压缩IP转发表：接近熵界及超越熵界
Compressing IP Forwarding Tables: Towards Entropy Bounds and Beyond
摘要：
最近，人们对压缩数据结构的兴趣高涨，其目的是将越来越大量的信息打包到受限的存储器中，而不牺牲标准操作的效率，如随机访问、搜索或更新。本文的主要目标是展示如何数据压缩可以受益于网络社区，通过展示如何挤压IP转发信息库（FIB），巨大的表咨询IP路由器作出转发决策，到信息理论熵的界限，基本上是零成本的最长前缀匹配和FIB更新。首先，我们采用最先进的压缩数据结构，产生一个静态熵压缩FIB表示与渐近最佳的查找。然后，我们重新设计了古老的前缀树，通常用于IP查找至少20年的IP路由器，也承认熵界和支持查找在最佳时间和更新在近最佳时间。在Linux内核原型上的评估表明，我们的压缩器将包含超过440 K前缀的FIB编码到大约100- 400 Kbps的内存中，查找吞吐量增加了三倍，并且对FIB更新没有影响。这份技术报告包含了对原稿的一些重要更正和修订。
英文摘要：
Lately, there has been an upsurge of interest in compressed data structures, aiming to pack ever larger quantities of information into constrained memory without sacrificing the efficiency of standard operations, like random access, search, or update. The main goal of this paper is to demonstrate how data compression can benefit the networking community, by showing how to squeeze the IP Forwarding Information Base (FIB), the giant table consulted by IP routers to make forwarding decisions, into information-theoretical entropy bounds, with essentially zero cost on longest prefix match and FIB update. First, we adopt the state-of-the-art in compressed data structures, yielding a static entropy-compressed FIB representation with asymptotically optimal lookup. Then, we re-design the venerable prefix tree, used commonly for IP lookup for at least 20 years in IP routers, to also admit entropy bounds and support lookup in optimal time and update in nearly optimal time. Evaluations on a Linux kernel prototype indicate that our compressors encode a FIB comprising more than 440K prefixes to just about 100--400 KBytes of memory, with a threefold increase in lookup throughput and no penalty on FIB updates. This technical report contains a number of important corrections and revisions to the original manuscript.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
