**4.2 Analysis of a 3G network**

42 Telecommunications Networks – Current Status and Future Trends

The *fop* is calculated based on (Perlingeiro & Ling, 2005) study as shown in (8) and (9), where *EN* is from (7) and *b'* is the normalized buffer (*b'*=*b*/*b0*), where *b* is the buffer and *b0*

> *FEP* **Model Ploss=1%**

√��� �� ��7 � � � � (9)

EN HTTP EN P2P

Poisson P2P Poisson HTTP

b=5min b=1min b=5s b=1s b=0,5s b=0,05s

��� <sup>=</sup> <sup>2</sup> 75

minimum possible buffer size, L is the burst factor.

100

00:00

02:00

04:00

06:00

08:00

10:00

**Time (***h***)** 

12:00

14:00

**FEP Estimator Average a = 108Mbps σ = 52,34 and Ploss=10-7**

16:00

18:00

20:00

22:00

00:00

150

*f*

*op***= Rate (Mbps)** 

200

250

300

350

Fig. 10. Bandwidth estimation curves using the FEP method.

0,54

0,58

0,62

0,66

0,7

0,74

**H**

0,78

0,82

0,86

0,9

0,94

0,98

**EN=Bandwidth (Mbps)**

0,5

The FEP Model shown in Figure 10 uses a dynamic tunnel configurator as shown in Figure 9, denoting a better usage of the total available bandwidth. In the figures it appears that when a constant calculated bandwidth is used, more bandwidth is required. In the same way, the FEP Estimator shows that as much aggregated the traffic will be in any time scale,

��

The second evaluated network is a brazilian 3G network. This network runs with more than 1 million attached 3G costumers with national coverage. The traffic samples were collected in July, 2009 in three different locations (Leblon, Barra da Tijuca and Centro) in Rio de Janeiro. Two monitors were located in the network to collect the traffic, as shown in Figure 11.

#### Fig. 11. 3G Network.

The main objective in this section is to investigate planning and project deployment phases based on traffic characterization. The first step is to classify the traffic per application. The second is to characterize the traffic using a procedure based on self-similarity (Clegg, 2005) or multifractal analysis (Carvalho *et al*, 2009).

Figure 12 shows the network topology for the Ethernet physical node B (ATM node) with an ATM-IP router which is responsible to convert ATM to Ethernet(IP). The same situation is found in RNC side where a Tellabs ATM-IP router aggregates all node B physical uplinks,

IP and 3G Bandwidth Management Strategies Applied to Capacity Planning 45

same IP subnet. The solution for this architectural problem was divide the Node Bs in 20 per

This division resulted in diminishing the broadcasting to less than 5%. This problem is very simple in a typical Ethernet topology, but not so easy to be detected when inserted in a 3G network. Ethernet is a protocol designed for local area purposes; the MEF (Metro Ethernet Forum) inserted some signalling standards as a way to simplify the application in metro and

Figure 14 shows the traffic trace collected in the 3G network and Figure 15 and Figure 16 show the singularity spectrum and the Hölder function for the 3G samples, showing the

This information is important do show this traffic can be characterized as multifractal in small scales of time, but in other hands the bandwidth model for this type of traffic model is also hard to build, because the nature of the traffic. Other important thing to understand is how to insert modifications with make the system not stable. In small scales, huge systems will need a lot of information to compute the bandwidth between to

The use of one model type can be very carefully choose because this could make the

Operations Staff make wrong decisions that could result in many downtime.

Fig. 14. Normalized 3G Traffic samples (milliseconds time scale).

possibility to use the multifractal model also to forecast purposes.

subnet, as shown in Figure 8 (after).

long-range use.

distance nodes.

every one carried through a Metro Ethernet network, with more than 50km radius Rio de Janeiro metropolitan area coverage.

Fig. 12. 3G topology from Node B to RNC.

