线索:实现快速更新压缩表并行查找与减少动态冗余

Tong Yang, Ruian Duan, Jianyuan Lu, Shenjiang Zhang, Huichen Dai, B. Liu
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引用次数: 16

摘要

骨干路由器路由表的规模继续保持快速增长,目前有的路由表的规模已达到400K条[1]。路由表压缩是解决大表压缩的有效方法。同时,由于网络拓扑结构的变化和互联网新功能的出现,对路由快速更新的需求日益迫切。此外,互联网链路传输速度已扩展到商用100Gbps和实验室实验400Gbps以太网,导致对超快速路由查找的迫切需求。为了实现高性能,骨干路由器必须同时优雅地处理路由表压缩、快速路由查找和快速增量更新(CLUE)这三个问题,而以往的工作往往只集中在三个维度中的一个。为了解决这些问题,我们提出了一套完整的解决方案——clue,通过改进以前的工作并增加一种新的增量更新机制。CLUE由三部分组成:路由表压缩算法、改进的并行查找机制和新的快速增量更新机制。路由表压缩算法基于ONRTC算法[2],这是TCAM快速并行查找和TCAM快速更新的基础。第二部分是对动态负载均衡并行查找机制的逻辑缓存方案的改进[3]。第三种是trie、TCAM和冗余前缀更新算法的结合。通过数学证明对CLUE的性能进行了分析,得出在最坏情况下,加速因子与冗余前缀的命中率成正比的结论,实验结果也证实了这一结论。大规模实验结果表明,与[3]中的机制相比,当使用4个TCAM时,CLUE在相同吞吐量下只需要约71%的TCAM条目、4.29%的更新时间和3/4的动态冗余前缀。此外,与[3]中的机制相比,CLUE还有一个优势——可以避免由于前缀更新冗余而导致的控制平面和数据平面频繁交互。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CLUE: Achieving Fast Update over Compressed Table for Parallel Lookup with Reduced Dynamic Redundancy
The sizes of routing table in backbone routers continue to keep a rapid growth and some of them currently increase up to 400K entries [1]. An effective solution to deflate the large table is the routing table compression. Meanwhile, there is an increasingly urgent demand for fast routing update mainly due to the change of network topology and new emerging Internet functionalities. Furthermore, the Internet link transmission speed has scaled up to 100Gbps commercially and towards 400Gbps Ethernet for laboratory experiments, resulting in a raring need of ultra-fast routing lookup. To achieve high performance, backbone routers must gracefully handle the three issues simultaneously: routing table Compression, fast routing Lookup, and fast incremental Update (CLUE), while previous works often only concentrate on one of the three dimensions. To address these issues, we propose a complete set of solutions-CLUE, by improving previous works and adding a novel incremental update mechanism. CLUE consists of three parts: a routing table compression algorithm, an improved parallel lookup mechanism, and a new fast incremental update mechanism. The routing table compression algorithm is based on ONRTC algorithm [2], a base for fast TCAM parallel lookup and fast update of TCAM. The second part is the improvement of the logical caching scheme for dynamic load balancing parallel lookup mechanism [3]. The third one is the conjunction of the trie, TCAM and redundant prefixes update algorithm. We analyze the performance of CLUE by mathematical proof, and draw the conclusion that speedup factor is proportional to the hit rate of redundant prefixes in the worst case, which is also confirmed by experimental results. Large-scale experimental results show that, compared with the mechanism in [3], CLUE only needs about 71% TCAM entries, 4.29% update time, and 3/4 dynamic redundant prefixes for the same throughput when using four TCAMs. In addition, CLUE has another advantage over the mechanism in [3] - the frequent interactions between control plane and data plane caused by redundant prefixes update can be avoided.
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