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• W2000657990 abstract "In this work, we first briefly introduce the concept of IP flow classification on a general conceptual level. The intention is to rise above the technological details and create a conceptual point of view on flow classification and closely related issue. Then we move on to study and compare earlier flow classification methods such as the all and selected flow classifier ad the packet count flow classifier. The comparison of these methods is done with actual network traffic and various performance metrics are presented. It is found that while the traditional methods of flow classification are found to reduce the resource usage of the network elements, they provide the user with an ambiguous traffic profile at the best. A measurement-based learning approach to flow classification is then presented. We first introduce the list based flow classification algorithm to act as the reference point to the novel approach of using learning vector quantization in flow classification. It is found that both the list classifier and the learning vector quantization algorithm, when used in flow classification, require only moderate performance from the network elements while producing an intuitive and user- comprehensible traffic profile being able to adapt to traffic profile changes. The learning vector quantization flow classifier is more sensitive to changing network traffic profiles and functions somewhat more accurately than the list classifier. While all measurement-based approaches suffer the delay of analyzing the measurement data our results indicate that measurement-based approach to flow classification is able to provide users more accurate service profiles in changing traffic environment while stating reasonable performance demands to the network equipment.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only." @default.
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• W2000657990 date "1998-12-16" @default.
• W2000657990 modified "2023-09-23" @default.
• W2000657990 title "<title>Adaptive flow classification in IP switching: the measurement-based approach</title>" @default.
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• W2000657990 doi "https://doi.org/10.1117/12.333718" @default.
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