Transport on complex networks: Flow, jamming and optimization

Many transport processes on networks depend crucially on the underlying network geometry, although the exact relationship between the structure of the network and the properties of transport processes remain elusive. In this paper we address this question by using numerical models in which both structure and dynamics are controlled systematically. We consider the traffic of information packets that include driving, searching and queuing. We present the results of extensive simulations on two classes of networks; a correlated cyclic scale-free network and an uncorrelated homogeneous weakly clustered network. By measuring different dynamical variables in the free flow regime we show how the global statistical properties of the transport are related to the temporal fluctuations at individual nodes (the traffic noise) and the links (the traffic flow). We then demonstrate that these two network classes appear as representative topologies for optimal traffic flow in the regimes of low density and high density traffic, respectively. We also determine statistical indicators of the pre-jamming regime on different network geometries and discuss the role of queuing and dynamical betweenness for the traffic congestion. The transition to the jammed traffic regime at a critical posting rate on different network topologies is studied as a phase transition with an appropriate order parameter. We also address several open theoretical problems related to the network dynamics

Internet scalability: properties and evolution

Copyright © 2008 IEEEMatthew Roughan; Steve Uhlig; Walter Willinge

Self-similar traffic and network dynamics

Copyright © 2002 IEEEOne of the most significant findings of traffic measurement studies over the last decade has been the observed self-similarity in packet network traffic. Subsequent research has focused on the origins of this self-similarity, and the network engineering significance of this phenomenon. This paper reviews what is currently known about network traffic self-similarity and its significance. We then consider a matter of current research, namely, the manner in which network dynamics (specifically, the dynamics of transmission control protocol (TCP), the predominant transport protocol used in today's Internet) can affect the observed self-similarity. To this end, we first discuss some of the pitfalls associated with applying traditional performance evaluation techniques to highly-interacting, large-scale networks such as the Internet. We then present one promising approach based on chaotic maps to capture and model the dynamics of TCP-type feedback control in such networks. Not only can appropriately chosen chaotic map models capture a range of realistic source characteristics, but by coupling these to network state equations, one can study the effects of network dynamics on the observed scaling behavior. We consider several aspects of TCP feedback, and illustrate by examples that while TCP-type feedback can modify the self-similar scaling behavior of network traffic, it neither generates it nor eliminates it.Ashok Erramilli, Matthew Roughan, Darryl Veitch and Walter Willinge

Modeling of Nonseasonal Quarterly Earnings Data: Working Paper Series--05-17

We present new empirical evidence on the predictive power of statistically-based quarterly earnings expectation models for firms which exhibit nonseasonal quarterly earnings patterns. In marked contrast to extant work we find: 1) a considerably greater frequency of nonseasonal firms (36%) when compared to Lorek and Bathke (1984) (12%) and Brown and Han (2000) (17%), 2) the random walk model (RW) provides significantly more accurate pooled, one-step ahead quarterly earnings predictions across 40 quarters in the 1994-2003 holdout period than the first-order autoregressive model (AR1) popularized by Lorek and Bathke and Brown and Han, and 3) the RW model provides significantly more accurate quarterly earnings predictions for large nonseasonal firms than smaller nonseasonal firms. The latter finding documents a size-effect with respect to predictive ability for nonseasonal firms similar to that evidenced for seasonal firms. These findings are particularly salient to researchers in search of efficient statistically-based quarterly earnings expectation models since 129 of 296 (43.6%) sample firms are not covered by security analysts

The Dynamics of Internet Traffic: Self-Similarity, Self-Organization, and Complex Phenomena

The Internet is the most complex system ever created in human history. Therefore, its dynamics and traffic unsurprisingly take on a rich variety of complex dynamics, self-organization, and other phenomena that have been researched for years. This paper is a review of the complex dynamics of Internet traffic. Departing from normal treatises, we will take a view from both the network engineering and physics perspectives showing the strengths and weaknesses as well as insights of both. In addition, many less covered phenomena such as traffic oscillations, large-scale effects of worm traffic, and comparisons of the Internet and biological models will be covered.Comment: 63 pages, 7 figures, 7 tables, submitted to Advances in Complex System

Site-specific ionisation edge fine-structure of Rutile in the electron microscope

Combined Bloch-wave and density functional theory simulations are performed to investigate the effects of different channelling conditions on the fine-structure of electron energy-loss spectra. The simulated spectra compare well with experiments. Furthermore, we demonstrate that using this technique, the site-specific investigation of atomic orbitals is possible. This opens new possibilities for chemical analyses

Power-law distributions in empirical data

Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution -- the part of the distribution representing large but rare events -- and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.Comment: 43 pages, 11 figures, 7 tables, 4 appendices; code available at http://www.santafe.edu/~aaronc/powerlaws

Investigation of Electrocatalysts Produced by a Novel Thermal Spray Deposition Method

Common methods to produce supported catalysts include impregnation, precipitation, and thermal spray techniques. Supported electrocatalysts produced by a novel method for thermal spray deposition were investigated with respect to their structural properties, elemental composition, and electrochemical performance. This was done using electron microscopy, X-ray photoelectron spectroscopy, and cyclic voltammetry. Various shapes and sizes of catalyst particles were found. The materials exhibit different activity towards oxidation and reduction of Fe. The results show that this preparation method enables the selection of particle coverage as well as size and shape of the catalyst material. Due to the great variability of support and catalyst materials accessible with this technique, this approach is a useful extension to other preparation methods for electrocatalysts

Highly optimized tolerance and power laws in dense and sparse resource regimes

Power law cumulative frequency $(P)$ vs. event size $(l)$ distributions $P(\geq l)\sim l^{-\alpha}$ are frequently cited as evidence for complexity and serve as a starting point for linking theoretical models and mechanisms with observed data. Systems exhibiting this behavior present fundamental mathematical challenges in probability and statistics. The broad span of length and time scales associated with heavy tailed processes often require special sensitivity to distinctions between discrete and continuous phenomena. A discrete Highly Optimized Tolerance (HOT) model, referred to as the Probability, Loss, Resource (PLR) model, gives the exponent $\alpha=1/d$ as a function of the dimension $d$ of the underlying substrate in the sparse resource regime. This agrees well with data for wildfires, web file sizes, and electric power outages. However, another HOT model, based on a continuous (dense) distribution of resources, predicts $\alpha= 1+ 1/d$. In this paper we describe and analyze a third model, the cuts model, which exhibits both behaviors but in different regimes. We use the cuts model to show all three models agree in the dense resource limit. In the sparse resource regime, the continuum model breaks down, but in this case, the cuts and PLR models are described by the same exponent.Comment: 19 pages, 13 figure