The size of routing tables has been growing rapidly, while the link transmission speed of Internet backbone has increased up to 100Gbps commercially and towards 400Gbps Ethernet for laboratory experiments. In order to alleviate the pressure from both the huge large routing table and very high interface speed, ISPs are trying to find ways to compress the table while striving to design a more powerful lookup engine. To address this issue, we propose an algorithm, named Optimal Non-overlap Routing Table Constructor (ONRTC), to compute an equivalent routing table with a minimal number of prefixes under the constraint that all the prefixes are not overlapped. Experimental evaluations show that, for large backbone routing tables, the ONRTC algorithm requires only about 71% of the original number of prefixes. We release ONRTC's source code in [10].

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.

With the rapid growth of the Internet, the update messages in backbone routers become more and more frequent due to the ever-increasing dynamic changes on network topologies and new emerging functionalities of the Internet. In addition, update messages often come as a burst. Update action interrupts the packet lookup operation in the router's data plane, thus inefficient incremental update algorithm slows down IP lookup speed, and potentially badly degrades the system performance during bursty updates. Among trie-based routing lookup algorithms, binary trie has the best update complexity O(W) (W is the maximum depth of the trie), but exhibits slow lookup speed, failing to be competent for forwarding tens of gigabit-per-second traffic in backbone routers. Therefore, various improved routing lookup algorithms are proposed to pursue high speed based on binary trie, but sacrificing the performance of incremental update. To minimize the interruption time that update operation incurs, we propose Blind Spot (BS) algorithm by picking out those updating nodes which would have produced domino effect, achieving an update complexity of O(lookup+h), meanwhile keeping the lookup speed almost unchanged. Blind Spot algorithm is a universal methodology, which is applicable to all the trie-based lookup algorithms. To evaluate the performance of BS algorithm, we applied it to Lulea [1] and LC-trie [2] algorithms as two representatives. Extensive experimental results show that both Lulea+BS and LC+BS algorithms achieve a much faster update speed than binary trie, while keeping the same lookup speed as the original Lulea and LC-trie algorithms.

Virtual router research has drawn increasing attention in recent years, and the most challenging issues of virtual routers are compression, lookup, and incremental update of 10∼200 routing tables. In this paper, we propose a set of solutions to achieve that storage, lookup time, and update time don't expand to 10∼200 times, but reduce to 1∼2 times.

With the fast development of Internet, the size of routing tables in the backbone routers keeps a rapid growth in recent years. An effective solution to control the memory occupation of the ever-increased huge routing table is the Forwarding Information Base (FIB) compression. Existing optimal FIB compression algorithm ORTC suffers from high computational complexity and poor update performance, due to the loss of essential structure information during its compression process. To address this problem, we present two suboptimal FIB compression algorithms — EAR-fast and EAR-slow, respectively, based on our proposed Election and Representative (EAR) algorithm which is an optimal FIB compression algorithm. The two suboptimal algorithms preserve the structure information, and support fast incremental updates while reducing computational complexity. Experiments on an 18-month real data set show that compared with ORTC, the proposed EAR-fast algorithm requires only 9.8% compression time and 37.7% memory space, but supports faster update while prolonging the recompression interval remarkably. All these performance advantages come at a cost of merely a 1.5% loss in compression ratio compared with the theoretical optimal ratio.