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Author Taylor, David E.
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2005
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Packet classification ♦ Flow identification
Abstract Packet classification is an enabling function for a variety of Internet applications including quality of service, security, monitoring, and multimedia communications. In order to classify a packet as belonging to a particular flow or set of flows, network nodes must perform a search over a set of filters using multiple fields of the packet as the search key. In general, there have been two major threads of research addressing packet classification, algorithmic and architectural. A few pioneering groups of researchers posed the problem, provided complexity bounds, and offered a collection of algorithmic solutions. Subsequently, the design space has been vigorously explored by many offering new algorithms and improvements on existing algorithms. Given the inability of early algorithms to meet performance constraints imposed by high speed links, researchers in industry and academia devised architectural solutions to the problem. This thread of research produced the most widely-used packet classification device technology, Ternary Content Addressable Memory (TCAM). New architectural research combines intelligent algorithms and novel architectures to eliminate many of the unfavorable characteristics of current TCAMs. We observe that the community appears to be converging on a combined algorithmic and architectural approach to the problem. Using a taxonomy based on the high-level approach to the problem and a minimal set of running examples, we provide a survey of the seminal and recent solutions to the problem. It is our hope to foster a deeper understanding of the various packet classification techniques while providing a useful framework for discerning relationships and distinctions.
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-09-01
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 37
Issue Number 3
Page Count 38
Starting Page 238
Ending Page 275

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Source: ACM Digital Library