**2. Network planning**

The planning task is fundamental to optimize resource utilization. The Fig. 1 describes, from an industry point of view, a complete feasible telecommunications planning cycle. The inputs are the service demands, described as all type of products/services needs per region and also per customer. The physical and logical inventory are very important to be accurate in terms of transmission mediums such as fiber or radio, demographic dispersion, network elements complete description, management assets, and other important physical and logical information.

In terms of innovation, the approach is to use new technologies to achieve new degrees of service delivery; this function shall be used as a complement for planning and forecasting purposes. Other very important function is the economic variables to calculate the return of the investments (ROI) and all other related costs (fixed and variable). All information about traffic usage will be collected and sampled depending on the nature of the service and will have a fast track for immediate operations and decision-making, normally every 5 minutes. For long-term planning these samples will be aggregate in hours, days and weeks.

The functions in Figure 1, in terms of long term capacity will be used to achieve the capacity to deliver new services allowing network expansion related to the inputs, generating new routing and topology and other capacity needs, as described in Figure 1. The traffic engineering function is used in real-time, under human supervision, sometimes even when some modification in terms of routing is proposed by an algorithm. Sometimes, this could not be feasible in practice because network stability is more important in operational environments (Carvalho et al., 2009; Evans & Filsfils, 2007).

The peering agreements will be done as a function of the outputs and also observing the commercial issues. In this way, many service providers have a peering committee to approve new peering interconnections, which has not only a technical importance as well as a marketing approach. The capacity outputs will generate purchasing activities; this will be done by an engineering implementation function. The main objective is to have an operational network, providing all kind of facilities and desirable services.

Along with the massive growth of the Internet and other applications, an increasing demand for different kinds of services for packet switching networks is important. Nowadays, these networks are expected to deliver audio and video transmissions with quality as good as that of a circuit switching network. In order to make it possible, the network must offer high quality services when it comes to bandwidth provisioning, delay, jitter and packet loss.

engineering task is valuable to optimize the network resources such as links, routing and processing capacity. One important issue in the traffic engineering task is that the capacity planning forecasting may be for medium long periods (or more than one year), due the fact is not easy to increase long distance link capacities in small periods of time. This problem is much more valuable when the coverage area income is not proportional to the area, as in countries like Brazil, China, Russia in which large areas not

The planning task is fundamental to optimize resource utilization. The Fig. 1 describes, from an industry point of view, a complete feasible telecommunications planning cycle. The inputs are the service demands, described as all type of products/services needs per region and also per customer. The physical and logical inventory are very important to be accurate in terms of transmission mediums such as fiber or radio, demographic dispersion, network elements complete description, management assets, and other important physical and

In terms of innovation, the approach is to use new technologies to achieve new degrees of service delivery; this function shall be used as a complement for planning and forecasting purposes. Other very important function is the economic variables to calculate the return of the investments (ROI) and all other related costs (fixed and variable). All information about traffic usage will be collected and sampled depending on the nature of the service and will have a fast track for immediate operations and decision-making, normally every 5 minutes. For long-term planning these samples will be aggregate in hours, days and

The functions in Figure 1, in terms of long term capacity will be used to achieve the capacity to deliver new services allowing network expansion related to the inputs, generating new routing and topology and other capacity needs, as described in Figure 1. The traffic engineering function is used in real-time, under human supervision, sometimes even when some modification in terms of routing is proposed by an algorithm. Sometimes, this could not be feasible in practice because network stability is more important in operational

The peering agreements will be done as a function of the outputs and also observing the commercial issues. In this way, many service providers have a peering committee to approve new peering interconnections, which has not only a technical importance as well as a marketing approach. The capacity outputs will generate purchasing activities; this will be done by an engineering implementation function. The main objective is to have an

Along with the massive growth of the Internet and other applications, an increasing demand for different kinds of services for packet switching networks is important. Nowadays, these networks are expected to deliver audio and video transmissions with quality as good as that of a circuit switching network. In order to make it possible, the network must offer high quality services when it comes to bandwidth provisioning, delay,

environments (Carvalho et al., 2009; Evans & Filsfils, 2007).

operational network, providing all kind of facilities and desirable services.

necessarily economically attractive.

**2. Network planning** 

logical information.

jitter and packet loss.

weeks.

Fig. 1. Telecommunications Industry Planning Process. Adapted from (De Deus, 2007; Evans & Filsfils, 2007).

