Dynamic Network Configuration Research Articles

A Survey on Security-Aware Measurement in SDN

Software-defined networking (SDN) is one of the most prevailing networking paradigms in current and next-generation networks. Basically, the highly featured separation of control and data planes makes SDN a proper solution towards many practical problems that challenge legacy networks, for example, energy efficiency, dynamic network configuration, agile network measurement, and flexible network deployment. Although the SDN and its applications have been extensively studied for several years, the research of SDN security is still in its infancy. Typically, the SDN suffers from architecture defect and OpenFlow protocol loopholes such as single controller problem, deficiency of communication verification, and network resources constraint. Hence, network measurement is a fundamental technique of protecting SDN against the above security threats. Specifically, network measurement aims to understand and quantify a variety of network behaviors to facilitate network management and monitoring, anomaly detection, network troubleshooting, and the establishment of security mechanisms. In this paper, we present a systematic survey on security-aware measurement technology in SDN. In particular, we first review the basic architecture of SDN and corresponding security challenges. Then, we investigate two performance measurement techniques in SDN, namely, link latency and available bandwidth measurements. After that, we further provide a general overview of topology measurement in SDN including intradomain and interdomain topology discovering techniques. Finally, we list three interesting future directions of security-aware measurement in SDN followed by giving conclusion remarks.

Auto-configuration of Physical Cell ID in LTE femtocellular systems using Self Organizing Networks

The commercial success of cellular networks, combined with advances in digital electronics, signal processing, and telecommunications research have lead to the design of next generation 4G-based long term evolution (LTE) wireless systems. The key essence of these emerging, LTE cellular systems lie in deployment of multiple femtocells for improved coverage and higher data rates. However, the arbitrary deployment of a wide number of femtocells makes the configuration, management and planning of LTE systems quite complex and challenging. In order to support dynamic and efficient network configuration, every cell needs to be assigned a particular Physical Cell ID (PCID). In this paper we show that the dynamic, optimal PCID allocation problem in LTE systems is NP-complete. Subsequently we provide a near-optimal solution using Self-Organizing Networks which models the problem using new merge operations and explores the search space using a suitable randomized algorithmic approach. We also discuss two feasible options for dynamic auto-configuration of the system and analyze the algorithm to prove its convergence. Simulation results point out that our proposed near-optimal solution dynamically achieves ~85−90 % of global optimal auto-configuration in computationally feasible time.

3G network traffic sources measurement and analysis

ABSTRACTIn this paper, we show statistical analyses of several types of traffic sources in a 3G network, namely voice, video and data sources. For each traffic source type, measurements were collected in order to, on the one hand, gain better understanding of the statistical characteristics of the sources and, on the other hand, enable forecasting traffic behaviour in the network. The latter can be used to estimate service times and quality of service parameters. The probability density function, mean, variance, mean square deviation, skewness and kurtosis of the interarrival times are estimated by Wolfram Mathematica and Crystal Ball statistical tools. Based on evaluation of packet interarrival times, we show how the gamma distribution can be used in network simulations and in evaluation of available capacity in opportunistic systems. As a result, from our analyses, shape and scale parameters of gamma distribution are generated. Data can be applied also in dynamic network configuration in order to avoid potential network congestions or overflows. Copyright © 2013 John Wiley & Sons, Ltd.

Adaptive Access Mechanism for WSNs of Eco-Environments

To solve the data transmission of eco-environment monitor by using WSNs, a new adaptive access mechanism is presented. The adaptive access mechanism senses the channel status information and the events of data transmissions, success or collision in the channel. Based on the measurement of idle slot intervals between two data transmission, nodes adjust parameters of access mechanism, which can adapt to the dynamic network configurations. The analysis and computer simulation demonstrate the validity and good scalability of the proposed access mechanism.

A network-oriented perspective on cardiac calcium signaling

The normal contractile, electrical, and energetic function of the heart depends on the synchronization of biological oscillators and signal integrators that make up cellular signaling networks. In this review we interpret experimental data from molecular, cellular, and transgenic models of cardiac signaling behavior in the context of established concepts in cell network architecture and organization. Focusing on the cellular Ca(2+) handling machinery, we describe how the plasticity and adaptability of normal Ca(2+) signaling is dependent on dynamic network configurations that operate across a wide range of functional states. We consider how (mal)adaptive changes in signaling pathways restrict the dynamic range of the network such that it cannot respond appropriately to physiologic stimuli or perturbation. Based on these concepts, a model is proposed in which pathologic abnormalities in cardiac rhythm and contractility (e.g., arrhythmias and heart failure) arise as a consequence of progressive desynchronization and reduction in the dynamic range of the Ca(2+) signaling network. We discuss how a systems-level understanding of the network organization, cellular noise, and chaotic behavior may inform the design of new therapeutic modalities that prevent or reverse the disease-linked unraveling of the Ca(2+) signaling network.

Autoconfiguration of a Dynamic Nonoverlapping Camera Network

In order to monitor sufficiently large areas of interest for surveillance or any event detection, we need to look beyond stationary cameras and employ an automatically configurable network of nonoverlapping cameras. These cameras need not have an overlapping field of view and should be allowed to move freely in space. Moreover, features like zooming in/out, readily available in security cameras these days, should be exploited in order to focus on any particular area of interest if needed. In this paper, a practical framework is proposed to self-calibrate dynamically moving and zooming cameras and determine their absolute and relative orientations, assuming that their relative position is known. A global linear solution is presented for self-calibrating each zooming/focusing camera in the network. After self-calibration, it is shown that only one automatically computed vanishing point and a line lying on any plane orthogonal to the vertical direction is sufficient to infer the dynamic network configuration. Our method generalizes previous work which considers restricted camera motions. Using minimal assumptions, we are able to successfully demonstrate promising results on synthetic, as well as on real data.

Yeast concentration estimation and prediction with static and dynamic neural network models in batch cultures

The second fermentation is one of the most important steps in Champagne production. For this purpose, yeasts are grown on a wine based medium to adapt their metabolism to ethanol. Several models built with various static and dynamic neural network configurations were investigated. The main objective was to achieve real-time estimation and prediction of yeast concentration during growth. The model selected, based on recurrent neural networks, was first order with respect to the yeast concentration and to the volume of CO2 released. Temperature and pH were included as model parameters as well. Yeast concentration during growth could thus be estimated with an error lower than 3% (±1.7×106 yeasts/ml). From the measurement of initial yeast population and temperature, it was possible to predict the final yeast concentration (after 21 hours of growth) from the beginning of the growth, with about ±3×106 yeasts/ml accuracy. So a predictive control strategy of this process could be investigated.

The Crossover Net LAN system using an intelligent head-end

The development of a multimedia broadband local area network (LAN) system named CrossoverNet using a intelligent head-end is presented. This head-end provides two-way digital/analog communication with dynamic channel exchange and network configuration control. Based on these functions, the head-end exchanges analog information in a manner similar to digital circuit exchange. Digital and analog information are transmitted separately over the same broadband cable and integrated in audiovisual protocol layers. To provide these facilities, a LAN architecture conforming to the Open Systems Interconnection (OSI) basic reference model is designed using broadband and CATV network technologies. The result, CrossoverNet, can control, operate, and manage a variety of audiovisual devices that are attached to it in a consistent manner. A prototype experiment illustrates the versatility of the system.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>