Cell Spotting - SIGCOMM

©2017 AKAMAI | FASTER FORWARDTM

Cell Spotting:Studying the Role of Cellular Networks

in the Internet

John RulaFabián E. Bustamante

Moritz Steiner*

*

✝*

✝✝

1


Understanding cellular growth is critical


• On subscriptions• >7.3 billion mobile phone subscriptions worldwide

• And content consumption• 3.1hr/day of digital media (1.4x as on

desktops/laptops)*• 50% of web traffic**• 91% of daily Facebook usage

Growing dominance of mobile

* eMarketer 4/17** Cisco VNI 2016


• 4.2x more mobile broadband subscribers• Mobile traffic growing at 45% annually• A path to connect the other half of the world

Part of the growth is cellular

But how much? And where?


Access TECHNOLOGY

✚Fastest growing tech

Mobile

⎼Hard to measure

CellularAccess DEVICE

✚Easy to measure: User-agent, device properties

Mobile

⎼Not a good proxy: >50% of mobile traffic is over WiFi

≈ Cellular


• Can’t tell which traffic is cellular • Don’t know how cell networks are organized• Can’t see global usage trends

Understanding cellular growth

Challenges?


• Approach to label cell networks and traffic• Validation with ground truth data• Detailed look at global cellular usage

Our work


• Motivation• Approach• Validation• Findings


Identify cellular IP addresses

• Detect network interface from browser API• Beacon data for cell networks identification• Requests to a large CDN as a proxy for traffic

Summary of our approach


Network interface from browserNetwork Information API• Detect client’s network connectivity• Notify of connection changes• Measure recent RTT and T-put

Limitation: Only knows client’s network interface

ConnectionType: WiFi

Hotspot

ConnectionType: Cell

ConnectionType: WiFi


From CDN Real User Monitoring (RUM) system• Network Information API available in 15% of beacons• 100s of millions of beacons per month

Calculate Cellular Ratio for each /24 and /48 CIDR• CR = (Cellular Hits) / (Cellular Hits + WiFi Hits)

Label subnets with Ratios > 0.5 as CELLULAR

Beacon data for cell identification


Calculate demand across entire CDN platform

Demand Unit (DU): relative measure of all CDN HTTP requests (out of 100,000)

Platform data for cell demand




High precisionbut potential for false-negatives

Diverse operators for validation

Operator Precision Recall F1European [Mixed] 0.99 0.82 0.90US [Dedicated] 1.00 1.00 1.00Middle Eastern [Mixed] 0.98 0.98 0.98




Worldwide Cellular Subnets:Global cellular IP space

Number of /24 350,687Number of /48 23,230Active IPv4 7.3%Active IPv6 1.2%


Global cellular IP space

Continent # /24 # /48 % Active IPv4 % Active IPv6Africa 79,091 28 53.2% 2%Asia 86,618 4,613 5.7% 0.5%Europe 65,442 2,117 4.8% 0.3%North America 27,595 16,166 2.1% 9.9%Oceania 4,352 35 5.4% 0.07%South America 87,589 271 22.6% 0.9%

Spread across 668 Cellular ASes.


Global cellular IP space

Continent # /24 # /48 % Active IPv4 % Active IPv6Africa 79,091 28 53.2 2Asia 86,618 4,613 5.7 0.5Europe 65,442 2,117 4.8 0.3North America 27,595 16,166 2.1 9.9Oceania 4,352 35 5.4 0.07South America 87,589 271 22.6 0.9

49.2% of all IPv6 demand is cellular


16.2% from cellular addresses end 2016

Global cellular demand

16.20%

83.80%

Cellular Fixed Line

3% 3% 4%

16%

35%

39%

OceaniaAfricaSouth AmericaEuropeNorth AmericaAsia


From ~26% (Asia, Africa) to 12% in Europe

Cell fraction of demand by continent

0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00%

Oceania

Africa

South America

Europe

North America

Asia Percentage of continent demand that is cellular:


And by country

0.0 0.2 0.4 0.6 0.8 1.0CeOOuODU DemDnd RDtiR

10-2

10-1

100

101

102

103

104

1RUm

. C

eOOu

ODU

Dem

Dn

d x

10

0,0

00

1AE86AAFA62C

United States IndiaIndonesia

Ghana


Top networks dominate demand

100 101 102

5DnNed A61 #

10-6

10-5

10-4

10-3

10-2

10-1

Cellu

lDr

Dem

Dn

d (

%)

Top 10 make up 38% of cellular demand


Serve both cellular and fixed-line access networksKey networks are mixed

Cellular ASNs Cellular Demand

0ixed DedicDted0

10

20

30

40

50

60

70

DemDnd

0ixed DedicDted0

10

20

30

40

50

60

A61

Cou

nt

Majority of networks are

mixed

Majority of demand

comes from dedicated


Mixed networks vary greatly

0 100 200 300 400 500 6005DnNed A61s

10−1

100

101

102

103

Dem

Dnd

(DU)

CellulDU)ixed

Cellular dominates some networks’ demand

Less so in other networks

Need prefix-level granularity for analysis


Demand concentrated in few subnets

100 101 102 103 104

5Dnked /24 Subnet

10-5

10-4

10-3

10-2

10-1

100

101

Dem

Dn

d

CellulDrFixed

Top 30 account for 73% of cellular demand

Top 2,600 account for 73% of fixed demand

Similar patterns across all ASes

Top 3 account for 53% of cellular demand


Most cellular clients share DNS resolvers

0.0 0.2 0.4 0.6 0.8 1.016 CellulDr FrDFtiRn

0.0

0.2

0.4

0.6

0.8

1.0

CD

F

ResRlver CellulDr FrDFtiRn

20% Fixed line

~20% Cellular60% Mixed

Most DNS resolvers shared with fixed line


Public resolver usage higher outside US

U.S.

Brazil Vietnam

South Africa

India

Hong Kong

Nigeria

Algeria


This work …• Accurate identification of cellular IP address space• Measure IP-level activity of cellular access• Cellular usage and behavior across different axes

Much to be done …• 19.7% cell demand increase last 8 months• Temporal dynamics of assignment• Sampling, …

Looking at the role of cellular


Questions?


Sept. 2016 launched India-wide 4G LTE“FREE, Unlimited Data for 6 months!”

Cellular Case Study: Reliance JioTech Capability, Deployment, Economics

0

5

10

15

20

25

30

35

40

Perc

ent C

ount

ry T

raffi

c (%

)

Pre-promotion, June 2016: Reliance carried 1.2% of India’s traffic

37.7% of India’s traffic by June 2017


13.7% of Traffic is Cellular86.3% is Fixed Line

Country Comparison: France & Indonesia

Dem

and

(DU

)

ASN

Top 10 French ISPs

Cellular Traffic Fixed Line Traffic

!

Dem

and

(DU

)

ASN

Top 10 Indonesian ISPs

Cellular Traffic Fixed Line Traffic

66.8% of Traffic is Cellular33.2% is Fixed Line4 out of top 5 ISPs are majority cellular

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Cell Spotting - SIGCOMM