Internet usage has changed, and the demands on the broadband access networks have increased, both regarding bandwidth and QoS. Characterizing the traffic, as seen by an broadband access network, can help understanding both the demands of today and the demands of tomorrow. In this paper we analyze traffic measurements from a Swedish municipal broadband access network and derive corresponding user behavior models. The paper focuses on Internet usage in terms of traffic patterns, volumes and applications. Also, user activity characteristics, as session lengths and traffic rate distributions, are analyzed and modelled. Notably, the resulting models for user session lengths turn out different than traditionally assumed.

Abstract-Measurements and numerical simulations of the noise statistics after a semiconductor optical amplifier (SOA) demonstrate nonlinear noise redistribution. The redistribution, which relies on self-modulation due to gain saturation and carrier dynamics, shows a strong power and bandwidth dependence and can be important for SOA-based regenerators.

Video content, of which YouTube is a major part, constitutes a large share of residential Internet traffic. In this paper, we analyse the user demand patterns for YouTube in two metropolitan access networks with more than 1 million requests over three consecutive weeks in the first network and more than 600,000 requests over four consecutive weeks in the second network.In particular we examine the existence of "local interest communities", i.e. the extent to which users living closer to each other tend to request the same content to a higher degree, and it is found that this applies to (i) the two networks themselves; (ii) regions within these networks (iii) households with regions and (iv) terminals within households. We also find that different types of access devices (PCs and handhelds) tend to form similar interest communities.It is also found that repeats are (i) "self-generating" in the sense that the more times a clip has been played, the higher the probability of playing it again, (ii) "long-lasting" in the sense that repeats can occur even after several days and (iii) "semiregular" in the sense that replays have a noticeable tendency to occur with relatively constant intervals.The implications of these findings are that the benefits from large groups of users in terms of caching gain may be exaggerated, since users are different depending on where they live and what equipment they use, and that high gains can be achieved in relatively small groups or even for individual users thanks to their relatively predictable behaviour.

High-quality video is being increasingly delivered over Internet Protocol networks, which means that network operators and service providers need methods to measure the quality of experience (QoE) of the video services. In this paper, we propose a method to speed up the development of no-reference bitstream objective metrics for estimating QoE. This method uses full-reference objective metrics, which makes the process significantly faster and more convenient than using subjective tests. In this process, we have evaluated six publicly available full-reference objective metrics in three different databases, the EPFL-PoliMI database, the HDTV database, and the Live Video Wireless database, all containing transmission distortions in H.264 coded video. The objective metrics could be used to speed up the development process of no-reference real-time video QoE monitoring methods that are receiving great interest from the research community. We show statistically that the full-reference metric Video Quality Metric (VQM) performs best considering all the databases. In the EPFL-PoliMI database, SPATIAL MOVIE performed best and TEMPORAL MOVIE performed worst. When transmission distortions are evaluated, using the compressed video as the reference provides greater accuracy than using the uncompressed original video as the reference, at least for the studied metrics. Further, we use VQM to train a lightweight no-reference bitstream model, which uses the packet loss rate and the interval between instantaneous decoder refresh frames, both easily accessible in a video quality monitoring system.