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The paradigm of having everyone and everything connected in an ubiquitous way poses huge challenges to today's networks due to the massive traffic volumes involved. To turn treatable all network tasks requiring traffic analysis, sampling the traffic has become mandatory triggering substantial research in the area. Aiming at fostering the deployment and tuning of new sampling techniques, this paper presents a flexible sampling framework developed following a multilayer design in order to easily set up the characteristics of a sampling technique according to the measurement task to be assisted. The framework implementation relies on a comprehensive sampling taxonomy which identifies the granularity, selection scheme and selection trigger as the inner characteristics distinguishing current sampling proposals. As proof of concept of the versatility of this framework in testing the suitability of distinct sampling schemes, this work provides a comparative performance evaluation of classical and recent sampling techniques regarding the estimation accuracy, the volume of data involved in the sampling process and the computational weight in terms of CPU and memory usage.

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Understanding network workload through the characterization of network flows, being essential for assisting network management tasks, can benefit largely from traffic sampling as long as an accurate snapshot of network behavior is captured. This paper is devoted to evaluate the real applicability of using sampling to support flow analysis. Considering both classical and emerging sampling techniques, a comparative performance study is carried out to assess the accuracy of estimating flow parameters through sampling. After identifying the main building blocks of sampled-based measurements, a sampling framework has been implemented to provide a versatile and fair platform for carrying out the testing and comparison process. Through an encompassing coverage of representative sampling techniques, the present study aims to provide useful insights regarding the use of sampling in traffic flow analysis.

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Rendering photorealistic images is a costly process which can take up to several days in the case of high quality images. In most cases, the task of sampling the incident radiance function to evaluate the illumination integral is responsible for an important share of the computation time. Therefore, to reach acceptable rendering times, the illumination integral must be evaluated using a limited set of samples. Such a restriction raises the question of how to obtain the most accurate approximation possible with such a limited set of samples. One must thus ensure that sampling produces the highest amount of information possible by carefully placing and weighting the limited set of samples. Furthermore, the integral evaluation should take into account not only the information brought by sampling but also possible information available prior to sampling, such as the integrand smoothness. This idea of sparse information and the need to fully exploit the little information available is present throughout this book. The presented methods correspond to the state-of-the-art solutions in computer graphics, and take into account information which had so far been underexploited (or even neglected) by the previous approaches. The intended audiences are Ph.D. students and researchers in the field of realistic image synthesis or global illumination algorithms, or any person with a solid background in graphics and numerical techniques. Table of Contents: Introduction / Spherical Fibonacci Point Sets for QMC Estimates of Illumination Integrals / Bayesian Monte Carlo for Global Illumination / Bibliography / Authors' Biographies Copyright © 2015 by Morgan & Claypool.

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