PlanB: Efficient Software IPv6 Lookup with Linearized $B^+$-Tree

2026-04-16

AI Summary

This work addresses the performance degradation and high memory overhead of traditional longest prefix match (LPM) algorithms caused by the diverse and lengthy prefixes in IPv6 forwarding tables. To overcome these limitations, the paper presents the first formulation of LPM as a unified one-dimensional elementary interval search problem and introduces a flattened array-based linearized $B^+$-tree structure. By integrating CPU-centric optimizations—including vectorization, batching, branch-free logic, and loop unrolling—the proposed design significantly enhances lookup efficiency. Experimental results demonstrate that the approach achieves a lookup rate of 390 million operations per second on a single AMD core, scaling to 3.4 billion operations per second on 12 cores, thereby outperforming existing software solutions by 1.6× to 14×.

Technology Category

• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Data Compression

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

IP lookup via Longest Prefix Match (LPM) is critical for packet forwarding. Unfortunately, conventional lookup algorithms are inefficient for IPv6 Forwarding Information Bases (FIBs), which are characterized by a set of long prefixes with diverse lengths. We observe that LPM inherently represents a two-dimensional (2D) search problem over both prefix values and prefix lengths, but existing algorithms mostly treat LPM as two separate levels of one-dimensional (1D) searches, causing poor lookup performance and high memory overhead. This paper presents PlanB, a novel scheme for high-speed IPv6 lookup. We transform the 2D LPM into an equivalent 1D search problem over elementary intervals, thereby unifying the search across prefix value and lengths. We then adapt a flat-array-based B-tree structure to the needs of LPM to propose the linearized $B^+$-tree, based on which we introduce an efficient search algorithm tailored to the properties of the transformed space. To maximize performance, we integrate PlanB with vectorization, batching, branch-free logic, and loop unrolling to fully exploit CPU parallelism. Extensive evaluation shows that PlanB achieves single-core performance of 390 Million Lookups Per Sec (MLPS) with real-world IPv6 FIBs on AMD processor, and scales to full-12-core performance of 3.4 Billion Lookups Per Sec (BLPS). This is 1.6$\times$$\sim$14$\times$ higher than state-of-the-art software-based schemes (PopTrie, CP-Trie, Neurotrie and HBS).

Problem

• IPv6 lookup
• Longest Prefix Match
• Forwarding Information Base
• 2D search problem
• LPM efficiency

Innovation

• Longest Prefix Match
• Linearized B⁺-Tree
• IPv6 Lookup
• Elementary Intervals
• Vectorization