Pricing part 2. S Networking Business. Slide 1. Helsinki University of Technology Networking Laboratory. S-38.

Transcription

1 Prcng part S Networkng Busness Helsnk Unversty of Technology Networkng Laboratory S /H Hämmänen Slde

2 Servce lfe cycle phases Impact on prcng penetraton margns Introducton Growth Maturty Declne tme Introducton: early adopters, skmmng vs. aggressve growth Growth: ncreasng demand, lttle competton, hgh margns Maturty: dfferentaton pressure, tough competton, low margns Declne: cost cuttng, harvestng nche segments, hgh margns Helsnk Unversty of Technology Networkng Laboratory S /H Hämmänen Slde

3 Backbone servces Impact of IP Asynchronous Frame Relay IP/RSVP ATM Synchronous SDH ISDN ATM/CBR Connecton orented IP/TCP IP/DS Connectonless Growth of IP traffc nvolves evoluton from nelastc to elastc applcatons (e.g. vdeo streamng nelastc elastc) from guaranteed servces to best-effort (the fundamental nature of IP s best-effort) from determnstc to statstcal multpleng (ref. effectve bandwdth) from bottleneck control to over-dmensonng from layer VPN to layer 3 IP VPN Key ssue: demand vs. supply of backbone capacty? Helsnk Unversty of Technology Networkng Laboratory S /H Hämmänen Slde 3

4 Backbone servces Wholesale of capacty between pre-defned smlar end-ponts FR Voce Data Vdeo IP MPLS Ethernet SDH ATM Dark fber Customers are other operators or ndvdual frms Portfolo of servces pont-to-pont vs. multpont basc (dark fber) vs. value-added (managed IP router servce) voce vs. data vs. vdeo ATM beng gradually replaced by Ethernet and MPLS Prcng based on Servce Level Agreements (SLA) and traffc parameters (peak rate, mean rate, data loss probablty, ma delay, mean delay, etc) Helsnk Unversty of Technology Networkng Laboratory S /H Hämmänen Slde 4

5 Backbone servces Servce Level Agreement (SLA) Servce level agreement: a documented result of a negotaton between a customer and a provder of a servce that specfes the levels of avalablty, performance, operaton and other attrbutes of the servce Statc SLA management: SLA contract s made between two human partes and ts terms cannot be changed wthout human nterventon Dynamc SLA management: SLAs are negotated and contracted automatcally usng some sgnalng procedures SLA tradng: dynamc SLA management where nformaton on servce provsonng, routng, and prcng are echanged between provders Helsnk Unversty of Technology Networkng Laboratory S /H Hämmänen Slde 5

6 Backbone servces SLA evoluton scenaro. Statc SLA management n telecom networks and dedcated data networks. Statc SLA management n IP-based best effort networks 3. Statc SLA management n IP dffserv (DS) networks? 4. Dynamc SLA management n IP DS networks? DS has the followng SLA characterctcs Large traffc aggregates (as opposed to ATM SVC) Typcal traffc aggregates are VoIP, WWW, specfc routes Aggregates appear as Traffc Condtonng Agreements (TCA) Traffc flows through DS domans (va ngress/egress nodes) Standardzed Per-Hop-Behavors (PHB) for ee prcng? Epedted Forwardng (EF) Assured Forwardng (AF) Helsnk Unversty of Technology Networkng Laboratory S /H Hämmänen Slde 6

7 Backbone servces SLA traders Access network Core network (transt doman) Access network ISP3 User ISP ISP ISP4 ISP5 User Legend SLA trader Statc SLA Dynamc SLA Dynamc SLAs between peer ISPs Statc SLAs for end-users Helsnk Unversty of Technology Networkng Laboratory S /H Hämmänen Slde 7

8 Backbone servces Summary of SLA tradng SLA tradng has not been tested n real deployments SLA tradng suts best for large networks and ISPs Transton from statc to dynamc SLA tradng s a major management challenge Based on smulaton results, SLA tradng can mprove network utlzaton by up to 40% compared to a tradtonal, shortest-path routed nter-doman network The resdual bandwdth prcng strategy s a sutable canddate for SLA tradng snce t ensures that prces ncrease wth SLA or lnk load Helsnk Unversty of Technology Networkng Laboratory S /H Hämmänen Slde 8

9 Internet access servces Congeston control The end-to-end bottleneck may occur at dfferent ponts In dedcated access: Increase the dedcated per subscrber access speed (e.g. ADSL) Push bandwdth sharng closer to subscrbers (e.g. HomePNA) In shared access/backbone/server: apply congeston control The level of congeston needs to be optmzed Too much congeston negatve network eternalty Too lttle congeston waste of network capacty Optons for congeston control Over-dmensonng (wastng of network capacty) Call admsson control, e.g. RSVP blockng (latest customers suffer) Automatc flow control, e.g. TCP (all customers suffer) Human farness control, e.g. HomePNA (local group dscplne) Congeston prcng (mamal socal surplus?) Helsnk Unversty of Technology Networkng Laboratory S /H Hämmänen Slde 9

10 Internet access servces Congeston prcng - theory Congeston prce s two-part: normal + eternalty, p+p E Socal surplus mamzaton () ma j u j ( j,y)-c(k), where y= /k, k=total fed capacty p E =-(/k) j du j ( j,y)/dy, where j =socally optmal demand Indvdual mamzaton of surplus for consumer () ma[u (,y)-p E ] =, f number of users s large Socal and ndvdual optma are the same, Nash equlbrum! Congeston prce converges to optmal prce va tatonnement: network determnes p E usng step () and publshes t, then each consumer solves step () to fnd, and so on u j are unknown network must vary p E untl fndng equlbrum y s unknown to consumers consumers estmate t va congeston Congeston prcng suts for epensve bottlenecks lke rado Congeston prcng facltates automatc optmal capacty plannng va the customer feedback loop Helsnk Unversty of Technology Networkng Laboratory S /H Hämmänen Slde 0

