Embed Size (px)
Citation preview
Gianfranco Ciccarella
Seminario ISCTI
16 Aprile 2018
5G Impacts and Opportunities
on Network Architecture and TCO
TECHNOLOGY
SUSTAINABILITY
TELCOs
Why Telcos IP Ecosystem and 5G require a disruptive transformation
OTT’s role on services and devices Strong Telco competition Regulatory policy push ‘Disruptive’ Telco transformation culture and vision services and network architecture business models
Impact of IP traffic rate growth on performance, revenues and TCO
Peak traffic rate Average traffic rate 2016 3,6 2021 6,14
Network cost depends on (1): Peak traffic rate [Gbps] Peak traffic rate Average traffic rate Traffic Volumes [Pbyte/month]
Ratio:
Netw. performance and service quality depend on Peak traffic rate Average traffic rate the ratio:
(1) Network Total Cost of Ownership depends on: • topology, technologies and architecture, that define network segments unitary costs [KE/(Gbps*year)] • Peak traffic rate that depends on traffic volumes [Pbyte/month] and on end user applications that define the ratio
Peak traffic rate / Average traffic rate
Key points on traffic rate: peak and access versus IP network difference
Issues related to traffic rate difference in UBB access versus IP network (aggregation, metro and core). To day the bit rate [Mbps/active user] ‘reference’ values are: • from 20-30 [Mbps/active user] up to 150-200 [Mbps/active user] for fixed and mobile UBB access networks • from 2 [Mbps/active user] up to 8-10 [Mbps/active user] for aggregation, metro and core networks(1) This huge traffic rate difference is critical for network TCO, application performance and UBB monetization.
(1) 2 [Mbps/active user] is related to nets with 1 [Mbps/user] and peak hour Contemporaneity Coefficient=0,5 (note that 0,5 is ‘low’ for to day utilization): 1 [Mbps/user]/0,5 [active user/user] = 2 [Mbps/active user] 10 [Mbps/active user] is related to nets with 1 [Mbps/user] and non peak hours Contemporaneity Coefficient=0,1
The cost to deliver 1 Pbyte/month depends on the ratio:
Peak traffic rate Average traffic rate
Latency
From 4G to 5G: 5G Use Cases and Application requirements(*)
5G Innovative Applications have challenging requirements in terms of: Latency Throughput (1)
to ensure the Quality of Experience (QoE) levels expected by End Users and by Internet of Thinks (IoT)
(*) European Parliament: 5G Network Technology Briefing , January 2016 Source: GSMA Intelligence, 2015
(1) Note that: TH << Bit Rate Apps TH = ‘speed’ of the application Bit Rate = ‘speed’ of the communication channel
Mobile Networks Throughput
Roughly half of mobile users report speeds of less than 4 Mbps Average Radio Bit Rate utilization in “large” (i.e. > 1 Mbyte) TCP flows is: 34.6%(*)
Average (1, *) :
TH Radio Bit Rate
= 34.6%
Source: Akamai, Delivering the best mobile experience, April 2016 (*) “An In-depth Study of LTE: Effect of Network Protocol and Application Behavior on Performance,” SIGCOM 2013
Data on radio link bit rate utilization are reported in: Binh Nguyen, Arijit Banerjee, Vijay Gopalakrishnan, Sneha Kasera, Seungjoon Lee, Aman Shaikh, and Jacobus Van der Merwe Towards Understanding TCP Performance on LTE/EPC Mobile Networks AllThingsCellular’14, August 22 2014, Chicago, IL, USA, http://dx.doi.org/10.1145/2627585.2627594.
