A Fast URL Lookup Engine for Content-Aware Multi-Gigabit Switches
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Abstract:
Cluster-based servers are one of the best solutions to build high-performance, scalable, and reliable Internet Web servers. A number of researches have been done about enabling the dispatcher in cluster-based Web servers to route the users requests based on higher layer information, such as URLs. Hashing functions and tree structure are often used to achieve the goal of URL lookup, but they may cause the problem of collision and result in unacceptable performance. This paper presents a fast scalable URL lookup mechanism that uses content addressable memory (CAM) as the basic hardware components. Our scheme not only supports exact matching of URL lookup, but also provides prefix-matching lookup ability so that it is very practically for URL content-filtering like systems. The proposed scheme takes constant time to lookup a URL and furnishes a rate of 100 million lookups per second. By applying the entry reuse concept, the expensive CAM space can be used in a more efficient way to store more URLs. With this fast URL lookup engine, the performance of content dispatchers or URL content filters can be greatly improved.Keywords:
Content-addressable storage
File server
An associative memory, unlike an addressed memory used in conventional computers, is content addressable. That is, storing and retrieving information are not based on the location of the memory cell but on the content of the information. There are a number of approaches to implement an associative memory, one of which is to use a neural dynamical system where objects being memorized or recognized correspond to its basic attractors. The work presented in this paper is the investigation of applying a particular type of neural dynamical associative memory, namely the projection network, to pattern recognition and data fusion. Three types of attractors, which are fixed-point, limit- cycle, and chaotic, have been studied, evaluated and compared.
Content-addressable storage
Bidirectional associative memory
Associative property
Memory map
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In this paper, relative capacity of a specific higher order Hopfield-type associative memory is considered. This model, which is known as exponential Hopfield Neural Network is suitable for hardware implementation and is not of a great computational cost. It is shown that, this modification of the Hopfield model significantly improves the storage capacity of the associative memory. We also classify the model via a stability measure, and study the effect of training the network with biased patterns on the stability.
Hopfield network
Bidirectional associative memory
Content-addressable storage
Associative property
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Swarm is a storage system that provides scalable, reliable, and cost-effective data storage. Swarm is based on storage servers, rather than file servers; the storage servers are optimized for cost-performance and aggregated to provide high-performance data access. Swarm uses a striped log abstraction to store data on the storage servers. This abstraction simplifies storage allocation, improves file access performance, balances server loads, provides fault-tolerance through computed redundancy, and simplifies crash recovery. We have developed a Swarm prototype using a cluster of Linux-based personal computers as the storage servers and clients; the clients access the servers via the Swarm-based Sting file system. Our performance measurements show that a single Swarm client can write to two storage servers at 3.0 MB/s, while four clients can write to eight servers at 16.0 MB/s.
File server
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This paper compares two working network-based file servers, the Xerox Distributed File System (XDFS) implemented at the Xerox Palo Alto Research Center, and the Cambridge File Server (CFS) implemented at the Cambridge University Computer Laboratory. Both servers support concurrent random access to files using atomic transactions, both are connected to local area networks, and both have been in service long enough to enable us to draw lessons from them for future file servers. We compare the servers in terms of design goals, implementation issues, performance, and their relative successes and failures, and discuss what we would do differently next time.
File server
Network File System
Server farm
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The authors compare the storage capacity and other properties of various neural associative processors (APs) and find that the Ho-Kashyap (H-K) AP has the largest storage capacity and can handle linearly dependent keys. General memory (random keys) and distortion-invariant pattern-recognition APs are considered. A discussion is presented of a content-addressable structure that further improves recall accuracy and noise performance and decreases the size of the memory matrix. Results from the new H-K content-addressable AP are given. In all cases, the APs use only one pass (no iterations) in recall
Content-addressable storage
Associative property
Bidirectional associative memory
Distortion (music)
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A low-power Content-Addressable-Memory (CAM) is introduced employing a new mechanism for associativity between the input tags and the corresponding address of the output data. The proposed architecture is based on a recently developed clustered-sparse-network using binary-weighted connections that on-average will eliminate most of the parallel comparisons performed during a search. Therefore, the dynamic energy consumption of the proposed design is significantly lower compared to that of a conventional low-power CAM design. Given an input tag, the proposed architecture computes a few possibilities for the location of the matched tag and performs the comparisons on them to locate a single valid match. A 0.13 um CMOS technology was used for simulation purposes. The energy consumption and the search delay of the proposed design are 9.5%, and 30.4% of that of the conventional NAND architecture respectively with a 3.4% higher number of transistors.
Content-addressable storage
Memory architecture
Associative property
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The content addressable memory (CAM) is allowed to search a data word without knowing where its address is. In addition, it is permissible to associate the content of the location or neighboring locations where the data word was identified. This paper presents our own approach for VLSI hardware implementation of the CAM memory. The proposed solution uses a Hopfield neural network model and is characterized by simplicity and the possibility of using the same hardware structures for saving may data patterns. Will be presented design methods and implementation to VLSI circuit structures, the performance of our solution and experimental results.
Content-addressable storage
Hopfield network
Associative property
Bidirectional associative memory
Simplicity
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Isomorphism relations are utilized to analyze the Hopfield associative memory. When the number of fundamental memories m/spl les/3, it is proved that two Hopfield associative memories are isomorphic if they have the same mutual distances between the fundamental memories. The number of stable states and the synchronous convergence time of a Hopfield associative memory are shown to be less than or equal to 2 to the power 2/sup m-1/ and 4 to the power 2/sup m-1/, respectively, where m/spl ges/1.< >
Associative property
Isomorphism (crystallography)
Content-addressable storage
Bidirectional associative memory
Hopfield network
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The content addressable associative memory based on the Hopfield neural network model can be used directly to store and retrieve information with robustness and error correction capability. The Hopfield associative memory can successfully recall data stored only when the stored data satisfy some stringent conditions. To overcome these limitations of the Hopfield model, some modifications have been proposed. In this paper we analyze a limitation of the Hopfield model, present a modification of the Hopfield model, and at last give its numerical simulation.
Hopfield network
Bidirectional associative memory
Content-addressable storage
Robustness
Associative property
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Ceph is a scalable and high performance distributed file system. In this study, a Ceph-based storage server was implemented and used actively. This storage system has been used as a disk of 40 virtual servers in 4 different Proxmox servers. Performance evaluation of the system has been conducted on virtual servers that holds Windows and Linux based operating systems.
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