10th World Telecommunication/ICT
Indicators Meeting (WTIM-12)
Bangkok, Thailand, 25-27 September 2012
Contribution to WTIM-12 session
Document C/12-E
26 September 2012
English
SOURCE:
Nokia Siemens Network
TITLE:
Measuring mobile broadband data traffic – End user and access network centric
approach
ITU ICT Indicators Meeting
Measuring Mobile Broadband Data Traffic
– End user and access network centric approach
Henri Helanterä, MSc
Solution Architect
Nokia Siemens Networks
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© Nokia Siemens Networks 2012
Table of Contents
1. Introduction: Future of Mobile Communications
2. Mobile Traffic Indicators
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–
–
–
–
–
–
–
–
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
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© Nokia Siemens Networks 2012
Henri Helanterä / 25.09.2012
The Future of Mobile Communications
New applications and devices drive usage
Mobile data traffic booms
Global Monthly Mobile Data
Traffic Forecast, in total*
7 EB
Global Monthly Mobile Data
Traffic Forecast, per user*
~6X
0.5 GB
1.2 EB
2012
2017e
Mobile network traffic
/ Cost
~3X
2012
Traffic
Communities, sharing
Video Streaming,
Mobile TV
1.5 GB
Music Downloading,
Audio Streaming
2017e
Network cost
(2G/3G)
Voice over IP
Location
Business on the move,
email
Navigation
Revenue
Profit
Network cost (4G)
Online
Gaming
Time
Voice
Data
Source： NSN consulting； Cisco VNI mobile，2011；*1EB=1024PB，1PB=1024TB，1TB=1024GB
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© Nokia Siemens Networks 2012
Henri Helanterä / 25.09.2012
File Transfer,
Personal
Cloud
Indicator: Traffic per User
Indicator Definition
• 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)
Monthly Data Usage Histogram
100.00%
90.00%
80.00%
70.00%
60.00%
Share of Users (%)
50.00%
Share of Traffic (%)
40.00%
Data sources
• Real-Time Traffic monitoring tools
• Gn/Gi interface probes
• xDRs
30.00%
20.00%
10.00%
0.00%
0-100 MB
100-500 MB
Source： NSN customer project
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© Nokia Siemens Networks 2012
Henri Helanterä / 25.09.2012
500-1000 MB
1 GB +
Indicator: Traffic per Device Model / Device Type
Indicator Definition
• Traffic (UL+DL) in MB per Device
Model / Device Type
Hourly Traffic for iPhone 4
200000
150000
Total (MB)
100000
Uplink (MB)
50000
Downlink (MB)
0
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© Nokia Siemens Networks 2012
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11
12
13
14
Subscribers’ share of Total Traffic
Modem
iPhone
Other
smartphones
50%
0%
0%
50%
Share of Subscribers
Source： NSN customer project
5
9
100%
Share of Traffic
Data sources
• Real-Time Traffic monitoring tools
• Gn/Gi interface probes
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Henri Helanterä / 25.09.2012
100%
Indicator: Hourly Share of Daily Traffic
Indicator Definition
• Share of Traffic (UL/DL) per Hour
of Daily Traffic
Data sources
• OSS PM counters
• Real-Time Traffic monitoring tools
• Gn/Gi interface probes
Busy hour is 7% of
daily traffic
35000
30000
25000
20000
15000
10000
5000
0
400
300
200
Data traffic (GB)
100
0
00
03
06
09
12
Source： NSN customer project
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© Nokia Siemens Networks 2012
Voice traffic (Erlang)
Henri Helanterä / 25.09.2012
15
18
21
Indicator: Traffic per Cell/Cluster
Indicator Definition
• Traffic (UL+DL) in MB/GB per Cell
– Cells can be grouped into clusters
to provide a geographical level for
analysis
Data sources
• OSS Performance Management
counters
• Real-Time Traffic monitoring
tools
• Gn/Gi interface probes
• xDRs
Traffic
60 GB
30 GB
0 GB
City
Source： NSN customer project
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© Nokia Siemens Networks 2012
Henri Helanterä / 25.09.2012