The first performance analysis of this network found some drawbacks in terms of latency and packet loss and jitter. In Figure 13 (before) is shown the first measures. One detected problem was the high level of broadcasting (ARP included) for this metro Ethernet network, in some periods, more than 80% of all IP traffic.

Fig. 13. 3G Traffic analysis (before and after).

As shown in Figure 12, the transport from Node-B to the RNC is performed by a MetroEthernet network that uses also a BFD protocol to track the availability of a Multiprotocol Label Switching (MPLS) Label Switched Path (LSP). In particular, BFD (Aggarwal et al., 2010) can be used to detect a data plane failure in the forwarding path of an MPLS LSP.

LSP Ping is an existing mechanism for detecting MPLS LSP data plane failures and for verifying the MPLS LSP data plane against the control plane, making possible the PseudoWire connections through a MPLS environment.

The problem, in this case, was an architectural design mistake because all Node B uplinks were configured in Level 2 VLANs (OSI Model), with more then 250+ 3G nodes B in the

every one carried through a Metro Ethernet network, with more than 50km radius Rio de

The first performance analysis of this network found some drawbacks in terms of latency and packet loss and jitter. In Figure 13 (before) is shown the first measures. One detected problem was the high level of broadcasting (ARP included) for this metro Ethernet network,

As shown in Figure 12, the transport from Node-B to the RNC is performed by a MetroEthernet network that uses also a BFD protocol to track the availability of a Multiprotocol Label Switching (MPLS) Label Switched Path (LSP). In particular, BFD (Aggarwal et al., 2010) can be used to detect a data plane failure in the forwarding path of an

LSP Ping is an existing mechanism for detecting MPLS LSP data plane failures and for verifying the MPLS LSP data plane against the control plane, making possible the

The problem, in this case, was an architectural design mistake because all Node B uplinks were configured in Level 2 VLANs (OSI Model), with more then 250+ 3G nodes B in the

Janeiro metropolitan area coverage.

Fig. 12. 3G topology from Node B to RNC.

in some periods, more than 80% of all IP traffic.

Fig. 13. 3G Traffic analysis (before and after).

PseudoWire connections through a MPLS environment.

MPLS LSP.

same IP subnet. The solution for this architectural problem was divide the Node Bs in 20 per subnet, as shown in Figure 8 (after).

This division resulted in diminishing the broadcasting to less than 5%. This problem is very simple in a typical Ethernet topology, but not so easy to be detected when inserted in a 3G network. Ethernet is a protocol designed for local area purposes; the MEF (Metro Ethernet Forum) inserted some signalling standards as a way to simplify the application in metro and long-range use.

Figure 14 shows the traffic trace collected in the 3G network and Figure 15 and Figure 16 show the singularity spectrum and the Hölder function for the 3G samples, showing the possibility to use the multifractal model also to forecast purposes.

This information is important do show this traffic can be characterized as multifractal in small scales of time, but in other hands the bandwidth model for this type of traffic model is also hard to build, because the nature of the traffic. Other important thing to understand is how to insert modifications with make the system not stable. In small scales, huge systems will need a lot of information to compute the bandwidth between to distance nodes.

The use of one model type can be very carefully choose because this could make the Operations Staff make wrong decisions that could result in many downtime.

Fig. 14. Normalized 3G Traffic samples (milliseconds time scale).

IP and 3G Bandwidth Management Strategies Applied to Capacity Planning 47

characterized the traffic and showed examples of how to apply the proposed frameworks. An special interest of our work has a focus in real operating networks and the examples

The traffic characterization procedures for mutilmedia traffic were explained. We provided analyses by collecting different types of traffic and measuring its self-similar or multifractal degrees. All of this work was done with some self-developed (Carvalho et al., 2006) tools

