that arise in that context. A classification technique approaching 100 % accuracy proves to be a labor-intensive process that needs to test flow-characteristics against multiple classification criteria in order to gain sufficient confidence in the nature of the causal application. Nevertheless, the benefits

Abstract—Large-scale packet classification such as Open-Flow table lookup in Software Defined Networking (SDN) is a key task performed at the Internet routers. However, the increasing size of the rule set and the increasing width of each individual rule make large-scale packet classification a

Abstract—Traffic classification is important to many network applications, such as network monitoring. The classic way to identify flows, e.g., examining the port numbers in the packet headers, becomes ineffective. In this context, deep packet inspection technology, which does not only inspect

Abstract—This paper aims to show the impact on classification accuracy and the level of computational gain that could be obtained in applying deep packet inspection on truncated peer to peer traffic flows instead of complete ones. Using one of the latest open source classifiers, experiments were

processors. GASPP integrates optimized GPU-based implementations of a broad range of operations commonly used in network traffic processing applica-tions, including the first purely GPU-based implementa-tion of network flow tracking and TCP stream reassem-bly. GASPP also employs novel mechanisms for tackling

In previous work[1-3] it has been shown how open-source routers on new PC hardware allows for forwarding speeds of 10Gb/s and above. In this work we extend the applicability of the results by showing how the new 10Gb/s interface classification techniques can be used to separate packet forwarding

Abstract — In the context of IEEE 802.11b network testbeds, we examine the differences between unicast and broadcast link properties, and we show the inherent difficulties in precisely estimating unicast link properties via those of broadcast beacons even if we make the length and transmission rate

I would like to thank Prof. Dr. Torsten Braun, head of the Computer Network and Distributed Systems group (RVS), for supervising this work and for his insightful advises. Prof. Dr. Torsten Braun encouraged and motivated me to publish my research results and he provided me the opportunity to present the work on various conferences, for which I thank him. I would also like to thank Prof. Dr. Roger Wattenhofer, responsible for the Koreferat of this work. Also, Prof. Dr. Oscar Nierstrasz who was willing to be the co-examinator of this work deserves many thanks. Many thanks go to my colleagues of the RVS group and of the IAM for our various interesting discussions about all kinds of topics and for making the institute a very pleasant and friendly place to work at. Special thanks go to David Steiner, Marc Steinemann, Matthias Scheidegger, Florian Baumgartner, Ruy De Oliveira, and Attila Weyland. There are many students who worked with me and helped a lot in developing and implementing. Among them I especially thankful to Thomas Bernoulli,

conditions. Flush is scalable; its effective bandwidth over a 48-hop wireless network is approximately one-third of the rate achievable over one hop. The design of Flush is sim-plified by assuming that different flows do not interfere with each other, a reasonable restriction for many sensornet ap-plications

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