20120731 analyzing the network friendliness of mobile applications

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Analyzing the NetworkFriendliness of Mobile Applications

version 1.0


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Analyzing the Network Friendliness

of Mobile Applications

Issue: 1.0

Date: 2012-07-05

Author: Song Jiantao

Email: [email protected]

Summary ..............................................................................................................................1

1 Preface ..............................................................................................................................2

2 Application Network Friendliness Optimization System .....................................................4

3 Method for Analyzing the Network Friendliness of Applications ........................................6

4 Apps Insider ....................................................................................................................10

4.1 Description ...................................................................................................................................................10

4.2 Apps Insider Case Study ................................................................................................................................12

5 Healthy Development of the Application Network Friendliness Optimization System .......14

6 References .......................................................................................................................16


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Summary

In recent years, the development of smartphones has made great progress. Mobile

application development for smartphones has become the next big thing in a post-PC

era. But since most developers are lacking in their understanding of mobile networks

and the behaviors of applications on mobile networks, these applications may be

unfriendly towards mobile networks and cause the following problems: high device

power consumption, frequent signaling storms on mobile networks, low utilization

efciency of network resources, deterioration in user experience, and threats to user

privacy and network security.

To guide the development of network-friendly applications, the GSM Associationhas released Guidelines for Development of Network Friendly Applications, and

hosted a Smarter App Challenge event. To further cultivate an application network

friendliness optimization system, Huawei has provided the Apps Insider, an automatic

tool for analyzing the network friendliness of applications. This white paper introduces

the methodology of analyzing network friendliness, describes the Apps Insider toolkit,

and gives a case study.

The Apps Insider assesses the network friendliness from the perspective of user

experience and network impact respectively, including assessment indexes like user

experience, device power consumption, signaling consumption, trafc consumption,

connection consumption, multi-radio capability (UMTS/LTE/Wi-Fi), privacy and security.

The Apps Insider adopts the client/server model: clients collect the measurementresults and report them to the server; the server calculates the average network

friendliness score of each application based on the score of each assessment index

and its weight; then it ranks applications by network friendliness. By analyzing the key

network friendliness indexes, the Apps Insider provides suggestions on application

development and network optimization to improve network friendliness.

Industry partners including developers and operators are welcome to use the Apps

Insider in Huaweis mLAB to improve the network-friendliness of applications.

Huaweis mLAB will also regularly release the network friendliness ranking of popular

applications and provide suggestions for optimizing their network friendliness.

Through continuous efforts to analyze, assess, optimize, and manage the network

friendliness of mobile applications, win-win outcomes for operators, application

developers, device vendors, network equipment suppliers, and end users will be made

possible.


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1 Preface

In recent years, the development of smartphones has made great progress. According

to Gartner, Inc., worldwide smartphone sales to end users soared to 472 million

units in 2011, up 58% from the previous year. Mobile application development

for smartphones has become the next big thing in a post-PC era. Most mobile

applications are developed based on the experience of fixed network applications.

As a result, these applications may be unfriendly towards mobile networks and cause

the following problems: high device power consumption, frequent signaling storms

on mobile networks, low utilization efficiency of network resources, deterioration

in user experience, and threats to user privacy and network security. To develop

friendly applications for mobile networks, developers must have a comprehensiveunderstanding of mobile networks and the behaviors of applications on mobile

networks.

On UMTS networks, UE (user equipment) has two basic operation modes: idle and

connected. In idle mode, the UE is in standby mode, no service is running, and the UE

is not connected to the Universal Terrestrial Radio Access Network (UTRAN). When

the Radio Resource Control (RRC) connection is set up, the UE is switched to the

connected mode. In connected mode, the UE has the following states: Cell-DCH, Cell-

FACH, and Cell/URA-PCH. In the Cell-DCH state, the UE has a dedicated channel (DCH

or HSPA) and consumes more power to transmit data at a faster rate. In the Cell-FACH

state, the UE consumes less power as data is transmitted at a lower rate. In the Cell/

URA-PCH state, the UE has no data transmitted on either the uplink or the downlink

and needs only the same amount of power as idle mode to retain the connection.

