An Efficient Parallel IP Lookup Technique for IPv6 Routers Using Multiple Hashing with Ternary marker storage

Internet address lookup is a challenging problem because of the increasing routing table sizes, increased traffic, higher speed links, and the migration to 128 bit IPv6 addresses. Routing lookup involves computation of best matching prefix for which existing solutions scale poorly when traffic in the router increases or when employed for IPV6 address lookup. Our paper describes a novel approach which employs multiple hashing on reduced number of hash tables on which ternary search on levels is applied in parallel. This scheme handles large number of prefixes generated by controlled prefix expansion by reducing collision and distributing load fairly in the hash buckets thus providing faster worst case and average case lookups. The approach we describe is fast, simple, scalable, parallelizable, and flexible

Design and Evaluation of Packet Classification Systems, Doctoral Dissertation, December 2006

Although many algorithms and architectures have been proposed, the design of efficient packet classification systems remains a challenging problem. The diversity of filter specifications, the scale of filter sets, and the throughput requirements of high speed networks all contribute to the difficulty. We need to review the algorithms from a high-level point-of-view in order to advance the study. This level of understanding can lead to significant performance improvements. In this dissertation, we evaluate several existing algorithms and present several new algorithms as well. The previous evaluation results for existing algorithms are not convincing because they have not been done in a consistent way. To resolve this issue, an objective evaluation platform needs to be developed. We implement and evaluate several representative algorithms with uniform criteria. The source code and the evaluation results are both published on a web-site to provide the research community a benchmark for impartial and thorough algorithm evaluations. We propose several new algorithms to deal with the different variations of the packet classification problem. They are: (1) the Shape Shifting Trie algorithm for longest prefix matching, used in IP lookups or as a building block for general packet classification algorithms; (2) the Fast Hash Table lookup algorithm used for exact flow match; (3) the longest prefix matching algorithm using hash tables and tries, used in IP lookups or packet classification algorithms;(4) the 2D coarse-grained tuple-space search algorithm with controlled filter expansion, used for two-dimensional packet classification or as a building block for general packet classification algorithms; (5) the Adaptive Binary Cutting algorithm used for general multi-dimensional packet classification. In addition to the algorithmic solutions, we also consider the TCAM hardware solution. In particular, we address the TCAM filter update problem for general packet classification and provide an efficient algorithm. Building upon the previous work, these algorithms significantly improve the performance of packet classification systems and set a solid foundation for further study

Algorithms and Architectures for Network Search Processors

The continuous growth in the Internet’s size, the amount of data traffic, and the complexity of processing this traffic gives rise to new challenges in building high-performance network devices. One of the most fundamental tasks performed by these devices is searching the network data for predefined keys. Address lookup, packet classification, and deep packet inspection are some of the operations which involve table lookups and searching. These operations are typically part of the packet forwarding mechanism, and can create a performance bottleneck. Therefore, fast and resource efficient algorithms are required. One of the most commonly used techniques for such searching operations is the Ternary Content Addressable Memory (TCAM). While TCAM can offer very fast search speeds, it is costly and consumes a large amount of power. Hence, designing cost-effective, power-efficient, and high-speed search techniques has received a great deal of attention in the research and industrial community. In this thesis, we propose a generic search technique based on Bloom filters. A Bloom filter is a randomized data structure used to represent a set of bit-strings compactly and support set membership queries. We demonstrate techniques to convert the search process into table lookups. The resulting table data structures are kept in the off-chip memory and their Bloom filter representations are kept in the on-chip memory. An item needs to be looked up in the off-chip table only when it is found in the on-chip Bloom filters. By filtering the off-chip memory accesses in this fashion, the search operations can be significantly accelerated. Our approach involves a unique combination of algorithmic and architectural techniques that outperform some of the current techniques in terms of cost-effectiveness, speed, and power-efficiency

