cellular networks - courses.cs.duke.edu

Cellular Networks

Day 21

3 different Stake-holders

• The cell provider– Want to maximize revenue: charge you for every

bit

• The mobile device– Wants to maximize battery utilization: turn itself

off

• Content provider– Wants to maximize user experience: reduce

latency to the user

Cellular Core Network

eNodeB 3 S-GW 2

P-GW

3

S-GW 1

eNodeB 1

eNodeB 2

Internet andOther IP Networks

GTP Tunnels

UE 2

UE 1

LTE Data Plane is too Centralized

• UE: user equipment

• eNodeB: base station

• S-GW: serving gateway

• P-GW: packet data network gateway

• Data plane is too centralized

Scalability challenges at P-GW on charging and policy enforcement!


• The cell provider– Want to maximize revenue: charge you for every

bit


off


latency to the user

Example: RRC State Machinefor a Large Commercial 3G Network

Promo Delay: 2 Sec

DCH Tail: 5 sec

FACH Tail: 12 sec

DCH: High Power State (high throughput and power consumption)FACH: Low Power State (low throughput and power consumption)IDLE: No radio resource allocated

Tail Time: waiting inactivity timers to expire

Courtesy: Feng Qian et al.


• The cell provider– Want to maximize revenue: charge you for

everybit


off


latency to the user

Outline

• Discuss Network Performance

• Discuss Implications of mobile device power optimizations

• Discuss implications of cellular network on performance

8

LTE Control Plane is too Distributed

• Problem with Inter-technology (e.g. 3G to LTE) handoff

• Problem of inefficient radio resource allocation

User

Equipment

(UE) Gateway

(S-GW)

Mobility

Management

Entity (MME)

Network

Gateway

(P-GW)

Home

Subscriber

Server

(HSS)

Policy Control and Charging Rules Function (PCRF)

Station

(eNodeB)

Base Serving Packet Data

Control Plane

Data Plane

• No clear separation of control plane and data plane

Network characteristics• 4GTest on Android

– http://mobiperf.com/4g.html

– Measures network performance with the help of 46 M-Lab nodes across the world

– 3,300 users and 14,000 runs in 2 months 10/15/2011 ~ 12/15/2011

20

25

30

35

40

45

50

-130 -120 -110 -100 -90 -80 -70

La

titu

de

Longitude

WiFiWiMAX

LTE

4GTest user coverage in the U.S.Courtesy: Junxian Huang et al.

Downlink throughput• LTE median is 13Mbps, up to 30Mbps

– The LTE network is relatively unloaded

• WiFi, WiMAX < 5Mbps median

0

5

10

15

20

25

30

WiFi LTE WiMAX eHRPD EVDO_A 1 EVDO_A 2 HSDPA 1 HSDPA 2 0

100

200

300

400

Y1

: N

etw

ork

th

rou

gh

pu

t (M

bp

s)

Y2

: R

TT

(m

s)

Downlink throughput (Y1)Uplink throughput (Y1)

RTT (Y2)RTT jitter (Y2)

Uplink throughput• LTE median is 5.6Mbps, up to 20Mbps

• WiFi, WiMAX < 2Mbps median

0

5

10

15

20

25

30


100

200

300

400

Y1

: N

etw

ork

th

rou

gh

pu

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bp

s)

Y2

: R

TT

(m

s)



RTT• LTE median 70ms

• WiFi similar to LTE

• WiMAX higher

0

5

10

15

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25

30


100

200

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400

Y1

: N

etw

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Y2

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Outline




The RRC State Machine for UMTS Network

• State promotions have promotion delay

• State demotions incur tail times

Tail Time

Tail Time

Delay: 1.5sDelay: 2s Channel Radio

Power

IDLE Not allocated

Almostzero

CELL_FACH Shared, Low Speed

Low

CELL_DCH Dedicated, High Speed

High


Example: RRC State Machinefor a Large Commercial 3G Network

Promo Delay: 2 Sec

DCH Tail: 5 sec

FACH Tail: 12 sec

DCH: High Power State (high throughput and power consumption)FACH: Low Power State (low throughput and power consumption)IDLE: No radio resource allocated

Tail Time: waiting inactivity timers to expire


Why State Promotion Slow?

• Tens of control messages are exchanged during a state promotion.

RRC connection setup: ~ 1sec Radio Bearer Setup: ~ 1 sec+Figure source: HSDPA/HSUPA for UMTS: High Speed Radio Access for Mobile Communications. John Wiley and Sons, Inc., 2006.

Example of the State Machine Impact:Inefficient Resource Utilization

FACH and DCH

Wasted Radio Energy 34%

Wasted Channel Occupation Time 33%

A significant amount of channel occupation time and battery life is wasted by scattered bursts.

