Panel II: Public Appropriation of Private Rights: Pursuing Internet Copyright Violators

It seems to me that the story of music on the Internet over the past five or six years is the story of two fantasies colliding. The first fantasy is that information wants to be free, that with the Internet we can throwaway all the bottles and just have the wine and the free flow of data, which apparently was generated from somewhere and then circulated forever. So, there was that fantasy, that we would not need copyright anymore because everything would be available to everyone. The other fantasy is the record companies\u27 fantasy of perfect control, that there would be some way to control every use, every copy, of music that was digital

Experimental Methodologies for Large-Scale Systems: a Survey

International audienceThe increasing complexity of available infrastructures with specific features (caches, hyperthreading, dual core, etc.) or with complex architectures (hierarchical, parallel, distributed, etc.) makes it extremely difficult to build analytical models that allow for a satisfying prediction. Hence, it raises the question on how to validate algorithms if a realistic analytic analysis is not possible any longer. As for some many other sciences, the one answer is experimental validation. Nevertheless, experimentation in Computer Science is a difficult subject that today still opens more questions than it solves: What may an experiment validate? What is a ''good experiment''? How to build an experimental environment that allows for "good experiments"? etc. In this paper we will provide some hints on this subject and show how some tools can help in performing ''good experiments'', mainly in the context of parallel and distributed computing. More precisely we will focus on four main experimental methodologies, namely in-situ (real-scale) experiments (with an emphasis on PlanetLab and Grid'5000), Emulation (with an emphasis on Wrekavoc) benchmarking and simulation (with an emphasis on SimGRID and GridSim). We will provide a comparison of these tools and methodologies from a quantitative but also qualitative point of view

A statistical model of internet traffic.

PhDWe present a method to extract a time series (Number of Active Requests (NAR)) from web cache logs which serves as a transport level measurement of internet traffic. This series also reflects the performance or Quality of Service of a web cache. Using time series modelling, we interpret the properties of this kind of internet traffic and its effect on the performance perceived by the cache user. Our preliminary analysis of NAR concludes that this dataset is suggestive of a long-memory self-similar process but is not heavy-tailed. Having carried out more in-depth analysis, we propose a three stage modelling process of the time series: (i) a power transformation to normalise the data, (ii) a polynomial fit to approximate the general trend and (iii) a modelling of the residuals from the polynomial fit. We analyse the polynomial and show that the residual dataset may be modelled as a FARIMA(p, d, q) process. Finally, we use Canonical Variate Analysis to determine the most significant defining properties of our measurements and draw conclusions to categorise the differences in traffic properties between the various caches studied. We show that the strongest illustration of differences between the caches is shown by the short memory parameters of the FARIMA fit. We compare the differences revealed between our studied caches and draw conclusions on them. Several programs have been written in Perl and S programming languages for this analysis including totalqd.pl for NAR calculation, fullanalysis for general statistical analysis of the data and armamodel for FARIMA modelling

Mice and elephants visualization of Internet traffic

Abstract. Internet traffic is composed of flows, sets of packets being transferred from one computer to another. Some visualizations for understanding the set of flows at a busy internet link are developed. These show graphically that the set of flows is dominated by a relatively few “elephants”, and a very large number of “mice”. It also becomes clear that “representative sampling” from heavy tail distributions is a challenging task