Longest Prefix Match Algorithms

V této bakalářské práci byly popsány základní algoritmy pro vyhledání nejdelšího shodného prefixu (LPM). K již existujícím implementacím v knihovně Netbench byl přidán další algoritmus - LC Trie. Všechny algoritmy, které knihovna obsahuje, byly testovány nad reálnými množinami IPv6 prefixů. Na základě zde získaných dat byly navzájem porovnány. Dále byly sepsány skripty pro stahování prefixů z významných zdrojů na internetu a testovací skripty k jednotlivým algoritmům.This bachelor's thesis deals with a description of basic longest prefix match (LPM) algorithms. Another algorithm - LC Trie - was added to existing implementations into the Netbench library. All the algorithms which the library includes were tested with real groups of IPv6 prefixes. They were compared on the basis of previously obtained data. Testing scripts for each of the algorithms were implemented as well as scripts for downloading groups of prefixes from significant sources on the internet.

An algorithm for fast route lookup and update

Increase in routing table sizes, number of updates, traffic, speed of links and migration to IPv6 have made IP address lookup, based on longest prefix matching, a major bottleneck for high performance routers. Several schemes are evaluated and compared based on complexity analysis and simulation results. A trie based scheme, called Linked List Cascade Addressable Trie (LLCAT) is presented. The strength of LLCAT comes from the fact that it is easy to be implemented in hardware, and also routing table update operations are performed incrementally requiring very few memory operations guaranteed for worst case to satisfy requirements of dynamic routing tables in high speed routers. Application of compression schemes to this algorithm is also considered to improve memory consumption and search time. The algorithm is implemented in C language and simulation results with real-life data is presented along with detailed description of the algorithm

Reducing Router Forwarding Table Size Using Aggregation and Caching

The fast growth of global routing table size has been causing concerns that the Forwarding Information Base (FIB) will not be able to fit in existing routers\u27 expensive line-card memory, and upgrades will lead to a higher cost for network operators and customers. FIB Aggregation, a technique that merges multiple FIB entries into one, is probably the most practical solution since it is a software solution local to a router, and does not require any changes to routing protocols or network operations. While previous work on FIB aggregation mostly focuses on reducing table size, this work focuses on algorithms that can update compressed FIBs quickly and incrementally. Quick updates are critical to routers because they have very limited time to process routing updates without impacting packet delivery performance. We have designed three algorithms: FIFA-S for the smallest table size, FIFA-T for the shortest running time, and FIFA-H for both small tables and short running time, and operators can use the one best suited to their needs. These algorithms significantly improve over existing work in terms of reducing routers\u27 computation overhead and limiting impact on the forwarding plane while maintaining a good compression ratio. Another potential solution is to install only the most popular FIB entries into the fast memory (e.g., an FIB cache), while storing the complete FIB in slow memory. In this paper, we propose an effective FIB caching scheme that achieves a considerably higher hit ratio than previous approaches while preventing the cache-hiding problem. Our experimental results using data traffic from a regional network show that with only 20K prefixes in the cache (5.36% of the actual FIB size), the hit ratio of our scheme is higher than 99.95%. Our scheme can also efficiently handle cache misses, cache replacement and routing updates

Models, Algorithms, and Architectures for Scalable Packet Classification

The growth and diversification of the Internet imposes increasing demands on the performance and functionality of network infrastructure. Routers, the devices responsible for the switch-ing and directing of traffic in the Internet, are being called upon to not only handle increased volumes of traffic at higher speeds, but also impose tighter security policies and provide support for a richer set of network services. This dissertation addresses the searching tasks performed by Internet routers in order to forward packets and apply network services to packets belonging to defined traffic flows. As these searching tasks must be performed for each packet traversing the router, the speed and scalability of the solutions to the route lookup and packet classification problems largely determine the realizable performance of the router, and hence the Internet as a whole. Despite the energetic attention of the academic and corporate research communities, there remains a need for search engines that scale to support faster communication links, larger route tables and filter sets and increasingly complex filters. The major contributions of this work include the design and analysis of a scalable hardware implementation of a Longest Prefix Matching (LPM) search engine for route lookup, a survey and taxonomy of packet classification techniques, a thorough analysis of packet classification filter sets, the design and analysis of a suite of performance evaluation tools for packet classification algorithms and devices, and a new packet classification algorithm that scales to support high-speed links and large filter sets classifying on additional packet fields