To efficiently utilize wireless resources and reduce power consumption, RRC state

transitions can be performed by using the dynamic channel allocation algorithm based

on the occupied radio link control (RLC) buffer. Signaling consumption varies according

to the state transition path. To avoid a ping-pong state transition, the DCH2FACH

transition is triggered when the deactivated timer T1 (2 to 10 seconds) expires after

data transmission is completed on the DCH/HSPA. Similarly, the FACH2PCH transition

is triggered when the deactivated timer T2 (2 to 10 seconds) expires.

The following is an example of the network-unfriendliness of a mobile application:

A social network application obtains the updates of friends by frequently polling the

server (known as a heartbeat). The heartbeat has little impact on xed networks. On

a wireless network such as the UMTS, however, a complete signaling process must be

performed for each heartbeat, for every upward transition (PCH2FACH, FACH2DCH)

and downward transition (DCH2FACH, FACH2PCH) of the RRC state on the signaling

plane. The user may obtain no valuable information from the heartbeat (his/her friends

may make no updates during heartbeat intervals), and the power and trafc are simply

wasted. Frequent heartbeats cause frequent RRC state transitions, consuming large


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quantities of signaling resources and increasing the CPU load of the interface board on

the control plane.

Currently, network friendliness design and the optimization of mobile applications have

gained extensive attention in both academic and industry elds. The GSM Association

has created the Guidelines for Development of Network Friendly Applications. AT&Ts

research institute has developed the Mobile Application Resource Optimizer (ARO) in

cooperation with University of Michigan. The AT&T ARO consists of a data collector

and data analyzer. The data collector tests applications by using a UE and records TCPsessions, HTTP packets and contents, screen video (3 fps), user input, battery level,

and GPS/camera/Bluetooth usage, and sends the test results to the data analyzer. The

data analyzer analyzes the impacts of mobile applications on wireless networks and

device power consumption based on the wireless network model and device power

consumption model.

Huaweis mLAB provides a wireless network environment that integrates GSM, UMTS,

LTE and Wi-Fi networks with the Apps Insider, an automatic tool for analyzing the

network friendliness of applications. With the wide use of smartphones, malware is

used to intrude on users privacy and attack wireless networks, posing great threats

to mobile users and network security. The Apps Insider assesses user experience,

power consumption, signaling consumption, traffic consumption and connection

consumption of applications in terms of multi-radio (UMTS/LTE/Wi-Fi) capability,privacy and security.


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Analysis

Use the Apps Insider to

analyze the UMTS/LTE/

Wi-Fi throughput rate,

RRC state transition

process, DL/UL signaling

consumption, terminal

power consumption,

connection consumption,

and privacy and s ecurity,

and save the analysis

results into the database.

Dene baselines for assessing

the user experience, terminal

power consumption,

signaling consumption, owconsumption, connection

consumption, multi-radio

capability, and privacy and

security, compare the analysis

results with the baselines,

score the network friendliness

of each application according

to the weight of each index,

and arrange the order of

applications according to the

aggregated score.

Identify key network

friendliness indexes

based on the comparison

result of each network

friendliness index with

its baseline, analyze the

key network friendlinessindexes, and provide

suggestions on application

development and network

optimization.

Establish an open

application network

friendliness assessment

center based on the

mLAB and formulate

regulations for assessing

and testing the network

friendliness of applications

to help developers publish

applications on application

stores, such as App Store

and Google Play.

Assessment

Optimization

Management

2 Application Network FriendlinessOptimization System

Currently, most mobile applications are developed based on the experience of xed

network applications. Unfortunately, developers tend to typically lack an understanding

of mobile networks and the behavior of applications on mobile networks. As a

result, these applications may be unfriendly towards mobile networks and cause the

following problems: high terminal power consumption, frequent signaling storms on

mobile networks, low utilization efciency of network resources, deterioration in user

experience, and threats to user privacy and network security. Huaweis mLAB aims to

build an application network friendliness optimization system that can analyze, assess,

optimize and manage the network friendliness of mobile applications to create win-

win outcomes for operators, application developers, device vendors, network vendors

and end users.

