129 research outputs found

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

    Get PDF
    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

    Optimasi Pencarian Alamat IP Menggunakan Counting Bloom Filter Pada Web Application Firewall

    Get PDF
    Proses pencarian IP pada Web Application Firewall menjadi sebuah tantangan ketika database IP yang dikumpulkan mulai membesar. WAF harus dapat dengan cepat memberikan keputusan apakah sebuah IP masuk kedalam kategori buruk (blacklisted ip) atau tidak. Berdasarkan penelitian-penelitian sebelumnya algoritma bloom filter dapat dengan mengurangi waktu pencarian secara signifikan, namun hal itu tidak cukup karena bloom filter memiliki kelemahan pada struktur datanya yang tidak dapat dilakukan penghapusan, berdasarkan masalah tersebut kami mengajukan pemodelan baru pencarian IP pada WAF dengan menggunakan algoritma Counting Bloom Filter

    Algorithms and Architectures for Network Search Processors

    Get PDF
    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

    Models, Algorithms, and Architectures for Scalable Packet Classification

    Get PDF
    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

    Fast Packet Classification Using Bloom Filters

    Get PDF
    While the problem of general packet classification has received a great deal of attention from researchers over the last ten years, there is still no really satisfactory solution. Ternary Content Addressable Memory (TCAM), although widely used in practice, is both expensive and consumes a lot of power. Algorithmic solutions, which rely on commodity memory chips, are relatively inexpensive and power-efficient, but have not been able to match the generality and performance of TCAMs. In this paper we propose a new approach to packet classification, which combines architectural and algorithmic techniques. Our starting point is the well-known crossproducting algorithm, which is fast but has significant memory overhead due to the extra rules needed to represent the crossproducts. We show how to modify the crossproduct method in a way that drastically reduces the memory required, without compromising on performance. We avoid unnecessary accesses to off-chip memory by filtering off-chip accesses using on-chip Bloom filters. For packets that match p rules in a rule set, our algorithm requires just 4 + p + ǫ independent memory accesses on average, to return all matching rules, where ǫ á 1 is a small constant that depends on the false positive rate of the Bloom filters. Each memory access is just 256 bits, making it practical to classify small packets at OC-192 link rates using two commodity SRAM chips. For rule set sizes ranging from a few hundred to several thousand filters, the average rule set expansion factor attributable to the algorithm is just 1.2. The memory consumption per rule is 36 bytes in the average case

    Data Structures and Algorithms for Scalable NDN Forwarding

    Get PDF
    Named Data Networking (NDN) is a recently proposed general-purpose network architecture that aims to address the limitations of the Internet Protocol (IP), while maintaining its strengths. NDN takes an information-centric approach, focusing on named data rather than computer addresses. In NDN, the content is identified by its name, and each NDN packet has a name that specifies the content it is fetching or delivering. Since there are no source and destination addresses in an NDN packet, it is forwarded based on a lookup of its name in the forwarding plane, which consists of the Forwarding Information Base (FIB), Pending Interest Table (PIT), and Content Store (CS). In addition, as an in-network caching element, a scalable Repository (Repo) design is needed to provide large-scale long-term content storage in NDN networks. Scalable NDN forwarding is a challenge. Compared to the well-understood approaches to IP forwarding, NDN forwarding performs lookups on packet names, which have variable and unbounded lengths, increasing the lookup complexity. The lookup tables are larger than in IP, requiring more memory space. Moreover, NDN forwarding has a read-write data plane, requiring per-packet updates at line rates. Designing and evaluating a scalable NDN forwarding node architecture is a major effort within the overall NDN research agenda. The goal of this dissertation is to demonstrate that scalable NDN forwarding is feasible with the proposed data structures and algorithms. First, we propose a FIB lookup design based on the binary search of hash tables that provides a reliable longest name prefix lookup performance baseline for future NDN research. We have demonstrated 10 Gbps forwarding throughput with 256-byte packets and one billion synthetic forwarding rules, each containing up to seven name components. Second, we explore data structures and algorithms to optimize the FIB design based on the specific characteristics of real-world forwarding datasets. Third, we propose a fingerprint-only PIT design that reduces the memory requirements in the core routers. Lastly, we discuss the Content Store design issues and demonstrate that the NDN Repo implementation can leverage many of the existing databases and storage systems to improve performance

    Classifying DDoS packets in high-speed networks

    Full text link
    Recently high-speed networks have been utilized by attackers as Distributed Denial of Service (DDoS) attack infrastructure. Services on high-speed networks also have been attacked by successive waves of the DDoS attacks. How to sensitively and accurately detect the attack traffic, and quickly filter out the attack packets are still the major challenges in DDoS defense. Unfortunately most current defense approaches can not efficiently fulfill these tasks. Our approach is to find the network anomalies by using neural network and classify DDoS packets by a Bloom filter-based classifier (BFC). BFC is a set of spaceefficient data structures and algorithms for packet classification. The evaluation results show that the simple complexity, high classification speed and accuracy and low storage requirements of this classifier make it not only suitable for DDoS filtering in high-speed networks, but also suitable for other applications such as string matching for intrusion detection systems and IP lookup for programmable routers.<br /

    Towards practical use of Bloom Filter based IP lookup in operational network

    Get PDF
    International audienceBloom Filter is a widely used data structure in computer science. It enables memory efficient and fast set membership queries. Bloom filter-based solutions have been proposed in the past decade for lookup in forwarding tables of backbone routers [2]. However, the main shortcomings of using Bloom Filters for lookup lie in the absence of support for deletion operations that are needed to update the forwarding tables. Counting Bloom Filter supporting deletion has therefore to be used, increasing significantly the memory requirement. Moreover, Counting Bloom Filter suffers from both false positive and false negative. In this paper, we propose to solve the issue with deletion of Bloom Filters by using a Withdrawal To annOuncement (WTO) mapping that replaces withdrawal with announcements, transforming deletions into additions or record changes. Experimental evaluation show that the proposed techniques improve largely the performance of Bloom Filter used for forwarding lookup and open way for the use of Bloom Filters in real operational settings
    corecore