This contribution concentrates on dynamic routing in WDM (Wavelength Division Multiplexing) networks. It is shown that a strategy based on precalculated alternatives and an adaptive dynamic path search performs very well over a wide load range. Moreover, specific effects for the impact of resource allocation strategies in photonic WDM networks are highlighted, especially the influence of wavelength converter usage strategies in networks with partial conversion. The paper also investigates the way non-Poisson traffic behaviour affects performance of routing strategies and presents how results from dynamic routing investigation can help to optimize the network planning process.

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this document, several methods of AC for stream traffic based on declared parameters and using bufferless multiplexing are proposed

In this paper, we present a synthetic traffic model for the Universal Mobile Telecommunication Systems (UMTS) based on measured trace data. The analysis and scaling process of the measured trace data with respect to different bandwidth classes constitutes the basic concept of the UMTS traffic characterization. Furthermore, we introduce an aggregated traffic model for UMTS networks that is analytically tractable. The key idea of this aggregated traffic model lies in customizing the batch Markovian arrival process (BMAP) such that different packet sizes of IP packets are represented by rewards (i.e., batch sizes of arrivals) of the BMAP. The effectiveness of the customized BMAP for modeling UMTS traffic is illustrated using the synthetic traffic model previously presented.

This paper deals with analysis and modelling of real Internet dial-up traffic based on extensive data, which are described and analysed. When recording the traffic process the time axis is digitalised and analysis and modelling must take this into account. In particular, this is very important for arrival processes. It is also important to distinguish between fast variations within short time intervals and slow variations during the day. The detailed analysis shows that the arrival process is a Poisson process with slow variations.

In the present days of electronic media, more and more people are using to Internet access for their official, personal, entertainment or other related works. Besides expanding services through broadband, a majority of people are still using modem based dialup connections for availing the Internet connectivity at their homes or organizations. There are many Internet service providers grooming in the market causing inherent competition in business. Due to high demand, users are growing drastically day-by-day creating excess traffic load and congestion on the dialup network. Service providers (or operators) are offering attractive packages to improve upon their customer-base as a part of marketing strategies. This paper presents a stochastic analysis of Internet traffic distribution among operators in light of 'Hold-on ' and 'Pull-Back' marketing strategies along with controlled usual parameters like first-choice and quality-of-service. It is observed that traffic distribution of different categories of users is influenced by the new marketing strategies. The first choice also depends on marketing plans and network blocking probabilities. Simulation study is performed with the help of a Markov chain model to examine the interrelationship among various market related parameters and traffic distribution.

Data originating from non-voice sources is expected to play an increasingly important role in the next generation mobile communication services. To plan these networks, a detailed understanding of their traffic load is essential. Recent experimental studies have shown that network traffic originating from data applications can be self-similar, leading to a different queueing behavior than predicted by conventional traffic models. Heavy tailed probability distributions are appropriate for capturing this property, but including those random processes in a performance analysis makes it difficult and often impossible to find numerical results. In this thesis three related topics are addressed: It is shown that Markovian models with a large state space can be used to describe traffic which is self-similar over a large time scale, a Maximum Likelihood approach to fit parallel Erlang-k distributions directly to time series is developed, and the performance of a channel assignment procedure in a wireless communication network is evaluated using the

Internet access traffic follows hourly patterns that depend on various factors, such as the periods users stay in the access point (e.g. at home or in the office) or their preferences for applications. The clustering of Internet users may provide important information for traffic engineering and tariffing. For example, it can be used to set up service differentiation according to hourly behavior, resource optimization based on multi-hour routing and definition of tariffs that promote Internet access in low busy hours. In this work, we identify patterns of similar behavior by grouping Internet users of two distinct Por-tuguese ISPs, one using a CATV access network and the other an ADSL one and offering distinct traffic contracts. The grouping of the users is based on their traffic utilization measured every half-hour. Cluster analysis is used to identify the relevant Internet usage profiles, with the partitioning around medoids and Ward’s method being the preferred clustering methods. For the two data sets, these clustering methods lead to 3 clusters with similar hourly traffic utilization profiles. The cluster structure is validated through discriminant analysis. Having identified the clusters, the type of applications used as well as the flow duration and transfer rate are analyzed for each cluster resulting in coherent outcomes.