Figure 2-1 Application network friendliness optimization system

Operator

Operator

Terminal

Terminal

Network

Network

User

Fighting alone

Win-Win

User

Application

Application


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Analysis

Analyzes the following data and saves the analysis results into a database: Traffic

Consumption (DL/UL, UMTS/LTE/Wi-Fi), RRC state transition, session number

consumption, device power consumption, privacy and security measures.

Assessment

Dene baselines for assessing user experience(key QoE indexes for different application

categories, e.g. MOS for VoIP, Page Loading Latency for web browsing), device

power consumption, signaling consumption, traffic consumption, session number

consumption, multi-radio capability, as well as privacy and security, and compares the

analysis results with the baselines, scores the network friendliness of each application

according to the weight of each index, and arranges the order of applications

according to their aggregated score.

Optimization

Identies key network friendliness indexes based on comparison results and provide

suggestions on application development and network optimization.

Management

Based on statistical analysis, app platforms like the Apple App Store and GooglePlay would be able to formulate required network friendliness rules and criteria

for published apps. End users would also be able to select apps according to their

friendliness ranking.


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3 Method for Analyzingthe Network Friendliness ofApplications

The method for analyzing the network friendliness of applications is as follows:

Collect data from the client and assistant tools (RNC signaling tracing tool, Agilent

power consumption analyzer and Wireshark packet catcher), assess the applications in

terms of user experience, device power consumption, signaling consumption, trafc

consumption, connection consumption, multi-radio capability (UMTS/LTE/Wi-Fi), as

well as privacy and security, then score the network friendliness of each application

according to the weight of each index, and arrange the order of applications by

aggregated score.

Figure 3-1 Method for analyzing the network friendliness of applications

Application /Classifcation

Weight ofassessment indexes

Data collectionand processing

Assessment andranking

Assessmentindexes

Optimizationsuggestions

Whatsapp/IM Wi-Fi DL/UL throughput rate trafc consumption Weight of trafc consumption

Assess the networkfriendliness of applications

User experienceassessment report

Network impactassessment report

Network optimizationsuggestions

Application developmentsuggestions

iMessage/IM3G/LTE DL/UL throughput Connection consumption Weight of connection

consumption

Rank applications ineach type

viber/VoIPRRC state transition User experience Weight of user experience

Identify key networkfriendliness indexes

Youtube/Video

Duration of each RRC state Multi-radio capability Weight of multi-radio

Facebook/SNS

Signaling quantity Signaling consumption Weight of signalingconsumptionSafari/Web

Power consumption analysismodel

Terminal power consumptionWeight of terminal power

consumptionGmail/MAIL

APP security analysis Privacy and security Weight of privacy and securityiCloud/Cloud


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The procedure for analyzing the network friendliness of applications is as follows:

Step 1 Classify applications and set the typical usage profile. In this step, select

the applications to be tested, record the phone model, operating system version,

name, and version of each application, and classify the applications. Applications can

be classied into the following types and their corresponding usage prole could be

predened.

Step 2 Collect and process raw data. In this step, select various test scenarios

including foreground with no user input, typical usage scenarios(multiple predened

usage proles), and background, collect the raw data with assistant tools (air interface

signaling analyzer, Gi interface IP packet analyzer, and Agilent power consumption

analyzer) and the data reported by devices, then save the data into the database.

Calculate assessment indexes from raw data and save in database, re-generate

baseline for each index.