State transitions impact end user experience and generate

signaling load.

Analysis powered by the ARO tool


Case Studies

• Study 17 popular Android applications– All in the “TOP Free” Section of Android Market

– Each has 250,000+ downloads as of Dec 2010

• Pinpoints resource inefficiency for many popular applications. For example,– Pandora Streaming

High radio energy overhead (50%) of periodic measurements

– Fox NewsHigh radio energy overhead (15%) due to users’ scrolling

– Google SearchHigh radio energy overhead (78%) due to real-time query suggestions


Case Study: Pandora Music

Problem: High resource overhead of periodic audience measurements (every 1 min)Recommendation: Delay transfers and batch them with delay-sensitive transfers


Case Study: Fox News

Problem: Scattered bursts due to scrollingRecommendation: Group transfers of small thumbnail images in one burst


Outline




BackgroundWant to understand how carrier topology,

routing choices and peering arrangements

affect Internet service performance for mobile

devices

Public Internet

Google

Mobile

Carrier

Ingress Point

Peering

Point6

Server

Location

Connection Setup

Server

A

Server

B

www.google.com

→ Server A

Update mapping based

on observed performance

DNS

Resolver

7

Optimal Routing

In the optimal case, all parts are located

in the same metro area

8

Metro Area

Types of Path Inflation

• Carrier Access Inflation

• Interdomain Inflation

• Carrier Access-Interdomain Inflation

• Provider Backbone Inflation

Four types of path inflation based on the

path segment where inflation occurs

14

Path Features

Local Metro Area Remote Metro

Area

Mobile

Access

Internet

Access

Google

Access

15

Carrier Access Inflation

Local Metro Area

(NYC)

Remote Metro

Area (Boston)

Traffic enters public Internet in remote area

Mobile

Access

Internet

Access

Google

Access

16

Interdomain Inflation

Traffic enters Google’s network in remote

area

Mobile

Access

Internet

Access

Google

Access

Local Metro Area

(NYC)

Remote Metro

Area (BOSTON)

17

Carrier Access-Interdomain

Inflation

Traffic enters public Internet in remote area,

and accesses a Google server in local area

Carrier

Access

Internet

Access

Google

Access

Local Metro Area

(NYC)

Remote Metro

Area (Boston)

18

Provider Backbone Inflation

Traffic enters Google’s network in local

area, but accesses a server in remote area

Carrier

Access

Internet

Access

Google

Access

Local Metro Area (NYC Remote Metro

Area (Boston)

19

DNS Clustering

Server

A

Server

B

www.google.com

→ Server A

Update mapping based

on observed performance

DNS

Resolver

20

Finding Ingress Points

Find ingress points through graph aggregation

of traceroutes, and DNS name resolution

Private or

unresolvable

hops

cr2.sffca.ip.att.net

→ San Francisco,

CA

21

Finding Peering Points

Find peering points by evaluating last hop

before entering Google’s network

Mobile Carrier (e.g.

Verizon)

Google

POS4-0-0.GW4.SJC7.ALTER.NET

→ San Jose, CA

22

209.85.249.3

(whois: Google)

Observed Ingress Points: SprintSeattle

San Francisco

Los

Angeles

Phoenix

Dallas

Omaha

Chicago

Kansas City

Miami

Atlanta

Washington

New York

12 or more ingress points, and observed new ones

throughout measurement study

30

Case Study 1: Lack of Ingress

Chicago

New

York

AT&T New York

traffic enters public

Internet in Chicago

area

1074km extra

distance

16ms extra latency

24

Case Study 2: Lack of Peering

San

Francisc

o

SeattleAT&T Seattle traffic

enters Google’s

network in Bay area

1089km extra

distance

16ms extra latency

25

Case Study 3: Client Clustering

San

Francisc

o

SeattleVerizon clients from

San Francisco and

Seattle directed to

same Google server

1089km extra

distance

16ms extra latency

26

Inflation Breakdown: AT&T

Area Count

Fractio

n

Inflated

Cause(s)

Extra

distanc

e

Extra

PLT

San

Francisco7759 1.00

Ingress,

Peering4200km 315ms

Seattle 303 1.00 Peering 2106km 158ms

New York 2720 1.00 Ingress 2148km 161ms

All clients in at least three metro areas

observed increased latency

due to lack of ingress and peering points27

Cellular Networks

• LTE protocol improves mobile client energy savings– Overheads still exists

• Path inflation exists in other parts of the world– Nawras clients in Oman are directed to

Paris (France) instead of New Delhi (India)

→ Round-trip distance increased by over 7000km

– Movistar clients in Spain are directed to London (UK)

instead of targeting local servers in Madrid

35

cellular networks - courses.cs.duke.edu

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