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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/EF38306D-4B5E-482D-BFC8-D94AB06E8232 | ||
| Year | 2006 | ||
| Type of Item | Conference Full Paper | ||
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| Bibliographic Citation | V. Papaefstathiou, I. Papaefstathiou, "An innovative low-cost Classification Scheme for combined multi-Gigabit IP and Ethernet Networks," in IEEE International Conference on Communications, 2006, pp. 211 - 216. doi: 10.1109/ICC.2006.254729 https://doi.org/10.1109/ICC.2006.254729 | ||
| Appears in Collections |
IP is certainly the most popular wide area network protocol while Ethernet is the most common Layer-2 network protocol, and it is currently being deployed beyond the tight borders of LANs. In order to accommodate the needs of MANs and WANs, several QoS mechanisms employed either at the IP layer or the MAC sublayer have been proposed. These QoS mechanisms require identification of network flows and the classification of network packets according to certain packet header fields. In this paper, we propose a classification engine employed either at the MAC sublayer or the IP layer, which is the successor of a scheme already successfuly implemented which is only employed at the MAC sublayer. This new scheme uses an innovative hashing scheme combined with an efficient trie-based structure. By using such techniques, the extremely high speed decisions ??at a rate of more than 100Gb/sec- are supported, while the memory needs of the proposed engine are significantly lower compared to those of the similar schemes currently used. This engine has been implemented in hardware utilizing less than 0.2mm2 in a state of the art CMOS technology. As a result the proposed scheme is a very promising candidate for both the next-generation IP classification engines(probably incorporated within the high-end network processors) as well as for the Ethernet equipments that need to support classification at multi-Gigabit per second network speeds, while also employing the minimum amount of memory.
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