Application

CategoryExamples Usage Proles in Active State: Key Characteristics

Instant Messaging

(IM)

WhatsApp, MSN, QQ,

iMessage

Send/Receive Messages Per Hour

Average Message Size(MB)

Average Messaging Interval(Seconds)

Social Networking

Services (SNS)

Facebook, Twitter, Sina

Weibo

Average User Prole Browsing Times Per Hour

Average Number of Posts/Comments Per Hour

Sent/Received with/without Pictures(Y/N)

Video Netix, Youtube, Youku

Video File Formats(MP4/FLV/H.264/)

Average Video File Size(MB)

Average Video Duration(Minutes)

VoIP Skype, Viber Average Call Duration(Seconds)

Web BrowsingChrome, IE, Safari,

Firefox, Opera, UCWeb

Average Page Size(MB)

Average Dwell Time on a Page(Seconds)

Cloud ServiceDropbox, Google Drive,

iCloud, SkyDrive

File Type of Syncing(DOC/PICTURE/CONTACTS/)

Average File Size(MB)

Average File Syncing Interval(Seconds)

Email Hotmail, Gmail

Average Mail Size(MB)

Average Number of Mails Sent/Received Per Hour

Average Mail Transfer Interval(Seconds)


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Figure 3-3 Device power consumption and signaling consumption estimation

based on RRC state transition

260mA

116mA

IDLE2D

F2D

P2F

D2FT1 = 2~10s

T2 = 2~10s

T3

F2PP2IDLE

3mA

RRC State TransitionProcess

SignalingQuantity

P2F;F2P 7

P2F;F2D;D2F;F2P 28

IDLE2D; D2F; F2P; P2IDLE 35

DCH/HSPA

FACH

PCHIDLE

Power consumption

baseline in each RRC state

Signaling consumption

NOTE:

1. User Experience as assessment indexes excludes device power consumption, privacy

and security, includes voice quality for VoIP service, page loading latency for web

browsing, and video quality for video service.

2. Power consumption of a device can be calculated based on the power consumption

baseline of each RRC state and the RRC state transition process instead of using the

power consumption tool which is more efcient but less accurate than using power

consumption reported by the device/test tool.

NOTE:

Power consumption baseline in each RRC state (Y axis) is the default value (based on

mLAB test results) for estimation and could be changed with conguration GUI.

Figure 3-2 Mapping from Raw Data to Assessment Indexes and from Indexes to

Criteria

Collected RawData

AssessmentIndexes

AssessmentCriteria

Wi-Fi DL/UL throughput(bps)

3G/LTE DL/UL throughput

(bps)

RRC state transition

Duration of each RRCstate (ms)

Signaling quantity

User experience*

Trafc consumption

Power consumption

analysis model

Multi-radio capability

Session consumption

MOS measurement

Signaling consumption

Network impact

TCP/UDP connections

Device powerconsumption*

User experience

TLS/SSL/ Encryption

Privacy and security

Privacy and security


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Assess the network friendliness of each application and arrange the order of

applications by network friendliness. In this step, calculate the overall network

friendliness score of each application based on the score by each assessment index

and weight of each assessment index, and arrange the order of applications by

network friendliness in each type.

Set the weight of each assessment index according to the application type.

Step 4 Provide optimization suggestions. In this step, analyze the key network

friendliness indexes, and provide suggestions on application development and network

optimization to improve the network friendliness of applications.

NOTE: More black area means higher weight

IM App1 Scores

indexes baseline score

Video App2 Scores


SNS App3 Scores


Step 3 Score and Ranking

Compare with baseline to score the network friendliness of each application by

each index. Each index is evaluated by its ranking sorted by the network impact

and user experience, and the score of each index is calculated by its ranking result.

The higher the network impact, the smaller the score. We set the score range from

0 to 5, with a maximum score being 5.

App

Category

Network Impact User ExperiencePrivacy &Security

Trafc Session Signaling Multi-RadioUser

ExperiencePower

ConsumptionPrivacy &Security

IM

VoIP

Web

Video

SNS

*


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4 Apps Insider

To accurately analyze the network friendliness of smartphones and applications,

Huaweis mLAB provides a wireless network environment that integrates GSM, UMTS,

LTE and Wi-Fi networks with the Apps Insider, an automatic tool for analyzing the

network friendliness of applications.

