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10 º Fiberness – Pieve Santo Stefano (Ar) – Giugno 2014

“ Bit-Rate ” and “ Application Performance ” in Ultra BroadBand Networks

Gianfranco Ciccarella - Telecom Italia

Vice President Global Advisory Services

4ºFocus: Gianfranco

Ciccarella - Telecom

Index

►

QoE platforms: the reason why

►

How to improve Quality of Experience

►

New business models

Telecom Italia


IP traffic growth

Data traffic «volumes» and

«bandwidth» are growing…

Peak Internet Traffic – Total Bandwidth (Terabit/s)

865 x 3,5

… and most of the IP traffic is Video

247

World

2012 2017 cv x 2,9

46

134 x 2,7

23

62 x 3,6

72

259 x 4,0

320

80

Western Europe LATAM North America APAC

Internet Video Traffic (% of total consumer traffic)

76%

69%

71%

66%

73%

66%

57% 57%

55%

51%

World cv

Average Internet – Total Bandwidth (Terabit/s)

9

2012 2017 x 2,8

Western

Europe

LATAM North America

2012 2017

APAC x 3,4 4

1,2 x 1,5 cv

1,3

2

3,2

Italy

Gianfranco Ciccarella – Telecom Italia – VP Global Advisory Services

Argentina Brazil

Source: Cisco VNI; Analysys Mason

3


Main performance requirements

Examples of service throughput requirements

Key performance Drivers:

• Downstream Application throughput

• Download time

2 CH b’cast

Business requirements on Download time

Strangeloop

– content delivery summit 2012

Gianfranco Ciccarella – Telecom Italia – VP Global Advisory Services 4


“Application Throughput” and “Bit-Rate”

Application Throughput is the most important KPI affecting end-user Quality of Experience (QoE)

User

Premises

Max TCP-IP data transfer Bit-Rate

Broadband

Line

16 Mbps*

Transmission Bit-Rate

13 Mbps*

Central

Office e.g. ADSL BBline monitor e.g. PC-web site speed test Tool

Web site

• Application Throughput is lower than Bit Rate (in some case much lower!)

• High Application Throughput requires low delay and low packet loss

•

QoS functionalities cannot improve Throughput

• QoE Platforms

(Application &

Content Delivery, WEB Acceleration,

Protocol Optimizations

…) are needed to improve best-effort IP network performance and to reduce network TCO 7 Mbps*

WEB FTP Application

Throughput e.g. Large File

Download (with a browser)

* Measures at my home, Italy, feb 2014



Application Throughput is lower than connection Bit Rate

Ookla SpeedTest (  bit rate)

= 6,98 Mbps (4Q 2013) Throughput/Bit-rate= 74%

Akamai average connection speed (  throughput)

= 5,2 Mbps (4Q 2013)

2010 Apr Jul Oct 2011 Apr Jul Oct 2012 Apr Jul Oct 2013 Apr Jul Oct 2014

Download Speed Test (a bit rate proxy) uses up to four HTTP threads to saturate the user connection ; several measure samples are analyzed to estimate the maximum connection speed.

The Average Connection Speed

(a throughput proxy) metric represents an average of the measured connection speeds across all of the unique IP addresses seen by Akamai for a particular geography

Akamai Status of the Internet 4Q13 Report



Application Throughput is lower than Connection Bit R ate…

…and it gets worse for higher Bit Rates (1/2)

( # ) Akamai connection speed (



Throughput ) - ( 3Q13 ) (*) Ookla SpeedTest data (



Bit Rate) - (3Q13)

For growing bit-rate:

• the ratio Throughput / BitRate decreases

• the “bit rate capacity waste” is more severe



Application Throughput is lower than Connection Bit R ate…

…and it gets worse for higher Bit Rates (2/2)

( # ) Akamai connection speed (



Throughput ) - ( ( 4Q13 ) (*) Ookla SpeedTest data (



Bit Rate) - (4Q13)

For growing bit-rate:

• the ratio Throughput / BitRate decreases

• the “bit rate capacity waste” is more severe



Bit Rate and Throughput vs RTT and Packet Loss

MAX TCP Throughput

20

7

TCP throughput over xDSL/FTTx access

Long-term target

Average DS

BitRate EU ( # )

