We consider the problem of organizing address tables for internet routers to enable fast searching. Our proposal is to to build an efficient, compact and easily searchable implementation of an IP routing table by using an LC-trie, a trie structure with combined path and level compression. The depth of this structure increases very slowly as function of the number of entries in the table. A node can be coded in only four bytes and the size of the main search structure never exceeds 256 kB for the tables in the US core routers. We present a software implementation that can sustain approximately half a million lookups per second on a 133 MHz Pentium personal computer, and two million lookups per second on a more powerful SUN Sparc Ultra II workstation. 1

This paper presents a way of doing IP packet classification at high speed, a critical operation for highcapacity routers. Two hardware designs are presented, both based on simple, standard logic. One goal is that the designs should be inexpensive and simple enough that it is feasible to replicate them on each input port on a router. The first design is for unicast forwarding, based on destination addresses, and does IP address lookup through a longest prefix match operation. The second design is for identifier lookup, used for multicast addresses and for packet flows. Both designs use standard memory and simple programmable logic, and are capable of one lookup per memory cycle. With standard memory technology, this corresponds to a rate of more than 10 Gb/s per port. In this paper we present the designs, analyse their performance and cost, and discuss how they can be used in a high-capacity router. 1.0

In this work we compare different methods for router workload deduction and flow classification schemes. Gigabit-routing solutions and different schemes of integrated switching and routing (Internet protocol (IP) switching) have emerged. Several flow classification schemes to be used in traffic-controled IP switching are presented and evaluated. As a new alternative, the `Learning Vector Quantization' classifier is examined and found to be able to successfully classify Internet traffic flows. # 1998 Elsevier Science B.V.

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 issues. Then we move on to study and compare earlier flow classification methods such as the all and selected flow classifier and the packet count flow classifier. The comparison of these methods is done with actual network tra#c 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 tra#c 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 tra#c profile being able to adapt to tra#c profile changes. The learning vector quantization flow classifier is more sensitive to changing network tra#c profiles and functions somewhat more accurately than the list classifier. While all measurement-based approaches su#er 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 tra#c environment while stating reasonable performance demands to the network equipment.