10 th World Telecommunication/ICT Indicators Meeting (WTIM-12) Bangkok, Thailand, September 2012

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1 10 th World Telecommunication/ICT Indicators Meeting (WTIM-12) Bangkok, Thailand, September 2012 Contribution to WTIM-12 session Document C/12-E 26 September 2012 English SOURCE: TITLE: Nokia Siemens Network Measuring mobile broadband data traffic End user and access network centric approach

2 ITU ICT Indicators Meeting Measuring Mobile Broadband Data Traffic End user and access network centric approach Henri Helanterä, MSc Solution Architect Nokia Siemens Networks 1 Nokia Siemens Networks 2012

3 Table of Contents 1. Introduction: Future of Mobile Communications 2. Mobile Traffic Indicators Indicator: Traffic per User Indicator: Traffic per Device Model / Device Type Indicator: Hourly Share of Daily Traffic Indicator: Traffic per Cell/Cluster Indicator: Traffic per Technology Indicator: Traffic per Access Point Indicator: Traffic per Application Protocol / Protocol Category Indicator: Traffic per Domain / Service Indicator: Mobile Data Service Usage by Age 3. Data Collection and Dissemination 4. Conclusions and Recommendations 2 Nokia Siemens Networks 2012

4 The Future of Mobile Communications Mobile network traffic / Cost Mobile data traffic booms Global Monthly Mobile Data Traffic Forecast, in total* 7 EB 1.2 EB ~6X e Traffic Global Monthly Mobile Data Traffic Forecast, per user* 0.5 GB Network cost (2G/3G) ~3X 1.5 GB e New applications and devices drive usage Video Streaming, Mobile TV Music Downloading, Audio Streaming Voice over IP Communities, sharing Location Business on the move, Revenue Navigation Voice Data Profit Network cost (4G) Time Online Gaming File Transfer, Personal Cloud Source: NSN consulting; Cisco VNI mobile,2011;*1eb=1024pb,1pb=1024tb,1tb=1024gb 3 Nokia Siemens Networks 2012

5 Indicator: Traffic per User Traffic (UL+DL) in MB per User Users Share of Total Traffic (UL+DL) Users can be further grouped by data usage (i.e. # of users in certain usage range) Real-Time Traffic monitoring tools xdrs % 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Monthly Data Usage Histogram MB MB MB 1 GB + Share of Users (%) Share of Traffic (%) 4 Nokia Siemens Networks 2012

6 Indicator: Traffic per Device Model / Device Type Traffic (UL+DL) in MB per Device Model / Device Type Hourly Traffic for iphone 4 Total (MB) Uplink (MB) Downlink (MB) Real-Time Traffic monitoring tools Share of Traffic % 50% Subscribers share of Total Traffic 0 % 0% 50% 100% Share of Subscribers Modem iphone Other smartphones 5 Nokia Siemens Networks 2012

7 Indicator: Hourly Share of Daily Traffic Share of Traffic (UL/DL) per Hour of Daily Traffic Busy hour is 7% of daily traffic OSS PM counters Real-Time Traffic monitoring tools Voice traffic (Erlang) Data traffic (GB) 6 Nokia Siemens Networks 2012

8 Indicator: Traffic per Cell/Cluster Traffic (UL+DL) in MB/GB per Cell Cells can be grouped into clusters to provide a geographical level for analysis OSS Performance Management counters Real-Time Traffic monitoring tools xdrs Traffic 60 GB 30 GB 0 GB City 7 Nokia Siemens Networks 2012

9 Indicator: Traffic per Cell/Cluster 10% of cells contribute from 43% to 73% of total traffic (data volume) depending on hour Approximately 54% of total traffic is contributed by 15% of the cells during the busy hour 8 Nokia Siemens Networks 2012

10 Indicator: Traffic per Technology Traffic (UL+DL) per Radio Access Technology (2G/3G) Time spent on 2G/3G per User Real-Time Traffic monitoring tools Share of Traffic 2000 Share of 3G Usage Time for 3G Devices 19.76% 80.24% 3G 2G % 0-20% 20-50% 50-80% % # of devices (thousands) 9 Nokia Siemens Networks 2012

11 Indicator: Traffic per Access Point Traffic (UL+DL) in GB per Access Point Blackberry.net Hourly Traffic (GB) Real-Time Traffic monitoring tools Blackberry.net HourlyTraffic per User (MB) Less users at night but more traffic per user 10 Nokia Siemens Networks 2012

12 Indicator: Traffic per Application Protocol / Protocol Category Traffic (UL+DL) in GB per Application Protocol / Protocol Category Real-Time Traffic monitoring tools Share of Traffic (DL) 22% Web P2P Web Media News Groups Others 11 Nokia Siemens Networks 2012

13 Indicator: Traffic per Domain / Service Total Unique Users per Internet Domain (Service) Traffic (UL+DL) in GB per Internet Domain (Service) Total Traffic (GB) Total Unique Users Facebook Youtube Twitter Real-Time Traffic monitoring tools Facebook Youtube Twitter Success Rates (%) Facebook Youtube Twitter Text Image Video HTTP GET HTTP POST 12 Nokia Siemens Networks 2012

14 Indicator: Mobile Data Service Usage by Age Use of Mobile Data Services by Age / Age Group Traffic (UL+DL) per Mobile Data Service by Age / Age Group Years Years Years Years Years Surveys Real-Time Traffic monitoring tools Customer Relationship Management Systems Source: NSN study; base: 21,000 mobile phone owner respondents from maturing and emerging markets; multiple answers possible 13 Nokia Siemens Networks 2012

15 Data Collection and Dissemination Core network (SGSN, GGSN, Gn, Gi) is the best source of subscriber and application level data Operator PLMN Operator services RRC/RAB Failures LAC, SAC, RNC IMSI RNC MSISDN APN IMEI Qos Data Volumes Cause Codes SGSN MSISDN APN Billing Type Data Volumes Throughput IP-Address GGSN MMS Charging Streaming Downloading WAP Iu Gn Gi Operator IP network RNC Mediation Corporate Internet ISP Partner 14 Nokia Siemens Networks 2012

16 Data Collection and Dissemination (cont d) Web clients Management Customer Care Network Engineering Marketing & Sales Product Lifecycle Network Operations Customer Care Real-time analysis Long-term reporting Customer Analytics Data processing and enrichment Mediation & control Data warehouse Customer repository CRM BSS Inventory Data collection SGSN GGSN RNC 15 Nokia Siemens Networks 2012 Real-Time Traffic Monitoring tools / Signalling logs Gn, Gi probes IP Flows + DPI CDRs from SGSN, GGSN, ICD, PoC, IMS Terminal Agents

17 Conclusions and Recommendations Mobile data traffic behaves differently from traditional Voice and SMS Device evolution and diverse range of applications drive growth in data traffic Analysing mobile data usage across different dimensions (user, application/service, device, cell/cluster, time) is important to better understand the relation between traffic and revenue Understand how capacity is utilized in different parts of the network at different times (e.g. Central Business District and sub-urban housing areas) Understand different user profiles and how they drive traffic and revenue Understand how Over-The-Top services and applications contribute to traffic and revenue Applying Fair Usage Policy to balance traffic, revenue and QoS Sourcing and processing indicator data Sourcing and processing the transactional data needed for this analysis sets new requirements for mobile operators IT systems (big data) Volume of transactional data is far greater than that of traditional Network Management statistics data Analyzed information and insights need to be made available in near real-time for timely and automated decision making and actions 16 Nokia Siemens Networks 2012

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