This thesis is organized as follows:
− Chapter 2 presents the state-of-the-art. Firstly, we show literature that ana-lyzes NRENs. Secondly, we focus on network monitoring tools that we have used along the thesis. Next, we present some different Internet traffic mod-els that the research community has proposed to characterize the Internet.
Finally, we show how some of these models have been applied to the capacity planning problem.
− Chapter 3 analyzes the popularity of the IP addresses and port number focusing in two aspects of the Internet’s characterization: the spatial and temporal diversities, which have usually been ignored. In this light, this chapter compares the behavior of universities with similar characteristics during several months. Moreover, we also explain why both spatial and temporal diversities occur. In addition, this chapter presents the measure-ment scenario that is analyzed throughout this thesis. Essentially, it includes a description of the RedIRIS’ architecture and measurement systems as well as the validation of the data.
− Chapter 4 analyzes the geolocation of the Internet traffic destinations paying also special attention to its spatial and temporal diversities.
− Chapter 5 deals with the traffic measurements reduction problem and pro-poses a new method to subsample network measurements over time based on the Multiresolution Analysis with wavelets.
− Chapter 6 analyzes the dynamics of the Internet traffic busy hour due to its importance in the capacity planning problem. Several goodness-of-fit tests are applied to an extensive set of RedIRIS’ measurements, concluding that a Gaussian distribution can model the Internet traffic busy hour. In addition, we further study the impact of the intrinsic features on the demands for bandwidth at the Internet traffic busy hour.
− Chapter 7 presents the conclusions that can be drawn from this thesis and proposes some directions for future research lines.
1.3. Thesis structure 7
Figure 1.1 shows the relation between the different contents that make up this work and the layout of the chapters.
(Chapter 2)
Distance Measures:
Network Techniques to Goodnessoffit Internetwork Traffic
Euclidean distance
Figure 1.1: Structure of the document
Chapter 2
State of the art
This chapter presents a general background of the most important is-sues that this work deals with and it is useful to justify the decisions taken along the research process. Specifically, we focus on the following items:
− Studies about other academic networks. In this section we analyze research on networks similar to RedIRIS since its measurements are an essential part of this thesis.
− Network monitoring tools, which serve to provide an accurate cap-ture process and deal with the huge amount of data that the traf-fic measurements imply. We have paid special attention to the Cisco’s Netflow and Multi Router Traffic Grapher logs because RedIRIS is currently using these tools.
− Data reduction. We present different proposed approaches that aim at reducing the volume of data for further processing and storage while still preserving relevant information.
− Traffic characterization. In this section we present several models to characterize the Internet traffic that the research community has proposed.
− Network Dimensioning. In this last section we study proposed methodologies to dimension networks as well as techniques to pre-dict the evolution of the Internet.
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2.1 National Research and Education Networks
The academic networks have received relatively much attention by the research community. Basically, this is due to the current regulation that usually prevents the research community from sharing actual traffic measurements and even cap-turing traffic in commercial networks [JTO09]. As a consequence and due to the greater accessibility of the academic networks, the researchers have typically used measurements from their own institutions which are normally academic networks.
As this thesis analyzes measurement from an extensive set of campus networks, this section presents some previous studies based on academic networks’ measure-ments.
On the one hand, there are many research studies that have used measure-ments from academic networks to evaluate new algorithms, systems, architec-tures or applications’ performance. Examples of this are the MEHARI and MIRA projects [LASP+99, RGMG+02]. In particular, MIRA is a distributed and scal-able real-time measurement platform and MEHARI is a low-cost programmscal-able and scalable tool for the analysis of IP/ATM traffic. The authors tested both projects using RedIRIS’ network and some details of the RedIRIS’ infrastruc-ture can be found in these works. Moreover, measurements from academic net-works have proven useful to validate traffic classification methodologies. Such methodologies try to identify the applications that compose the traffic generated by a network. Examples of that are the community research’s efforts to detect Skype [BMM+07, BMM+09] and to classify the traffic into different application groups [CEBRSP].
In addition, other studies have shown the current state of a given academic net-work in terms of bandwidth, number of nodes and number of centers connected;
however, these studies neither explain the link capacity planning nor compare the behavior between the institutions that make up the network. For instance, in [Lia93, MKYK89, Mat87] the characteristics of the Japan and China’s aca-demic networks are shown. However the authors do not provide any conclusion or methodology for capacity planning. Similarly, the academic network of Slovenia is explained in detail in [JBOK03]. Specifically, the authors introduce the net-work topology, netnet-work technology, national and international connectivity, and services that are available in the network.