Bit-Rate and Application Performance in Ultra BroadBand Networks

Transcription

1 Bit-Rate and Application Performance in Ultra BroadBand Networks Gianfranco Ciccarella - Telecom Italia Vice President Global Advisory Services 4ºFocus: Gianfranco Ciccarella - Telecom

2 Index QoE platforms: the reason why How to improve Quality of Experience New business models Telecom Italia

3 IP traffic growth Data traffic «volumes» and «bandwidth» are growing x 3,5 Peak Internet Traffic Total Bandwidth (Terabit/s) and most of the IP traffic is Video Internet Video Traffic (% of total consumer traffic) 76% 69% 71% 73% 66% 66% 57% 57% 55% 51% cv x 4,0 x 3,6 x 2,9 134 x 2, World Western Europe LATAM North America APAC Average Internet Total Bandwidth (Terabit/s) cv x 2,8 9 World Western Europe LATAM North America APAC x 3,4 4 x 1,5 cv 2 1,2 1,3 3,2 Italy Argentina Brazil Source: Cisco VNI; Analysys Mason 3

4 4 10 º Fiberness Pieve Santo Stefano (Ar) Giugno 2014 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

5 5 Application Throughput and Bit-Rate Application Throughput is the most important KPI affecting end-user Quality of Experience (QoE) Application Throughput is lower than Bit Rate (in some case much lower!) User Premises Transmission Bit-Rate Broadband Line 16 Mbps* Central Office e.g. ADSL BBline monitor Web site High Application Throughput requires low delay and low packet loss QoS functionalities cannot improve Throughput Max TCP-IP data transfer Bit-Rate 13 Mbps* WEB FTP Application Throughput 7 Mbps* e.g. PC-web site speed test Tool e.g. Large File Download (with a browser) QoE Platforms (Application & Content Delivery, WEB Acceleration, Protocol Optimizations ) are needed to improve best-effort IP network performance and to reduce network TCO * Measures at my home, Italy, feb 2014

6 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 6

7 Application Throughput is lower than Connection Bit Rate 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 7

8 Application Throughput is lower than Connection Bit Rate and it gets worse for higher Bit Rates (2/2) ( # ) Akamai connection speed ( Throughput ) - (3Q13) (4Q13) (*) Ookla SpeedTest data ( Bit Rate) - (4Q13) For growing bit-rate: the ratio Throughput / BitRate decreases the bit rate capacity waste is more severe 8

9 Throughput and Bit Rate [ Mbps ] 10 º Fiberness Pieve Santo Stefano (Ar) Giugno 2014 Bit Rate and Throughput vs RTT and Packet Loss MAX TCP Throughput TCP throughput over xdsl/fttx access Long-term target Average DS BitRate EU ( # ) 20 7 Average DS Throughput EU (**) Average DS BitRate Italy (*) Average DS Throughput Italy (**) 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 Basso, et Al. Packet Loss Rate in the Access Through Application-Level Measurements, ACM SIGCOMM Helsinki, Aug

10 Index QoE platforms: the reason why How to improve Quality of Experience New business models Telecom Italia

11 11 10 º Fiberness Pieve Santo Stefano (Ar) Giugno 2014 How to improve Quality of Experience Multiple copies of contents Improve application throughput Reduce web page download time QoE platforms Content & Application Delivery networks, caching Reduce user-server 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.

12 CDN/TC Platforms improve best effort network performance UBB access Network Policy Control. QoE Platforms. Mobile Access Fixed Access Metro Regional Core International Network All-IP Domestic Network Content nearer to users better performance (higher throughput) Akamai 2012, «Empirical Network Analysis» 12

13 Throughput improvement by protocol enhancer 13 From 2 to 5 times higher throughput for many traffic types (e.g. FTP, HTTP, video HD )

14 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 14

15 15 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

16 16 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 Tx Network: ROADM in Core and Metro, WDM in Aggregation IP - Ethernet WDM MPLS IP - Ethernet ROADM MPLS IP - Ethernet ROADM QoE Platform deployment: Content Delivery Network Transparent caching Application Delivery Network Web Acceleration TO-BE AS-IS

17 17 10 º Fiberness Pieve Santo Stefano (Ar) Giugno 2014 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 On Net Caching in the Metro On Net Caching in the Core Off Net Caching Last Mile BB-UBB access areas CO/metro sites core sites Out of Telco ISP domain sites User s premises Internet Interconnection Point

18 18 10 º Fiberness Pieve Santo Stefano (Ar) Giugno 2014 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) 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 User s premises

19 Costs Saving due to Caches deployed in the network Network Costs Saving (%) as a function of Cx (*) Unitary upstream network Cost [K /Gbps] (*) Cx = 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

20 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 IP services edge Peer caches Access aggregation C = centralized architecture D = distributed architecture Network transport costs reduced 47% Source: ALU WP - VIDEO SHAKES UP THE IP EDGE;

21 Index QoE platforms: the reason why How to improve Quality of Experience New business models Telecom Italia

22 22 10 º Fiberness Pieve Santo Stefano (Ar) Giugno 2014 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 Two sides Business Model OTT CDN, ADN,... Services fees TELCO Internet Access fees + premium services Enduser

23 23 QoE enables Telco Premium services UBB+QoE = ACCESS MONETIZATION QoE improvement enables incremental revenues from NGAN & LTE To get a Premium Access Fee from UBB access: Average UBB price uplift access bit-rate improvement is not sufficient higher application throughput and lower download time are needed Western EU DSL Cable FTTx

24 24 10 º Fiberness Pieve Santo Stefano (Ar) Giugno 2014 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 traffic (QoE platforms) enabling network cost saving 3 options NO deal Payback Time = never Deal, but no use of QoE platforms Additional Revenues from OTT/CP Payback Time estimate > 25 years Deal, and use of QoE platforms Additional Revenues from OTT/CP Capex saving = 25%-40% Payback Time estimate = years

25 25 10 º Fiberness Pieve Santo Stefano (Ar) Giugno 2014 Business models transformation guidelines Content, Services Є Access fee Є BestEffort Dumb Pipe ADV Є Internet Best Effort OTT CP Content, Services Access fee Є Є Premium Access Fee Є Differentiated quality for IP delivery ADV QoE capable network Є Є OTT Content, Services CP : Router/Server/Cache OTT: Over The Top; CP: Content Provider; ADV: Advertising New IP Interconnection Policy Regulatory issues on Net Neutrality

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