(1) Apps TH = ‘speed’ of the application
Bit Rate = ‘speed’ of the communication
channel
TH/Bit Rate increase requires:
• Lower Roud Trip Time
• Lower Packet Loss
Contents and Apps closer to End Users… impact on network architecture (1/2)
4G Mobile Network Architecture ‘As Is’
Contents and Apps closer to End Users… impact on network architecture (2/2)
Mobile Architecture ‘To-Be’: Cloud RAN, Virtual EPC and CD Platforms
Cloud RAN
V-EPC
V-EPC
Network and POP target architecture
Fixed / Mobile Access Net
OLT
Router
OLT Cloud/QoE Platforms
QoE Platforms
Cloud Platform
Apps/ v QoE
Edge POP
Cloud/QoE Platform
Core POP
Metro/Regional POP
Cloud/QoE Platform
Cloud/QoE Platform
v IP Edge
Traffic managed by Content Delivery Platforms 2016 2021 58% 41% 20% 23% 22% 35%
• IP Edge and Content Delivery Platforms distribution • Increasing role of Content Delivery Platforms
(Cloud e QoE) that • by 2021 will carry 71% of the total internet
traffic • up from 52% in 2016 (Cisco VNI 2017)
• Content delivery platforms will carry traffic
closer to the end user
• Technologies: NFV, SDN
Edge Computing: Apps performance improvement
Edge Computing: Apps performance improvement
OTT Remote Server
Telco GTW
Mob Core Node
Base Station
CRAN Node
Mobile Terminal
LatencyCore-OTT Server LatencyCRAN-Core LatencyTerm-CRAN
Accelerator
Cache
Accelerator
Case Studies
Case Study
Latency distribution Cache & Accelerators deployment Speed
UP
A 2,1
B 2,4
C 2,4
D 1,8
E 2,4
F 4,5
55% 30% 15%
Cache Efficiency: 60%
Accelerator Efficiency: 95%
Term-CRAN CRAN-Core
15% 20% 65% Term-CRAN CRAN-Core Core-OTT Server
Core-OTT Server
25% 35% 40% Term-CRAN CRAN-Core Core-OTT Server
Same Packet Loss with and without QoE Platf.
Saving by Edge Computing
Network Cost (no QoE; TH=TH q) – Network Cost (QoE; TH q) Saving = (TH = TH q) Network Cost (no QoE; TH=TH q)
Saving evaluation • Traditional IP Network architecture (centralized IP Edge and no QoE platforms) vs Edge
Computing Architecture (distributed IP Edge and distributed QoE platforms) • Same Network segments costs (Access, Aggregation, Metro and Core), i.e. same Network
topology and technology • TH = TH q, i.e. same total average throughput for the traditional IP Network (TH) and the Edge Computing (TH q) • Edge computing Architecture cost includes the costs for distributed Platforms
• Cloud (Hw and Sw), v IP Edge (v BRAS and v EPC), v QoE or physical QoE
Edge Computing in Mobile Networks:
• Cloud RAN
• Distribution, based on Telco Cloud and NFV, of
• Mobile IP Edge (EPC) and
• Platforms to improve the application performance and to reduce TCO
UBB Network Costs • UBB Network cost simple models (a)
– C fixed access = f(TkR: user take rate) (1) – C mobile access = f(TR: peak hour traffic rate/act.user; CC) (2) – C aggr.+metro+core = f(TR: peak hour traffic rate/a.user; CC) CC: Contemporaneity Coefficient [Active user/user]
Netw. Cost [E/(user*year)]=Netw. Cost[KE/(Gbps*year)]*CC[active user/user]*TR[Mbps/active user]
C fixed access[E/(user*year)] = c1 + c2/TkR C mobile access[E/(user*year)] = Cma[KE/(Gbps*year)]*CC*TR[Mbps/active user] (b),(c) C aggr.+metro+core[E/(user*year)]= Ca+m+c[KE/(Gbps*year)]*CC*TR[Mbps/active user] (c) Ca+m+c: Cost of aggregation + metro + core network [KE/(Gbps*year)]
Normalized Cost [Mbps/user]= C[E/(user*year)]/Norm. Coeff.[KE/(Gbps*year)] (d)
From [KE/Gbps] to [KE/(Gbps*year)] rough evaluation: xy[KE/(Gbps*year)]=zw[KE/Gbps]*1,85/5[1/years] The coefficient 0,85 = 0,15[installation 15%]+5*0,1[year*(O&M 10%/year)]+0,04*5[(WACC 4%/year)*year] WACC: Weighted Average Cost of Capital
(a) Fixed access: LL+GPON OLT; Mobile access: radio link + eNodeB or C RAN (b) Mobile access saving=f(THoRBR: Throughput/radio link bit rate) (c) CC: contemporaneity coefficient [active user/user] TR act. user [Mbps/(active user)]*CC= TR user[Mbps/user] (d) Norm. Coeff. = Reference cost
(1) The cost does not depend on the user traffic rate, because fixed UBB access bit rate depends on technology and bit rate is much higher than the user bit rate in the aggr.+metro+core network. (2) The cost depends on the user traffic rate.
