When should I use network emulation ?

The design and development of a complex system requires an adequate methodology and efficient instrumental support in order to early detect and correct anomalies in the functional and non-functional properties of the tested protocols. Among the various tools used to provide experimental support for such developments, network emulation relies on real-time production of impairments on real traffic according to a communication model, either realistically or not. This paper aims at simply presenting to newcomers in network emulation (students, engineers, ...) basic principles and practices illustrated with a few commonly used tools. The motivation behind is to fill a gap in terms of introductory and pragmatic papers in this domain. The study particularly considers centralized approaches, allowing cheap and easy implementation in the context of research labs or industrial developments. In addition, an architectural model for emulation systems is proposed, defining three complementary levels, namely hardware, impairment and model levels. With the help of this architectural framework, various existing tools are situated and described. Various approaches for modeling the emulation actions are studied, such as impairment-based scenarios and virtual architectures, real-time discrete simulation and trace-based systems. Those modeling approaches are described and compared in terms of services and we study their ability to respond to various designer needs to assess when emulation is needed

The weight and density of carbon nanotubes versus the number of walls and diameter

The weight and density of carbon nanotubes are calculated as a function of their characteristics (inner diameter, outer diameter, and number of walls). The results are reported in the form of diagrams which may be useful to other researchers, in particular in the fields of synthesis/production, materials and composites, health/toxicity studies

Optimization of TFRC loss history initialization

This letter deals with the initialization of the loss history structure in the TFRC (TCP-Friendly Rate Control) mechanism. This initialization occurs after the detection of the first loss event after every slowstart phase. The loss history is crucial for the algorithm since it returns the packet loss rate estimation. This estimation is used in the TFRC equation to compute the sending rate. In this letter, we propose a new method to compute the packet loss rate which is more computationally efficient and remains as accurate as the classical commonly used method. The motivation of this work is to reduce the computation time and formulate a unified computation scheme. This method is based on the Newton’s algorithm issued from numerical analysis of the TCP throughput equation. This proposal is evaluated analytically and the results show a significant improvement in terms of the computation time

On the Rapid Increase of Intermittency in the Near-Dissipation Range of Fully Developed Turbulence

Intermittency, measured as log(F(r)/3), where F(r) is the flatness of velocity increments at scale r, is found to rapidly increase as viscous effects intensify, and eventually saturate at very small scales. This feature defines a finite intermediate range of scales between the inertial and dissipation ranges, that we shall call near-dissipation range. It is argued that intermittency is multiplied by a universal factor, independent of the Reynolds number Re, throughout the near-dissipation range. The (logarithmic) extension of the near-dissipation range varies as \sqrt(log Re). As a consequence, scaling properties of velocity increments in the near-dissipation range strongly depend on the Reynolds number.Comment: 7 pages, 7 figures, to appear in EPJ

GTFRC, a TCP friendly QoS-aware rate control for diffserv assured service

This study addresses the end-to-end congestion control support over the DiffServ Assured Forwarding (AF) class. The resulting Assured Service (AS) provides a minimum level of throughput guarantee. In this context, this article describes a new end-to-end mechanism for continuous transfer based on TCP-Friendly Rate Control (TFRC). The proposed approach modifies TFRC to take into account the QoS negotiated. This mechanism, named gTFRC, is able to reach the minimum throughput guarantee whatever the flow’s RTT and target rate. Simulation measurements and implementation over a real QoS testbed demonstrate the efficiency of this mechanism either in over-provisioned or exactly-provisioned network. In addition, we show that the gTFRC mechanism can be used in the same DiffServ/AF class with TCP or TFRC flows

Fast Computation of Fourier Integral Operators

We introduce a general purpose algorithm for rapidly computing certain types of oscillatory integrals which frequently arise in problems connected to wave propagation and general hyperbolic equations. The problem is to evaluate numerically a so-called Fourier integral operator (FIO) of the form $\int e^{2\pi i \Phi(x,\xi)} a(x,\xi) \hat{f}(\xi) \mathrm{d}\xi$ at points given on a Cartesian grid. Here, $\xi$ is a frequency variable, $\hat f(\xi)$ is the Fourier transform of the input $f$, $a(x,\xi)$ is an amplitude and $\Phi(x,\xi)$ is a phase function, which is typically as large as $|\xi|$; hence the integral is highly oscillatory at high frequencies. Because an FIO is a dense matrix, a naive matrix vector product with an input given on a Cartesian grid of size $N$ by $N$ would require $O(N^4)$ operations. This paper develops a new numerical algorithm which requires $O(N^{2.5} \log N)$ operations, and as low as $O(\sqrt{N})$ in storage space. It operates by localizing the integral over polar wedges with small angular aperture in the frequency plane. On each wedge, the algorithm factorizes the kernel $e^{2 \pi i \Phi(x,\xi)} a(x,\xi)$ into two components: 1) a diffeomorphism which is handled by means of a nonuniform FFT and 2) a residual factor which is handled by numerical separation of the spatial and frequency variables. The key to the complexity and accuracy estimates is that the separation rank of the residual kernel is \emph{provably independent of the problem size}. Several numerical examples demonstrate the efficiency and accuracy of the proposed methodology. We also discuss the potential of our ideas for various applications such as reflection seismology.Comment: 31 pages, 3 figure