Web Traces Research Articles

Traces of a limnic food web in the Eocene Lake Messel — a preliminary report based on fish coprolite analyses

The extraordinary fossil preservation of a wide variety of organisms (i.e. algae, arthropods, vertebrates) in the Eocene Lake Messel locality partly allows the reconstruction of an autochthonous food web. Investigations on over 150 fish coprolites from different stratigraphic horizons reveal for the first time cuticular remains of different arthropod taxa including Branchiopod Crustacea (Cladocera, Conchostraca) and Nematocere Diptera (Culicidae, Chaoboridae). The picture of the “hostile” Lake Messel must be revised with these new finds, as there is now evidence for diverse and abundant autochthonous life in the upper water layers. The organisms identified here were part of a complex food web, and thus close the gap between primary producers and fish in the Lake Messel ecosystem. By coprolite analysis, the food web reconstruction presented here is based on evidence of actually eaten oragnisms. Primary producers were pelagic, floating or benthic algae, the latter growing on a substrate (Messel Waterlily and other higher plants). A number of Arthropoda described here (e.g. Culicidae) fed on these algae as primary consumers. Second level consumers include Chaoboridae (described here for Messel for the first time) and Heteroptera. Juvenile and small fish, which in turn were captured by adult predatory fish, crocodiles, turtles, and piscivore mammals represent higher order trophic levels.

Web traffic demand forecasting using wavelet‐based multiscale decomposition

In this paper we propose an experimental forecasting strategy taking into account the long-range dependence of aggregate network traffic, and we apply it to provide one-minute-ahead World-Wide Web (Web) traffic demand forecasts in terms of average number of bytes transferred. Recently, statistical examination of Web traces have shown evidence that Web traffic arising from file transfers exhibits a behavior that is consistent with the notion of self-similarity. Essentially, self-similarity indicates that significant burstiness is present on a wide range of time scales (i.e., the process is long-range dependent). Hence the idea of exploiting this multiscale property with a view towards discovering and capturing regularities underlying the time series which may prove useful for short-term traffic load forecasting. We carry out a wavelet transform decomposition of the original series to decompose the traffic time series into varying scales of temporal resolution, with the aim of making the underlying temporal structures more tractable. In a second step, each individual wavelet series—supposed to capture some features of the series—is fitted with a dynamical recurrent neural network (DRNN) model to output the wavelet forecast. The latter are afterwards recombined to form the next-minute Web Traffic demand. The method is applied on a large set of HTTP logs and is shown to yield good results. © 2001 John Wiley & Sons, Inc.

Web traffic demand forecasting using wavelet‐based multiscale decomposition

In this paper we propose an experimental forecasting strategy taking into account the long-range dependence of aggregate network traffic, and we apply it to provide one-minute-ahead World-Wide Web (Web) traffic demand forecasts in terms of average number of bytes transferred. Recently, statistical examination of Web traces have shown evidence that Web traffic arising from file transfers exhibits a behavior that is consistent with the notion of self-similarity. Essentially, self-similarity indicates that significant burstiness is present on a wide range of time scales (i.e., the process is long-range dependent). Hence the idea of exploiting this multiscale property with a view towards discovering and capturing regularities underlying the time series which may prove useful for short-term traffic load forecasting. We carry out a wavelet transform decomposition of the original series to decompose the traffic time series into varying scales of temporal resolution, with the aim of making the underlying temporal structures more tractable. In a second step, each individual wavelet series—supposed to capture some features of the series—is fitted with a dynamical recurrent neural network (DRNN) model to output the wavelet forecast. The latter are afterwards recombined to form the next-minute Web Traffic demand. The method is applied on a large set of HTTP logs and is shown to yield good results. © 2001 John Wiley & Sons, Inc.

The traces of the smallest spider's web

The traces of the smallest spider's web

Modeling and simulation of broadband satellite networks. II. Traffic modeling

For p.I see ibid., vol.37, no.3, p.72-9 (1999). Traffic models for satellite network simulation must cover a broad range of traffic types and characteristics because the type of users/terminals that will access a satellite channel will range from a single home user (low traffic aggregation) to Internet backbone nodes (very high traffic aggregation). In addition, since satellite resources are generally considered more scarce than their fiber counterparts, the accuracy and practicality of traffic models play a crucial role. We address these challenges by identifying three traffic modeling areas for satellite network simulation, and propose an effective and flexible traffic model for each area: a discrete autoregressive process for MBone video source modeling; the superposition of fractal renewal processes (Sup-FRP) model for Web request arrivals; and a generalized shot-noise-driven Poisson point process (GSNDP) for aggregate traffic flows modeling. The efficacy of the first two models is demonstrated based on statistical analysis of various video and Web traces. These simulation-oriented models provide accurate descriptions of source traffic at various levels of traffic aggregation, and thus enable satellite network researchers and practitioners to understand key network design and engineering issues such as QoS provisioning based on simulation.