4.1 Description

The Apps Insider adopts the client/server model. The Apps Insider clients collect the

measurement results and report them to the server. The Apps Insider server calculates

the average network friendliness score of each application based on the score of each

assessment index and its weight, and ranks the applications by network friendliness.

By analyzing the key network friendliness indexes, the Apps Insider providessuggestions on application development and network optimization to improve the

network friendliness of applications.

Figure 4-1 Framework of the Apps Insider

User experience

assessment report

Network impact

assessment report

Networkoptimization

suggestions

Application

development

suggestions

Apps Insider

server

Analyze the

networkfriendliness of

applications

Selectassessment

objectsRNC

NodeB

CN

mLAB Apps Insider toolkit

Apps Insider

client

IM Video

SNS

VoIP

Gi

interface

IP packetanalysis

Airinterface

signalinganalysis

Power

consumptionanalysis


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Apps Insider client

The Apps Insider clients include applications on Android, iPhone and Windows

smartphones. Select the application to be assessed, for example: Sina Weibo. Classify

the application, for example: Sina Weibo is a Social Networking Service (SNS). Set test

scenarios such as foreground with no user input, normal use, background, start time

and end time. Select assessment objects, such as the E2E delay, RTT, DNS response

time, DL/UL trafc and power consumption. Report measurement results to the server.

Display nal assessment results.

Apps Insider assistant tools

The Apps Insiders assistant tools include the air interface signaling analyzer, Gi

interface IP packet analyzer, Agilent power consumption analyzer, and MOS analyzer.

Apps Insider server

The Apps Insider server collects information from the device, power consumption

analyzer, and Gi interface IP packet analyzer, and saves the information into a

database. Define baselines for assessing the network friendliness of applications

in terms of user experience, device power consumption, signaling consumption,

traffic consumption, connection consumption, multi-radio capability, and privacy

and security, and provides suggestions on application development and network

optimization.


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Rank applications by network friendliness.

Figure 4-2 Ranking of SNS applications by friendliness

4.2 Apps Insider Case Study

This case analyzes the network friendliness of three popular SNSs (AppA, AppB

andAppC) on the iPhone and Android.

NOTE: User experience indexes vary by type of application. VoIP user experience is

assessed by the MOS analyzer.

Log in to AppA, AppB andAppC, and perform no operations within 20 minutes in

the foreground.

Phone ModelOperating

System

Mobile

Application

Application

Version

Application

TypeTest Scenario

Test Duration

(Minutes)

iPhone4 iOS 5.0 AppA a SNS Foreground 20

iPhone4 iOS 5.0 AppB b SNS Foreground 20

iPhone4 iOS 5.0 AppC c SNS Foreground 20

Huawei Honor Android 2.3.6 AppA d SNS Foreground 20

Huawei Honor Android 2.3.6 AppB e SNS Foreground 20

Huawei Honor Android 2.3.6 AppC f SNS Foreground 20

Assessment Dimension Value RangeWeight (a default value congurable according to

application type)

Connection quantity (0, 5] 0.1

Trafc consumption (0, 5] 0.2

Signaling consumption (0, 5] 0.2

User experience* (0, 5] 0.1

Terminal power consumption (0, 5] 0.2

Multi-radio capability (0, 5] 0.1

Privacy and security (0, 5] 0.1

Dene the weight of each assessment index.


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As shown in Figure 4-2, AppCs iPhone app ranks last. According to the analysis

results of key friendliness network indexes, the device power consumption and

signaling consumption have great impacts on the ranking of applications. AppCs

iPhone app queries the server every 30 seconds (heartbeat) to check whether any

updates have been made. Each heartbeat causes the PCH2FACH state transition. The

data transmission on the FACH lasts for 3 seconds. After the data is transmitted, the

FACH2PCH state transition is triggered when the deactivated timer T2 expires.

The optimization suggestions on AppCs iPhone app are as follows:

The 30-second interval is too short. Increase the heartbeat interval.

Use the push mechanism instead of the poll mechanism. Then the server pushes

the information to the client through the long TCP connection when any update is

made on the server.