The processes of traffic characterization and modelling are very important points of a good network project. A precise traffic modelling may allow the understanding of a physical network problem as a mathematical problem whose solution may be simpler. For example, the use of traffic theory suggests that mathematical models can explain, at least for some confidence degrees, the relationship between traffic performance and network capacity (De Deus, 2007; Fonseca, 2005).

The next sections will provide an example on a 3G network using traffic samples to study the planning and project deployment phases. The network described in our study runs with more than 1 million attached 3G costumers with national coverage. In this network, we collected traffic in July 2009 in three different locations (Leblon, Barra da Tijuca and Centro) in Rio de Janeiro. In this way, the first step was to classify the traffic per application. The second step was to characterize the traffic using a procedure based on selfsimilarity (Clegg, 2005) or multifractal analysis (Carvalho et al., 2009). These results were used as basis for proposing a method to manage the traffic in the network.

To manage the traffic demands, we deployed a traffic engineering concept that divides the traffic across the network through tunnels. The bandwidth was monitored and in the observed period, we collected metrics that were used as inputs to decide how to configure new parameters that may fit the incoming needs. An ILEC (incumbent local exchange

IP and 3G Bandwidth Management Strategies Applied to Capacity Planning 33

represent the inter-packet times. The Erlang model, broadly used in telephony systems has been successfully used for capacity planning for many years and is based in the premise that

The Poisson model was considered accurate in the early years of the packet switched networks and was heavily used for capacity planning. In the early 90's, the work of Leland(Leland et al., 1994) proved that the behavior of the Ethernet traffic was considerably different than Poisson traffics mainly regarding self-similar aspects with long-range dependence, which is not well described by short memory processes. In practice, the packet switched networks that were planned using the Poisson model, normally had an overprovision in links capacity to comply with the lack of accuracy of the model. Considering the different works about capacity planning following the work of Leland, the heavy-tail models were considered more accurate to describe the traffic in packet switched

One kind of traffic that appears often in wideband networks is the burst traffic. It can be generated by many applications such as compressed video services and file transfers. This traffic is characterized by periods with activity (on periods) and periods without activity (off periods). Moreover, as proved in (Perlingeiro & Ling, 2005), (Barreto, 2007), it is possible to generate self-similar traffic by the aggregation of many sources of burst traffics that presents

The self-similar model defines that a trace of traffic collected at a time scale has the same statistical characteristics that an appropriately scaled version of the traffic to a different time scale (Nichols et al., 1998). From the mathematical point of view, the self-similarity of a stochastic process in continuous time is defined as shown in Equation 1, which defines a

( ) ( ), 0

The sample functions of a process X(*t*) and its scaled version of the *a*–HX(*at*) obtained by compressing the time axis by the factor amplitudes "a" , can not be distinguished statistically. Therefore, the moments of order n of X(*t*) are equal to the moments of order n of X (*at*), scaled by a-Hn. The Hurst parameter, H is then a key element to be identified in the traffic. For self-similar traffic, the H is greater than 0.5 and less than 1. For a Poisson traffic this value is close to 0.5. Experimental results show that this same parameter in operational networks (Perlingeiro & Ling, 2005; Carvalho et. Al., 2007) has values between 0.5 and 0.95. Then, the parameter H may be a descriptor of the degree of dependence on long traffic

The aforementioned Hurst parameter plays a major role on the measurement of the selfsimilarity degree. The closer it is of the unity, the greatest the self-similarity degree. One of the most popular self-similar processes is the fractional Brownian motion (fBm), which is the only self-similar Gaussian process with stationary increments. The increments process of the fBm is the fractional Gaussian noise (fGn). To generate the traffic, we first create a fGn

*<sup>H</sup> X t a X at a* <sup>−</sup> = > (1)

*d*

a Poisson distribution describes the traffic in this type of network.

networks and consequently, they appeared as a better choice.

a heavy-tailed distribution for the on period.

process in continuous time X (*t*) as exactly self-similar.

**2.3 Self-similar** 

(Zhang et al.; 1997).

carrier) service provider of IP traffic was used to collect real network traces and we simulated a similar architecture of this network using the OPNET Modeler tool.

A 3G with a Metro Ethernet access was also analysed. The analysis considered a per application separation of traffic. The statistical analysis was done using a self-similarity approach, calculating the Hurst parameter using different calculation methodologies (Abry et al., 2002). Some multifractal analysis was also done as a tool to better choose the time scale.

The results show that the proposed method is able to generate better results in terms of an on-line traffic engineering control and also to provide key information to long term capacity planning cycles. The Traffic Engineering function is detailed using some network simulations examples. Finally, some long term forecasting and short term traffic engineering proposal was done in a 3G networks.