11 Internet access servces Congeston prcng - practce Tme-of-day prcng (e.g. fed-prce tckets n Internet Café) Prcng per applcaton & traffc type Types pre-defned usng dffserv, e.g. www, VoIP, etc Automatc traffc classfcaton and resource re-allocaton Prcng per user s wllngness-to-pay Prce-drven separaton of servce classes (e.g. Pars Metro Prcng) Prorty servce classes based on relatve qualty (e.g. va dffserv) Note that flat-rate prcng well reflects the operator s large share of fed cost, but cannot effcently tackle the problem of temporary congeston! Helsnk Unversty of Technology Networkng Laboratory S /H Hämmänen Slde

12 S /H Hämmänen Slde Helsnk Unversty of Technology Networkng Laboratory Congeston prcng Eample: Tme-of-day prcng Assume utlty for consumer :, n whch the varables mply the amount of usage for peakhour and off-peak-hour, respectvely. By denotng capacty lmts for both peak-hour (t=) and off-peak-hour (t=) perods wth C, we end up wth a mamzaton problem: Ths leads nto a Lagrangan optmzaton problem (from the perspectve of socal planner), n whch we can now denote the Lagrangan constants wth symbols p (for comfort n the nterpretaton): It s easy to see that based on the frst-order condtons of the Lagrangan formula above, we end up wth the same soluton (gven the prce vector) by solvng the consumer s problem for each, only f we can balance the Lagrangan constants (=prces) so that the capacty s n full use (.e. Kuhn- Tucker condtons). Ths requres tatonnement (slght adjustments of prce so that the consumpton and capacty are balanced). Note that f e.g. the peak-hour utlty s hgher on average, n equlbrum ts prce must be hgher, too. ), ( u = = = N t t N t C s t u,,.. ), ( ma = = = = N N N C p C p u L ), ( p p u, ), ( ma

13 Content servces Prvate vs. publc goods Prvate good (e.g. candy bar) You consume one, there s one less for others - depletetable If consumed no one else can - ecludable Margnal cost > 0 Prce = margnal cost. Acheved on deal market when supply = demand Publc good (e.g. rado broadcast) Nondepletable when used by one, the same amount s avalable to others. Nonecludable Use by one does not eclude others from usng the good. Margnal cost 0 Prce 0 fed cost s not recovered taaton, nonusage based fees Helsnk Unversty of Technology Networkng Laboratory S /H Hämmänen Slde 3

14 Content servces Evoluton eamples Best-effort IP servce: Intally publc good Flat monthly fee Congeston Prvate good eternalty. Telephone call: In PSTN and over rado nterface = prvate good ( candy bar ) prce/unt. Value-added IP servce, e.g. VoIP: Intally usage fee. CPU and memory gettng cheaper (Moore s law) Margnal cost of new customer 0 Flat-rate. Dgtal Content: Margnal cost 0 Copyrght and IPR control enable both prvate and publc goods. Copyrght volatons, e.g. peer-to-peer traffc development of dgtal rghts management (DRM) or bundlng wth other prvate goods! Helsnk Unversty of Technology Networkng Laboratory S /H Hämmänen Slde 4

15 Servce bundlng Vertcal vs. horzontal bundlng n GSM Vertcal bundlng Bundlng of access wth content For nstance weather report over SMS Horzontal bundlng Bundlng of access servces (e.g. multple rados, crcut vs. packet-swtched, voce vs. data) Bundlng of vertcally bundled servces (e.g. weather report over SMS vs. WAP) Bundlng enables Cross-subsdes and servce dfferentaton Value-based prcng,.e. fleble testng of subscrber s wllngness-to-pay Helsnk Unversty of Technology Networkng Laboratory S /H Hämmänen Slde 5

16 Servce bundlng Roll-out of new servces Cross-subsdes enable early roll-out of stll non-proftable servces Operator can also take rsk of new handsets va handset subsdes Helsnk Unversty of Technology Networkng Laboratory S /H Hämmänen Slde 6

17 Case: DoCoMo -mode prcng Unoffcal content provders (53 000) Offcal content provders (3000) Content-based fee (possbly free) DoCoMo 9% of content fee 70% free content Premum content: 0,39-,34e/servce/month,34e/month Fed fee 0,003e/packet(8B) = 8e/MB End-user Accounts for 87% of the -mode ARPU Accounts for less than % of the I-mode ARPU Source: Sandro Grech, 003 (prces 00) Helsnk Unversty of Technology Networkng Laboratory S /H Hämmänen Slde 7

18 Prcng of telecom equpment Tradtonally prcng s based on hardware capacty (e.g. swtchng centers, routers, base statons), whch hdes software R&D costs pressure to prce software Capacty prcng s adapted per type of capacty GSM MSC swtchng capacty (number of smultaneous calls) GSM HLR storage capacty (number of subscrbers) GSM BTS rado transmsson capacty (number of TRXs) IP router capacty (bts/sec, packets/sec, number of ports, etc) Server transacton capacty (SMS/sec, locatons/sec, etc) Growng eplotaton of general purpose operatng systems and hardware (e.g. Un) n network elements s lkely to gradually un-bundle the prcng of software and hardware Helsnk Unversty of Technology Networkng Laboratory S /H Hämmänen Slde 8