19.09.2005WP2 TEA 1

Feasibility of Bandwidth on-DemandCase study approach, models and issues

Stuttgart meeting 19-20th of September 2005

WP2.3/2.5

Håkon Lønsethagen (Astrid Solem, Borgar Olsen)

Telenor R&D

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Outline

• Motivation and objectives of BoD case study• Significant OPEX and CAPEX elements• OPEX model• CAPEX and OPEX related to Network and Service Management• Traffic sources and assumptions• Resource efficiency gains• Tariffs and income• Further work

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Motivation and objectives of BoD case study

• Scope– Network operator deploys BoD and/or “dynamic” (L1) TE capabilities– Given the context of a L1-L2 network offering L1 and CO-PS services, typically

L1 and Ethernet services– Services offered to Business customers, Service providers, and internal

services

• Motivation– More efficient resource usage– OPEX and CAPEX savings– Increased Income

• Objectives– To explore and analyse issues related to feasibility of BoD

• Find significant OPEX and CAPEX changes compared with traditional leased lines and with ASTN based leased lines

• Find potential network resource efficiency gains• Find potential increased demand for bandwidth• Find potential new revenue and income

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Significant OPEX and CAPEX elements that should be studied

• Making some rough estimates on which are significant OPEX and CAPEX elements

• OPEX– Service activation, deactivation and bandwidth upgrade/change– Equipment and SW licences– Charging and billing– Maintenance of equipment and components– Network management (e.g. SLA assurance) – Sales and marketing, Customer acquisition– Customer care

• CAPEX– Control plane– IT/OSS integration and new SW and licences

• Customer provisioning• Service management, activation and assurance• Control Plane management• Charging and billing• Customer care

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• Initial focus – Service management and bandwidth change

– The most significant OPEX element

– Have used Siemens/IMEC OPEX model cost elements for traditional and ASTN service provisioning

– ASTN OPEX cost assessment vs. BoD OPEX cost assessment

– Using connection length and bandwidth categories (granularities)

OPEX model

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SMo

CEUCnR

OPEX model – Service management

SO CEC SA SCh

SCe

SO Service Offer

CEC Customer Edge (CE) Configuration/Provisioning

SA Service Activation

SCh Service (Bandwidth/SLA) Change

SCe Service Cease

CEU CE Upgrade

CnR Contract Renegotiation

SMo Service Move

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• No separate CAPEX-model developed• The existing German reference network is taken as a basis in order to

derive parameters for the model, that is:– The total traffic to be handled– Number of hops used to model ”lengths”– Number of connections

• The registration of types and number of leased line services in Telenor equivalent to the categories:

– n*10M– n*100M– n*1G– Lambda

Use of existing workService categories

Hamburg

BerlinHannoverBremen

Norden

Essen

Dortmund

Köln

Düsseldorf

Frankfurt

NürnbergMannheim

Karlsruhe

StuttgartUlm

München

Leipzig

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Services

The registration of types and number of leased line services in Telenor equivalent to the categories:

• n*10M• n*100M• n*1G• Lambda

Is used to find the % of bandwidth consumed by each traffic category

And thus the number of connection of each traffic category in a network handling the traffic according to the German network

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Traffic sources and assumptions

Hamburg

BerlinHannoverBremen

Norden

EssenDortmund

Köln

Düsseldorf

Frankfurt

NürnbergMannheimKarlsruhe

StuttgartUlm

München

Leipzig

Total traffic - 3035 Gbit/s

2Mbit/s X%

….

2,5Gbit/s Y%

Lambda Z%

Service categories

n*10Mbs

n*100Mbs

n*1Gbs

n*Lambda

% of traffic for different service

categories

Scaled to network of

German network size

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Number of service changes

% of traffic for different traffic categories

Number of connections for different traffic categoriesGrowth rate

BoD Case:

Percentage of connections in each traffic category and provisioning option

Provisioning options:

• Freely allocatable

• Customer decided

• Leased lines

Yearly number of connections for different traffic categories

Scaled to German network

Traditional case:

Holding time

BoD Case:

% of service changes for each traffic category and provisioning option

Traditional case:

% of service changes

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Total cost for service change

• Number of service changes for each traffic category• For each traffic category the percentage of short, medium and

long connections are the same– Percentage of connections with different lengths is derived from

German network• Short: 1-2 hops• Medium: 3-4 hops• Long: >4 hops

• The unit cost for traditional and BoD service change (depending on granularity/service category and length)

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Cost per service change

• It is assumed that cost per service change will depend on length and traffic category

• Input: Cost per service change Lambda traditional and ASTN (from Siemens/IMEC work)

• Service change unit cost - Traditional case– A granularity multiplier is used to derive cost for the other traffic categories,

resulting in cost for medium length connections for all granularities– Cost for short and long lengths for Traditional case is derived using the Length

multipliers traditional

• Service change unit cost - BOD case– Derived from Unit cost traditional and BoD/Traditional cost reduction factors for

BOD medium length– Unit cost for short and long is derived from BOD unit cost medium length through

use of BoD length multipliers

• Method TO BE FURTHER ASSESSED– BoD is likely to require a greater cost reduction, compared with the

Siemens/IMEC study, in order to be attractive

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Unit cost for service change

Initial “guesses”:Cost ratio BoD/Trad.