Average DS

Throughput EU (**)

Average DS

BitRate Italy (*)

Average DS

Throughput Italy (**)

20 50 80 100

EU Commission report (oct.2013) shows that in EU:

P.Loss = 0,2% - 0,5% “Latency” = 19ms - 36ms

(*) NetIndex/Ookla SpeedTest (4Q13)

( # ) EU Commission report “Quality of BB in EU” (oct. 2013)

(**) Akamai State of the Internet 4Q13

Rif: ● M.Mathis et Al. , “Macroscopic Behavior of TCP Congestion Avoidance Algorithm, July 1997

9

Index

►


►


►

New business models

Telecom Italia


How to improve Quality of Experience

 Improve application throughput

QoE platforms

 Reduce web page download time

Multiple copies of contents

Content & Application Delivery networks, caching…

Reduce userserver distance…

Reduce latency for better performance

Network protocols optimization…

WEB acceleration, Front End Optimization…

 QoS functionalities cannot improve throughput nor reduce WEB page download time

 Telcos can leverage on both QoE platforms & QoS functionalities

 … and QoE platforms enable network TCO saving

Quality of Service (Network Level)

• QoS functionalities are always used in IP networks and provide traffic management mechanisms

(e.g. IETF Diffserv: Differentiated Services, ...) based on different priorities. QoS is also needed in case of network congestion

Quality of Experience (QoE)

• Subjective measure, from the user’s perspective, of the overall quality of the service provided.

• Usually expressed as “MOS, Mean Opinion Score”, ranging from 1 to 5.

• QoE is improved by platforms such as content & application delivery, caching, protocol optimization, front-end optimization, compression, adaptive bitrate.



CDN/TC Platforms improve best effort network performance

UBB access

Network

Policy Control

.

QoE Platforms

.

Mobile Access

Fixed Access

Metro

Regional

Core

All-IP Domestic Network

International

Network

Content nearer to users  better performance (higher throughput)

Akamai 2012, «Empirical Network Analysis»



Throughput improvement by “protocol enhancer”…

From 2 to 5 times higher throughput for many traffic types (e.g. FTP,

HTTP, video HD …)



Web page download time improvement …

QoE can be improved with a mix of technologies and solutions

4 Full optimization 3 Add a CDN

2 Keep Alive & Compression 1 No web acceleration sec

Strangeloopnet “ WEB performance Automation” http://www.youtube.com/watch?v=IPn0T1UacIA



OTT are looking to “enter” in the Telco Networks

PoP

1

2

PoP

• QoE platforms managed by OTTs

• Options 3 & 4 require IP

EDGE distribution

PoP

3

PoP

4



Network transformation guidelines

APP Server/

QoE Platf.

IP EDGE

APP Server/

QoE Platf.

IP EDGE

APP Server/

QoE Platf.

IP EDGE

APP Server/

QoE Platf.

Key Points

End-to-end IP/MPLS on WDM for IP EDGE, Application Servers and QoE platforms distribution

IP -

Ethernet

MPLS

IP -

Ethernet

WDM ROADM

TO-BE

MPLS

IP -

Ethernet

ROADM

AS-IS


Tx Network:

ROADM in Core and Metro,

WDM in Aggregation

QoE Platform deployment:

Content Delivery Network

Transparent caching

Application Delivery Network

Web Acceleration

… …

16


Cache in the Net (1/2) : RTT reduction

20 ms < RTT < 25 ms (95%)

30 ms < RTT < 35 ms (95%)

Simulation Results

RTT normalized distribution with

Caches deployed:

• at the interconnection point

(Off Net)

• in Core sites (On Net)

• in Metro sites (On Net)

RTT > 35 ms

User’s premises

Last Mile

BB-UBB access areas

On Net

Caching in the Metro

CO/metro sites

On Net

Caching in the Core core sites

Off Net

Caching

Out of Telco ISP domain sites

Internet

Interconnection

Point



Cache in the Net (2/2) : TCP throughput

PLR: 0,01%

PLR: 0,1%

Simulation Results

Max TCP throughput distribution with Caches deployed:

