering and state sharing are the two existing methods to overcome routing asymmetry. State sharing works only for stateful traffic flows whereas clustering leads to large traffic overhead. A hybrid scheme, the “Hybrid Asymmetric Traffic Classifier” (HATC) has recently been proposed to merge the better aspects of clustering and state sharing. HATC can handle both stateful and stateless flows with reduced overhead compared to clustering. In this study, we propose a novel flow identification method, the “Policy Broker-centric Traffic Classifier” (PBTC), which employs policy broker devices that undertake policy and charging rules functions in the provider's network. The performance of the proposed method is compared against that of HATC under two real traffic traces and several simulated scenarios to demonstrate that PBTC outperforms HATC.
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路由不对称深度包检测系统中以策略代理为中心的流量分类器体系结构
如果一个流的数据包在不同的物理路径上进行正向和反向传输,则称该流为不对称路由。路由不对称导致DPI系统在流量识别、流量整形、策略执行、配额管理等方面出现问题。聚类和状态共享是现有的两种克服路由不对称的方法。状态共享只适用于有状态的流量,而集群会导致较大的流量开销。最近提出了一种混合方案,即“混合非对称流量分类器”(HATC),它融合了聚类和状态共享的优点。与集群相比,HATC可以处理有状态流和无状态流,并且开销更小。在本研究中,我们提出了一种新的流量识别方法,即“以策略代理为中心的流量分类器”(PBTC),该方法使用策略代理设备在提供商的网络中承担策略和收费规则功能。在两条真实流量路径和几种模拟场景下,将PBTC与HATC的性能进行了比较,证明了PBTC优于HATC。
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