1. Traffic forecasts models for the transport network
Borgar Tørre Olsen
Kjell Stordahl, Kjell O Kalhagen, Jørgen Lydersen, Bjørn Olufsen, Nils K Elnegaard
Telenor

2. TONIC project (IST )
Full name: Techno-Economics of IP optimised networks and services
Duration: 1/2001 – 12/2002
Consortium
Nokia Corporation (co-ordinator), FIN
Telenor AS, N
France Telecom S.A., F
University of Athens, GR
Atlantide Grenat Logiciel, F
Swisscom AG, CH
Universidade de Aveiro, P
T-Systems Nova GmbH, D
Website:

3. Outline Network platforms and services Residential market
ADSL forecasting model
Business market
Total traffic forecasting model
Conclusions

4. Network platforms and services
Twisted pair copper (Voice /Internet /broadband services on ISDN or XDSL)
Fixed Wireless LMDS/ LAN (Voice/ Internet/ broadband services)
Cable networks (Voice/ Internet /broadband services on Cable modems/ HFC)
Mobile networks ( Voice/ Data on GSM, GPRS or UMTS)
PSDN
Frame relay/ATM
Various IP networks
Leased lines (SDH, WDM or dark fibre)

5. Network platforms (fixed broadband)

6. Traffic - residential market
Voice traffic (POTS/ISDN)
Dialled internet traffic (POTS/ISDN)
ADSL traffic
VDSL traffic
Fixed wireless broadband traffic (FWBB)
Cable modem traffic

7. POTS/ISDN traffic => Broadband
The voice traffic is flattening out
The Internet traffic has increased significantly the last years
There is migration effects between dialled Internet traffic and DSL traffic
The heavy users are leaving dialled Internet and order broadband accesses

8. Drivers for ADSL traffic evolution
Growth of ADSL subscriptions
Application evolution
Increased traffic per subscriber
Migration from low access capacity to higher
Increased symmetric communication

9. Drivers for ADSL traffic evolution

10. ADSL traffic forecasting model
ADSL access forecasts
Application evolution
Migration to higher access capacities
Busy hour and traffic in the busy hour
Packet concentration factor

11. Access capacity evolution
Migration from narrowband to broadband Internet
Migration from asymmetric communication to symmetric communication
Increased use of entertainment applications
Examples: Video on demand, video streaming, virtual reality, music on demand, games, simulators, video exchanges, home office, large file transfers, video conferences etc

12. Market share evolution between ADSL products (European average)

13. Evolution of ADSL downstream capacity (Mbit/s) per subscriber

14. Broadband penetration forecasts (% of households) Mean European figures

15. Market share distribution between ADSL, VDSL, FWBB and HFC, European average

16. Residential market: Traffic forecasting model
Traffic indicator:
VR(t) = Nt i bit uit Ait Cit Mit pit
Nt : Number of households in year t bit : Busy hour concentration factor for technology i Ait: Packet switching concentration factor for technology i uit : Access capacity utilisation for technology i Cit: Mean downstream access capacity for technology i Mit: Incumbent’s access market share for technology i pit: Access penetration forecasts (%) for technology i i=1: voice traffic i=2: dialled Internet traffic i=3: ADSL traffic i=4: VDSL traffic i=5: Fixed wireless broadband traffic

17. Traffic - business market
1 Voice traffic (POTS/ISDN)
2 Dialled internet traffic (POTS/ISDN)
3 PSDN traffic
4 Frame relay/ATM traffic
5 ADSL traffic
6 VDSL traffic
7 Fixed wireless broadband traffic (FWBB)
8 Fast Ethernet traffic
9 Gigabit Ethernet traffic
10 Leased lines

18. Broadband business forecasts ADSL and VDSL

19. Business market: Traffic forecasting model
Traffic indicator:
VB(t) = Nt i bit uit Ait Cit Mit pit
Nt : Number of households in year t bit : Busy hour concentration factor for technology i Ait: Packet switching concentration factor for technology i uit : Access capacity utilisation for technology i Cit: Mean downstream access capacity for technology i Mit: Incumbent’s access market share for technology i pit: Access penetration forecasts (%) for technology i
i=1,2,

20. Traffic forecasts mobile operators

21. Total traffic forecasts model
The total traffic at year t:
V(t) = VR(t) + VB(t) + VM(t) + VO(t)
Where:
VR(t) : Traffic forecast residential market
VB(t) : Traffic forecasts business market
VM(t) : Traffic forecasts mobile market
VO(t) : Traffic forecasts operator market
Adjusted busy hour traffic:
V*(t) = VR(t) + VBP(t) + VBL(t) + VM(t) + VO(t)
Where VBP(t) is the packet switched business traffic and  the proportion of the switched business traffic, which is carried in the residential busy hour

22. Capacity forecasts
The traffic forecasts are transformed to capacity forecasts by increasing the capacity according to:
Applying Lindberger’s approximation for busy hour dimensioning
Adding additional capacity for packet overhead
Adding additional capacity because of stepwise upgrading of capacity

23. Conclusions
A traffic volume indicator has been developed to estimate the yearly traffic increase in the transport network
The traffic volume indicator doe’s not include redundancy and protection in the core network
The traffic volume indicator is used to as input for: - transport network planning process - evaluation of new network structures - expansion of the network - introduction of new core network technology as Fast Ethernet and Gigabit Ethernet.

24. Time for Questions & Answers