This work addresses the applicability of a switched queuing network as the sole communication network in an aircraft cabin, and proposes an algorithm based on a mixed-integer program to determine hard bounds for the end-to-end delay. These hard bounds guarantee mandatory performance bounds for safety-relevant functions in the aircraft cabin, such as audio announcements or smoke detection. Techniques from the field of deterministic Network Calculus are used to benchmark the results from our novel approach. The results show that our solution allows improved mapping of non-preemptive queuing networks, compared to relevant state-of-the-art approaches.

The SSL and TLS infrastructure used in important protocols like HTTPs and IMAPs is built on an X.509 public-key infrastructure (PKI). X.509 certificates are thus used to authenticate services like online banking, shopping, e-mail, etc. However, it always has been felt that the certification processes of this PKI may lack in stringency, resulting in a deployment where many certificates do not meet the requirements of a secure PKI.

Capturing network traffic with commodity hardware has become a feasible task: Advances in hardware as well as soft- ware have boosted off-the-shelf hardware to performance levels that some years ago were the domain of expensive special-purpose hardware. However, the capturing hardware still needs to be driven by a well-performing software stack in order to minimise or avoid packet loss. Improving the capturing stack of Linux and FreeBSD has been an extensively covered research topic in the past years. Although the majority of the proposed enhancements have been backed by evaluations, these have mostly been conducted on different hardware platforms and software versions, which renders a comparative assessment of the various approaches difficult, if not impossible.

One major challenge in communication networks is the problem of dynamically distributing load in the presence of bursty and hard to predict changes in traffic demands. Current traffic engineering operates on time scales of several hours which is too slow to react to phenomena like flash crowds or BGP reroutes. One possible solution is to use load sensitive routing. Yet, interacting routing decisions at short time scales can lead to oscillations, which has prevented load sensitive routing from being deployed since the early experiences in Arpanet.

Search engines are a vital part of the Web and thus the Internet infrastructure. Therefore understanding the behavior of users searching the Web gives insights into trends, and enables enhancements of future search capabilities. Possible data sources for studying Web search behavior are either server-side logs or client-side logs. Unfortunately, current server-side logs are hard to obtain as they are considered proprietary by the search engine operators. Therefore we in this paper present a methodology for extracting client-side logs from the traffic exchanged between a large user group and the Internet. The added benefit of our methodology is that we do not only extract the search terms, the query sequences, and search results of each individual user but also the full clickstream, i.e., the result pages users view and the subsequently visited hyperlinked pages. We propose a finite-state Markov model that captures the user web searching and browsing behavior and allows us to deduce users' prevalent search patterns. To our knowledge, this is the first such detailed client-side analysis of clickstreams.

We present a novel algorithm for efficiently gathering statistics about the hit frequencies on the nodes of a search tree in a packet processing system, under limiting space constraints. The expand and collapse (EaC) algorithm is a heuristic that periodically adjusts the subset of nodes of the search tree at which statistics are gathered, in order to use the limited space available to collect statistics in preference from the currently most heavily-hit nodes in the search tree. We prove convergence and good node-hit coverage of the algorithm and validate its performance on a set of simulated data. The information collected can be useful for a variety of reasons, such as inferring traffic properties, discovering failures and attacks or dynamically optimizing the search method itself for locality patterns in the oncoming traffic.

This paper introduces a methodology for estimating interdomain Web traffic lows between all clients worldwide and the ervers belonging to over one housand content providers. The idea is to use the server logs from a large ontent Delivery Network (CDN) to identify client downloads of content provider (i.e., publisher) Web pages. For each of these Web pages, a client typically downloads some objects from the content provider, some from the CDN, and perhaps some from third parties such as banner advertisement agencies. The sizes and sources of the non-CDN downloads associated with each CDN download are estimated separately by examining Web accesses in packet traces collected at several universities.