Context. In the present network inventive world, there is a quick expansion of switches and protocols, which are used to cope up with the increase in customer requirement in the networking. With increasing demand for higher bandwidths and lower latency and to meet these requirements new network paths are introduced. To reduce network load in present switching network, development of new innovative switching is required. These required results can be achieved by Software Define Network or Traditional layer-3 technologies.Objectives. In this thesis, the end to end (e2e) transmission performance of OpenFlow and Layer-3 switches and their dynamic characteristics are investigated using network simulation.Methods. To replicate real life network topology and evaluate e2e transmission performance, a simulation based test-bed is implemented for both OpenFlow switch and layer-3 switch. The test beds are implemented using Network Simulator-3 (NS3). A two-tire network topology is designed with specified components for performance evaluation.Results. The performance metrics like throughput, average delay, simulation time and Packet Delivery Ratio (PDR) are measured, results are analyzed statistically and are compared. The behavior of network traffic in both the topologies are understood using NS-3 and explained further in the thesis.Conclusions. The analytical and statistical results from simulation show that OpenFlow switching performs relatively better than layer-3 switching.

Networking technology is connecting more and more people around the world. It has become an essential part of our daily life. For this connectivity to be seamless, networks need to be fast. Nonetheless, rapid growth in network traffic and variety of communication protocols overwhelms the Central Processing Units (CPUs) processing packets in the networks. Existing solutions to this problem such as ASIC, FPGA, NPU, and TOE are not cost effective and easy to manage because they require special hardware and custom configurations. This thesis approaches the problem differently by offloading the network processing to off-the-shelf Graphic Processing Units (GPUs). The thesis's primary goal is to find out how the GPUs should be used for the offloading. The thesis follows the case study approach and the selected case studies are layer 2 Bloom filter forwarding and flow lookup in Openflow switch. Implementation alternatives and evaluation methodology are proposed for both of the case studies. Then, the prototype implementation for comparing between traditional CPU-only and GPU-offloading approach is developed and evaluated. The primary findings from this work are criteria of network processing functions suitable for GPU offloading and tradeoffs involved. The criteria are no inter-packet dependency, similar processing flows for all packets, and within-packet parallel processing opportunity. This offloading trades higher latency and memory consumption for higher throughput. / Nätverksteknik ansluter fler och fler människor runt om i världen. Det har blivit en viktig del av vårt dagliga liv. För att denna anslutning skall vara sömlös, måste nätet vara snabbt. Den snabba tillväxten i nätverkstrafiken och olika kommunikationsprotokoll sätter stora krav på processorer som hanterar all trafik. Befintliga lösningar på detta problem, t.ex. ASIC, FPGA, NPU, och TOE är varken kostnadseffektivt eller lätta att hantera, eftersom de kräver speciell hårdvara och anpassade konfigurationer. Denna avhandling angriper problemet på ett annat sätt genom att avlasta nätverks processningen till grafikprocessorer som sitter i vanliga pc-grafikkort. Avhandlingen främsta mål är att ta reda på hur GPU bör användas för detta. Avhandlingen följer fallstudie modell och de valda fallen är lager 2 Bloom filter forwardering och ``flow lookup'' i Openflow switch. Implementerings alternativ och utvärderingsmetodik föreslås för både fallstudierna. Sedan utvecklas och utvärderas en prototyp för att jämföra mellan traditionell CPU- och GPU-offload. Det primära resultatet från detta arbete utgör kriterier för nätvärksprocessfunktioner lämpade för GPU offload och vilka kompromisser som måste göras. Kriterier är inget inter-paket beroende, liknande processflöde för alla paket. och möjlighet att köra fler processer på ett paket paralellt. GPU offloading ger ökad fördröjning och minneskonsumption till förmån för högre troughput.