Scale Cost ratio BOD/Traditional ref. 6,5 %3 Ratio BOD/Traditional n*10M 19,6 %2 Ratio BOD/Traditional n*100M 13,0 %

1,5 Ratio BOD/Traditional n*1GE 9,8 %1 Ratio BOD/Traditional n*Lambda=2,5G 6,5 %

Length Multiplier BODShort 0,7Medium 1,0Long 1,3

Length Multiplier TraditionalShort 0,7Medium 1,0Long 1,3

Granularity multipler Traditionaln*10M 1,0n*100M 2,0n*1GE 5,0n*Lambda=2,5G 10,0

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Further work – Input needed

• Consolidated NOBEL cost model– Further work needed (?)

• Control Plane cost model– OK?– Running/Support licence costs?

• CAPEX and OPEX related to IT/OSS– Initial cost, initial licence cost, system integration/modification cost, running

licence cost (+ other IT/OSS OPEX cost)– Service Activation

• New and/or system integration/modification

– Service Assurance• New and/or system integration/modification

– Charging• New mediation components and system integration/modification

– Billing• System integration/modification

• Development of a NOBEL IT/OSS cost model?– Who can provide what input?

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BOD Resource efficiency (CAPX) gains

Motivation for BOD is to utilize temporal traffic variations between given end-points:

• Typically in the time range of minute – month• (limited by packet switching in the lower limit and leased lines in

the upper limit)

Competing technologies are• Leased lines (based on SDH or )• Packet switching (e.g. MPLS-based VPNs)

Possible result: BoD is feasible if BoD for L1/L2 and BoD for L3/L2 is introduced into the market

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BoD performance gain

Gain potential

• BoD efficiency somewhere between leased lines and packet switching

• Granularity in time and capacity determine where:– frequent (relative to traffic variations) changes in small steps ~ packet switching– infrequent changes in large steps ~ leased lines

• Gain will vary significantly depending on traffic sources– 10% - 20% - 40% ???

Interesting types of traffic variations

• Periodic (e.g. daily) variations, e.g. different busy hours between – Residential and business data traffic– Between ISP traffic and mobile traffic

• Large timescale stationary stochastic variations

• Existing results??

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Pricing model – Assumptions and approach

• To develop a Pricing model for BoD services• Expected increase in demand due to price reduction• Approach:

– Based on existing number of connections for traditional case:– What is the income we have to match (per hour?)– With the number of connections for BOD case – what amount of cost reductions can be

achieved?

• Make a scaling matrix for tariffs with length and capacities relative to a given tariff (reference: n*10M and short set to 1)

• Multiply with the number of LL traditional contracts• Do the same for BoD but distribute the tariffs according to holding times or 1/

frequency of changes, possibly taking into consideration categories (Freely allocatable, Customer decided and LL types)

• Normalise BoD tariffs to match LL traditional contracts to find the typical tariffs for the BoD services

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Income – assumptions and approach(Example)

Scaling factor for services:

Revenue of LL 90 days as reference

Nr of LL as reference:

Revenue of BoD

To be done!!

Volume 55 % 41 % 4 %Length factors Short Medium Long

Service factors 1 2 2,4n*10M 1 1 2 2,4n*100M 2 2 4 4,8n*1GE 3 3 6 7,2

n*Lambda=2,5G 3,4 3,4 6,8 8,16

Nr of LLYear 2006 2007 2008

n*10M 50 583 53 113 55 768 58 557n*100M 3 035 3 339 3 672 4 040n*1GE 152 182 219 262

n*Lambda=2,5G 20 26 34 44

Revenue per year Rel. to n*10M short price, 90 daysYear 2006 2007 2008

n*10M 300 740 315 777 331 566 348 145n*100M 36 089 39 698 43 668 48 034n*1GE 2 707 3 248 3 898 4 677

n*Lambda=2,5G 409 532 691 899Sum 339 945 359 255 379 823 401 755

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Further work – Open issues

• Tariff models• Rough estimation of potential resource efficiency gains• Demand changes – as a result of BoD• Estimation of service change unit costs• Rough estimation of IT/OSS related costs• Analysis and summary of the initial rough estimations and results