UBB Network Costs (2)
• C fixed network[E/(user*year)]= f(TkR, TR, CC)=
C fixed access[E/(user*year)]+C aggr.+metro+core[E/(user*year)]
• C mobile network[E/(user*year)]= f(TR, CC)=
C mobile access[E/(user*year)]+C aggr.+metro+core[E/(user*year)]
• Fixed or Mobile netw. normalized cost [Mbps/user]=
C fixed or mobile network[E/(user*year)]
Ref. Cost[KE/(Gbps*year)] Saving :
• QoE platf. such as Transparent caching (for fixed/mobile aggr.+metro+core) • Increase of Throughput/radio link traffic rate (for mobile access)
Edge Computing: Network Cost Saving
More details are presented in the following slide.
Mobile Network saving for Transparent Cache and 2 Accelerators function of SU
Saving% 10
TH RAN ECC [Mbps] cost % cost % (a) 30 40 10 10 20 10 30 40 50 10 20 50
100 %
50 %
Transp. Cache in C-RAN
- 50 %
SU
2 Accelerators 30 40 10 10 20 10 30 40 50 10 20 50
5 1
ECC TC% = F(SU, HR) ECC AC% = F(SU) 1 < SU TC =<5 HR = 0,5
ECC TC%(SU) 100
ECCTC% (SU) 100
250%
50%
Saving % with TC in SU = 1 TH RAN ECC% Sav% 30 40 10 -10% 10 20 10 -10% 30 40 50 -50% 10 20 50 -50%
SU = 1 Sav% TC = = - 100*ECC TC%/MNC%
(a) With SU = 1
Infrastructure sharing impact on 5G capex
5G can drive the transformation of Telco services/network architecture and business
Telcos’
performance OTTs’ performance Key telco issue
How to address the
issues?
Time to market
(TTM)
Total cost of
ownership (TCO)
Application serv.
performance
UBB monetization
Application and
network services
‘separation’
2
1,5-2 years 1 month
Very high/high Low
To be improved (1) Performance
- focused on bit rate - based on IP transport (L1 to L3)
Performance limited by Telco networks
- Focused on throughput and download time - based on Content Deliv. Platforms (L4)
Ability to monetize is linked to application performance improvement, i.e. to throughput/bit rate ratio
Not relevant – no network to monetize
Edge
Computing Distribution
of: • IP Edge • Content
Delivery and Services Platforms
1
2
(1) Apps TH = ‘speed’ of the application
Bit Rate = ‘speed’ of the communication channel
Conclusions: take away and open issues
Key take away
Fiber to any RAN site
RAN technologies and cell densification
Edge Computing Architecture (Enhanced Content Delivery = performance improvement + cost saving)
From shared infrastructure to shared active network components
…
Key open issues
Which network functionalities should be distributed?
Is NFV technology affordable?
Is the ‘deep’ Edge Computing distribution affordable?
Will 5G drive the integration of fixed and wireless networks?
Are key target Telco business models defined?
Are 5G standards/committees driving the disruptive tranformation?
…