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
Source： NSN customer project
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© Nokia Siemens Networks 2012
Henri Helanterä / 25.09.2012
Indicator: Traffic per Technology
Indicator Definition
• Traffic (UL+DL) per Radio Access
Technology (2G/3G)
• Time spent on 2G/3G per User
Data sources
• Real-Time Traffic monitoring tools
• Gn/Gi interface probes
Share of 3G Usage Time for 3G Devices
Share of Traffic
2000
1500
19.76%
3G
2G
80.24%
# of
devices
(thousands)
1000
500
0
0%
Source： NSN customer project
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Henri Helanterä / 25.09.2012
0-20%
20-50%
50-80%
80-100%
Indicator: Traffic per Access Point
Blackberry.net – Hourly Traffic (GB)
Indicator Definition
• Traffic (UL+DL) in GB per
Access Point
Data sources
• Real-Time Traffic
monitoring tools
• Gn/Gi interface probes
Blackberry.net – HourlyTraffic per User (MB)
Source： NSN customer project
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Less users at night
but more traffic per
user
Indicator: Traffic per Application Protocol / Protocol Category
Indicator Definition
• Traffic (UL+DL) in GB per
Application Protocol / Protocol
Category
Data sources
• Real-Time Traffic monitoring tools
• Gn/Gi interface probes
Share of Traffic (DL)
22%
Source： NSN customer project
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Web
P2P
Web Media
News Groups
Others
Indicator: Traffic per Domain / Service
Indicator Definition
• Total Unique Users per
Internet Domain (Service)
• Traffic (UL+DL) in GB per
Internet Domain (Service)
Total Traffic (GB)
Total Unique Users
100
300000
250000
90
200000
150000
80
100000
70
50000
0
60
Facebook
Youtube
Twitter
50
40
Data sources
• Real-Time Traffic monitoring
tools
• Gn/Gi interface probes
Success Rates (%)
30
100
80
60
40
20
0
20
10
0
Facebook
Text
Youtube
Image
Source： NSN customer project
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© Nokia Siemens Networks 2012
Henri Helanterä / 25.09.2012
Twitter
Video
Facebook
Youtube
HTTP GET
Twitter
HTTP POST
Indicator: Mobile Data Service Usage by Age
Indicator Definition
• Use of Mobile Data Services by
Age / Age Group
• Traffic (UL+DL) per Mobile Data
Service by Age / Age Group
Data sources
• Surveys
• Real-Time Traffic monitoring
tools
• Gn/Gi interface probes
• Customer Relationship
Management Systems
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© Nokia Siemens Networks 2012
15-26 Years
26-35 Years
36-45 Years
46-55 Years
56-70 Years
Source： NSN study; base: 21,000 mobile phone owner respondents from maturing and emerging markets;
multiple answers possible
Henri Helanterä / 25.09.2012
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
• Billing Type
• Data Volumes
• Throughput
• IP-Address
• MSISDN
• APN
• IMEI
• Qos
• Data Volumes
• Cause Codes
Charging
SGSN
Iu
MMS
Streaming
E-mail
Downloading WAP
GGSN
Gn
Gi
Operator IP
network
RNC
Corporate
Mediation
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© Nokia Siemens Networks 2012
Henri Helanterä / 25.09.2012
Internet
ISP
Partner
Data Collection and Dissemination (cont’d)
…
Management
Customer
Care
Network
Engineering
Marketing
& Sales
Product Lifecycle
Network
Operations
…
Web
clients
Customer Care
Data processing
and enrichment
Real-time analysis
Long-term reporting
Customer Analytics
CRM
Mediation
& control
Data
warehouse
Customer
repository
BSS
Inventory
Data collection
SGSN
GGSN
RNC
Real-Time Traffic
Monitoring tools /
Signalling logs
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© Nokia Siemens Networks 2012
Terminal Agents
Gn, Gi probes
IP Flows + DPI
Henri Helanterä / 25.09.2012
CDRs from SGSN,
GGSN, ICD, PoC, IMS
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
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© Nokia Siemens Networks 2012
Henri Helanterä / 25.09.2012