The traffic models give us a good idea of the traffic behavior. In fact, the models can be valuable tools to the conception, management and sizing of a telecom network, resulting on efficient use of its resources. The operator can plan the growth of the network just to fit the business model, guaranteeing at different moments the efficient use of network resources, guaranteeing, on the other hand, the users satisfaction. In this context, the traffic models can also be used to define alternative policies that, for example, promote the network adaptation

Some very important to considering is how to improve the planning function with a better forecasting (Zukerman et al., 2003)., in terms of long time period for new assets plan and

Something also very important is how to manage the network resources to have the best optimization possible, this will provide costumer better experience when using and buying

Abry P., Baraniuk R.; Flandrin P.,Ried; R., Veitch D. (2002). The Multiscale Nature of

Aggarwal, R; Kompella, K.; Nadeau, T.; Swallow, G. (2010). Bidirectional Forwarding

Avallone, S.; Pescapè, A.; Romano, S.P., Esposito, M.; Ventre, G. (2002). "Mtools: a one-way-

Barreto, P. S. (2007). Otimização de Roteamento Adaptativo em Redes Convergentes com tráfego autosimilar. Orientador:Carvalho, P. H. P. Tese de Doutorado, UnB. Carvalho, P. H. P.; Barreto, P. S.; De Deus, M.; Queiroz, B.; Carneiro, B. (2007). A per

Carvalho, P.H.P.; Barreto, P. S.; Queiroz, B.; Carneiro, B.N. (2006). Modelagem, Geração e

Análise de Tráfego em Redes Multiserviços, GTAR, LEMOM, UnB.

Network Traffic Discovery, Analysis and Modeling. IEEE Signal Processing

Detection (BFD) for MPLS Label Switched Paths (LSPs). RFC 5884. ISSN: 2070-1721,

delay and round-trip-time meter" 6th WSEAS International Conference, Crete, July

Application Traffic Analysis in a Real IP/MPLS Service Provider Network. The 2nd IEEE IFIP/ International Workshop on Bradband Convergence Networks(IM2007/BCN2007), IEEE Communications Society , Munich, Germany, 21 a 25 de maio de 2007 ISBN: 1-4244-1297-8. Digital Object Identifier:

show the application of the proposed frameworks in these environments.

and also with some other tools (FRACLAB, 2011; OPNET, 2011).

in periods with different levels of congestion.

also to implement new products.

Magazine, 19(3):28-46.

June 2004. IETF Documents.

10.1109/BCN.2007.372751.

exactly their needs.

2002.

**6. References** 

Fig. 15. 3G Traffic multifractal analysis – Singularity Spectrum (milliseconds time scale).

Fig. 16. 3G Traffic multifractal analysis – Hölder function. (milliseconds time scale).

#### **5. Conclusion**

This chapter presented an approach and a set of frameworks to characterize traffic and optimize network planning in IP and 3G networks. Based on real traffic measurements, we characterized the traffic and showed examples of how to apply the proposed frameworks. An special interest of our work has a focus in real operating networks and the examples show the application of the proposed frameworks in these environments.

The traffic characterization procedures for mutilmedia traffic were explained. We provided analyses by collecting different types of traffic and measuring its self-similar or multifractal degrees. All of this work was done with some self-developed (Carvalho et al., 2006) tools and also with some other tools (FRACLAB, 2011; OPNET, 2011).

The traffic models give us a good idea of the traffic behavior. In fact, the models can be valuable tools to the conception, management and sizing of a telecom network, resulting on efficient use of its resources. The operator can plan the growth of the network just to fit the business model, guaranteeing at different moments the efficient use of network resources, guaranteeing, on the other hand, the users satisfaction. In this context, the traffic models can also be used to define alternative policies that, for example, promote the network adaptation in periods with different levels of congestion.

Some very important to considering is how to improve the planning function with a better forecasting (Zukerman et al., 2003)., in terms of long time period for new assets plan and also to implement new products.

Something also very important is how to manage the network resources to have the best optimization possible, this will provide costumer better experience when using and buying exactly their needs.
