Next, this work pretends to characterize the RedIRIS’ busy hour given its importance for accurate capacity planning. This challenge is of fundamental im-portance for the ISPs since the quality that their users receive directly depends on the link bandwidth. In general, there are two approaches [vdM06] to meet the Service Level Agreements (SLA): (i) using Integrated Services (Intserv) or Differ-entiated Services (Diffserv) to limit either the number of users in the network or the resources that can be requested, and (ii) overprovisioning the network capacity such that all the users and applications’ requirements are met. In this thesis, we pay special attention to this latter option.
We have found that the two main drawbacks of the most of the current ap-proaches to the capacity planning problem are: (i) The temporal and spatial diversities are ignored, and (ii) such approaches are typically based only on a pri-ori measurements of the demand for capacity. For instance, these approaches use dedicated measurement systems to obtain the bandwidth consumption and, then, the measurements are used to estimate the demand for bandwidth [PNvdMM09].
However, sometimes it is not possible to measure in a given network, for example, a new institution that joins RedIRIS or a subnetwork without a dedicated measure-ment system. In addition, sometimes the problem is to foresee “what happens if”
a certain feature changes. That is, the problem is to deem the variation on the de-mand for network resources by adding new network users, when network topology changes or it is upgraded. If the estimation are based on previous measurements only, then, these questions cannot be addressed.
In this light, this thesis takes one step further and shows how the demand for bandwidth can be estimated by means of the intrinsic features of the networks.
Basically, such features include the number of users and the network access ca-pacity. Thus, given these features, network managers can estimate the demand for bandwidth in their networks.
1.2 Objectives and hypotheses
The overall aim of this work is to show how the internetwork measurements (suf-ficiently representative and appropriately captured, validated and reduced) can
be useful to characterize a facet of the Internet’s behavior as important as the Internet’s traffic busy hour and how it can be estimated from the IP networks’
intrinsic features. Consequently, the following specific hypotheses and objectives are defined:
1. Hypothesis: Traffic measurements gathered from a limited number of net-works and limited duration cannot be considered to be sufficiently represen-tative of the Internet.
Objectives: We pretend to assess to what extend the traffic measurement campaigns must last to obtain stationary internetwork statistics of a given network.
In addition, we pretend to assess if a homogeneous set of networks shows similar behavior with regard to several internetwork statistics.
2. Hypothesis: If the Internet traffic measurement campaigns must last for long periods of time, the volume of data that such campaigns entails can result by itself difficult to analyze, monitor, and store.
Objectives: This objectives comprises several aspects:
− To propose new techniques to subsample Internet traffic measurements.
We focus on the fact that it is well known that such measurements follow the patterns of the human behavior, and, consequently, they show strong periodicity.
− To define an automatic and objective mechanism to identify when the subsampled signal is not longer representative of the original one.
− To validate the proposed approaches with real data that represents measurements and statistics of the Internet traffic using an extensive set of networks during a representative period of time.
− To compare the proposed approaches with previous well-known method-ologies to subsample time-series.
3. Hypothesis: The demand for bandwidth in the busy hour over mid-length periods can be characterized by a stochastic process.
1.2. Objectives and hypotheses 5
Objective: The aim of this objective is to model the traffic volume exchanged during the busy hour over time by means of a stochastic process. This task comprises two subtasks:
− Visual inspection of the data in order to propose a model.
− Validation of such model with real data, in this case measurements from RedIRIS.
4. Hypothesis: The demand for bandwidth during the busy hour over long periods calls for a non-stationary process model.
Objective: This objective includes the inspection of stationary of the demand for bandwidth in the long term, bearing in mind that it is expected that the demand increases over time. Thus, we pretend to assess if such increment is either at a constant rate or, conversely, the demand changes as a staircase function (that is, as a set of consecutive stationary processes).
5. Hypothesis: The demand for bandwidth in low-utilized networks are not polluted by their access capacities. As RedIRIS networks’ utilizations are typically low, we support the hypothesis that access capacities are not “cap-ping” the demand of the users.
Objective: This objective tries to evaluated if the demand for bandwidth is correlated to the access capacity of the networks in an extensive set of network scenarios.
6. Hypothesis: The parameters of the process that models the demand for bandwidth over time can be estimated by means of the networks’ intrinsic features. Consequently, the demand for bandwidth in given a network can be estimate in an objective and fairly fashion, and, even, avoiding to carry out a dedicated measurement campaign.
Objective: Once the busy hour process is modeled by a stochastic process, this objective intends to infer the parameters of such process by means of explanatory variables, specifically, a set of network intrinsic features.
The reader may notice that these specific objectives are treated throughout this Ph.D. thesis and their evaluation is reported at the end of this document.