• at the interconnection point

(Off Net)

• in Core sites (On Net)

• in Metro sites (On Net)

User’s premises

Last Mile

BB-UBB access areas

On Net Caching in the Metro

CO/metro sites

On Net Caching in the Core core sites

Off Net Caching

Out of Telco ISP domain sites



Costs Saving due to Caches deployed in the network

Network Costs Saving (%) as a function of “Cx” (*)

(*) Cx =

Unitary “upstream network” Cost [K€/Gbps]

Unitary Cache Cost [K€/Gbps] referred to “cache fan-out”

“Upstream network “ cost = cost from the cache insertion point to the Big Internet interconnection

EC (Cache efficiency or HitRatio) = average % of traffic delivered by the cache

“Cache Fan Out” = traffic delivered by the cache (given by: EC * Traffic delivered to End Users

“downstream” the cache insertion point)

19



Costs saving due to distributed IP Edge & Caching

Case Study

A Bell Labs case study compared the TCO of a centralized IP edge to a distributed IP edge with CDN content caches over the five-year period. Network model was based on a large Tier 1 service provider in NA

20 Tier 1 COs

99 Tier 2 COs

C = centralized architecture

D = distributed architecture

• IP services edge

• Peer caches

• Access aggregation

 Network transport costs reduced 47%

Source: ALU WP - VIDEO SHAKES UP THE IP EDGE; 2012


20

Index

►


►


►

New business models

Telecom Italia


New business models

Best-effort is not sufficient to meet requirements for

Application/Content/Services in the ALL-IP scenario

 QoE platforms in the Domestic Network are needed

To complement best effort IP traffic termination, Telcos are deploying QoE platforms and are offering differentiated quality for

IP delivery to end-users and OTT/CP

Examples of OTT/CP and Telcos agreements: Comcast/Netflix, Verizon/Google,

Orange/Cogent, several Akamai agreements (including Akamai/Telefonica …)

 Incremental revenues from OTT/CP: “Two -sides business model”

 Telco Premium services offered to end users

OTT

Two –sides Business Model

€

Services fees

€

CDN, ADN, ...

TELCO

€

Internet Access fees

+ premium services


Enduser

22


QoE enables Telco Premium services

UBB+QoE = ACCESS MONETIZATION…

QoE improvement enables incremental revenues from NGAN & LTE

Average UBB price uplift To get a Premium Access Fee from

UBB access:

• access bit-rate improvement is not sufficient

• higher application throughput and lower download time are needed

Western EU

DSL Cable FTTx



Netflix-Comcast case

• Comcast must terminate Netflix traffic, to avoid end-users complaint & churn (

~

30% of Comcast customers are also Netflix customers)

• Comcast gets a “traffic termination“ revenue from Netflix , additional to the “access revenue” from end-users, in a two-sides business model.

The Telco revenue is related to the “value” of the Application/Content that OTT/CP offers to end-users.

In the Netflix case, the “value” is relatively small (“US $7.99 all you can watch ”) ; in other cases, the “traffic value” is higher (e.g. Amazon services)

• Comcast goal is to handle Netflix traffic efficiently ( … a huge traffic, e.g. 3Tbps, that will further grow)

• The agreement with Netflix facilitates Comcast use of more effective&efficient solutions to handle Netflix

( QoE platforms ) enabling network cost saving traffic

3 options

NO deal • Payback Time = never

Deal, but no use of QoE platforms

Deal, and use of

QoE platforms

• Additional Revenues from OTT/CP

• Payback Time estimate > 25 years

• Additional Revenues from OTT/CP

• Capex saving = 25%-40%

• Payback Time estimate = 10-15 years



Business models transformation guidelines

Content, Services

Є

Content, Services

Access fee

Є

Є

Є

Access fee

BestEffort

Dumb Pipe

ADV

Є

Internet Best Effort

OTT

Є

OTT

Content,

Services

CP

CP

Differentiated quality for IP delivery Є

Є

ADV

Premium Access Fee

QoE capable network

: Router/Server/Cache OTT: Over The Top; CP: Content Provider; ADV: Advertising

New IP Interconnection Policy

Regulatory issues on Net Neutrality




26

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