We then relate this large-time scaling phenomenon to the empirically observed characteristics of WAN traffic at the level of individual connections or applications. We first validate and confirm an earlier finding, originally due to Paxson and Floyd, that actual WAN traffic is consistent with statistical self-similarity for sufficiently large time scales. In this paper, we report on some preliminary results from an in-depth, wavelet-based analysis of a set of high-quality, packet-level traffic measurements, collected over the last 6-7 years from a number of different wide-area networks (WANs). Priority queuing and waiting time limits.Īpplicability in Internet of Things and opportunistic networks. Numerical results of different peakedness of the traffic input flows. Polya Arrival Process, gamma and Pareto distributions.ĭescription and evaluation of Polya/D/1 model. Finally, we express views on openresearch issues for offering optimization in the Internet traffic analyses. All of them demonstrate special moments in the breakdown of the shaping effect. This is done by traffic fractality analyses, codec-dependent resource reallocation and Fibonacci backward difference traffic moments analyses. We map the data from measurements and simulations with the application layer requirements, cross-layer Quality of Service (QoS) and Quality of Experience (QoE) parameters. The behavior of the system at its bounds is shown. During an end-to-end simulation, more complex queuing models with priorities are proposed. A proper analytical description of the end-recipient traffic flows and point process of self-similarity inputs are applied for a better user behavior specification. Polya, Pareto and gamma distributions have the capability to change shape and scale in a way to simulate different types of observed traffic.
In our next analysis, we look at mapping the measured data with the Polya arrival process by Pareto and gamma distributed inter-arrival times. Then, we highlight the self-similar nature of the incoming traffic at network nodes. We start with traffic measurements and obtain accurate data for detail network simulations and precise analysis. The proposed technology for analysis is flexible enough to different types of traffic in opportunistic networks. The aim of this chapter is to present different approaches to network traffic management applicable to the IP, transport and application layers in IP, 3G, WiMAX and 4G technologies. Finally, the influence of the shape of this distribution on the queuing effects is studied. This leads to extending the basic queue model with variable service times, based on the packet size distribution. After the detailed analysis of a basic fixed service time queue, some considerations are made on real networking components. This paper presents and analyzes some qualitative results on the altering of a bin count vector when passing it through a queuing system. Directly describing the effect of queuing systems on the variance-time behavior of this discrete representation of traffic is relatively unexplored terrain. These models often represent packet streams in a discrete way by calculating the bin count vector. In this, there is a contradiction with more recent traffic models capable of capturing the multi-fractal nature of network traffic e.g. Most of these articles employ a continuous representation of network traffic, in the form of timestamps or interarrival times. A large number of research articles are devoted to queuing theory and queuing systems.
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