Packet processing in modern routers and switches relies on rule matching, primarily performed by two core modules: IP prefix lookup for next-hop determination and packet classification for multi-field policy enforcement. However, most existing algorithms are rule-centric and assume uniform rule access, overlooking the highly skewed nature of real-world network traffic. Such mismatch between static rule organization and dynamic traffic behavior leads to inefficiency in both lookup and classification. To address this limitation, we propose a Traffic-aware Lookup and Forwarding (TLF) framework that leverages traffic measurement with lookup operations, enabling online adaptation to dynamic traffic patterns and frequent rule updates. Experimental results demonstrate that TLF provides 1.04×–3.37× speedups for lookup and forwarding over state-of-the-art algorithms, while substantially reducing both memory overhead and construction time. Furthermore, integrating TLF into Vector Packet Processor (VPP) and Open vSwitch (OVS) results in throughput improvements of 2.61× and 4.88×, respectively.

Packet classification is a critical component for accurately categorizing traffic in network systems. The efficiency of packet classification algorithms is primarily determined by two key factors: the classifier's data structure and the characteristics of the traffic being classified. While significant efforts have been made to optimize data structures, the potential of leveraging traffic characteristics remains underexplored. In this study, we revisit the network dataplane by integrating the network measurement module with the packet classification module. We propose an innovative Network-aware Packet Classification system (NPC) that utilizes sketch techniques to extract network traffic features. These features guide the construction of decision trees, enabling efficient and adaptable packet classification across diverse network environments. Experimental results demonstrate that the NPC achieves speedups ranging from 1.86× to 23.88× over state-of-the-art algorithms, while significantly reducing memory overhead and construction time, highlighting its practical value in real-world scenarios. Furthermore, integrating NPC into Open vSwitch (OVS) yields throughput improvements of 10.71× to 13.01× compared to the native OVS.