The optimization suggestions on wireless networks are as follows:

Adopt the smart state transition scheme and optimize the network conguration

parameters.

Identify heartbeats in self-learning mode.

Dynamically set the deactivated RRC state transition timers (T1, T2 and T3)

according to heartbeats in self adaptation mode.

Analyze key network friendliness indexes.

Provide optimization suggestions.

Figure 4-3 Analysis results of the IP traffic, state transition and power

consumption of AppCs iPhone app


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Figure 5-1 Smartphone Ecosystem Collaboration

5 Healthy Development of the

Application Network FriendlinessOptimization System

With the fast development of smartphones and mobile applications, the network

unfriendliness of smartphones and mobile applications are wasting large quantities

of precious mobile network signaling resources and wireless resources, affecting

user experience and posing a threat to user privacy and network security. To guide

the development of network-friendly applications, the GSM Association released

Guidelines for Development of Network Friendly Applications, and AT&T hasdeveloped the ARO and provided optimal practices. To build an application network

friendliness optimization system, Huawei launched the mLAB, a mobile broadband

(MBB) innovation laboratory. The mLAB provides a wireless network environment that

integrates GSM, UMTS, LTE and Wi-Fi networks and the Apps Insider, an automatic

tool for analyzing the network friendliness of applications.

How to utilize network friendliness evaluation platform in Huaweis mLAB? Three

options including open environment, evaluation service and joint research are

available.

Open Environment

Operators

NetworkVendors

Device VendorsPlatformProviders

CollaborationApp Developers

Evaluation Service Joint Research

Access mLAB network friendliness evaluation

platform through Internet

Install application client on top of mLAB

simulated device OS (Software Device)

Offered Services to

Analysis specic application network

friendliness

Provide valuable network optimization

and application design suggestions

Setting up joint research project between

partners and mLAB

Agreed objective and project time plan/

delivery


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Figure 5-2 Features of Huaweis mLAB Network Friendliness Evaluation Platform

Industry partners including developers, device vendors, platform providers and

operators are welcome to use mLAB resources to analyze the network-friendliness of

applications, enhance the network friendliness of applications in terms of application

development and network optimization, and improve user experience. The mLAB is

also willing to establish an open application network friendliness assessment center

and help developers publish applications for platforms like the Apple App Store

and Google Play. The mLAB will regularly release the network friendliness ranking

of applications and provide suggestions for optimizing the network friendliness of

applications. Users can also refer to this ranking when selecting applications.

Huawei is committed to building a harmonious MBB ecosystem with friends in the

industry and boosting the prosperity and development of the MBB industry, said

Wang Tao, President of Huaweis Wireless Network Product Line.

Assistant Tools

Real WirelessNetwork

Environment

Full Choices ofDevices/OS

NetworkOptimization&Application

DesignSuggestions

Air interface signaling analyzer

Gi interface IP packet analyzer Agilent power consumption analyzer

With different access technology( Wi-Fi/

UMTS/LTE/GSM)

Flexible network features conguration (e.g.

3GPP R8 FD)

Abundant device types, OS versions

Based on mLAB research in popular

applications user behavior and network

impact


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6 References

1. Smartphone Challenge: Guidelines for Development of Network Friendly

Applications, Version 0.11. GSM Association Ofcial Document TS.20, November

2011

2. http://developer.att.com/ARO

3. F. Qian, Z. Wang, A. Gerber, Z. M. Mao, S. Sen, and O. Spatscheck. Profiling

Resource Usage for Mobile Applications: a Cross-layer Approach. In MobiSys,

2011


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A Acronyms

D

DCH Dedicated Channel

DL Downlink

F

FACH Forward Access Channel

G

GPS Global positioning system

GSM Global system for mobile communications

H

HSPA High Speed Packet Access

HTTP Hyper Text Transfer Protocol

I

IM Instant Messaging

IP Internet Protocol

L

LTE Long Time Evolution

M

MOS Mean opinion score

P

PCH Paging Channel

Q

QoE Quality of experience

RRNC Radio Network Controller

S

SNS Social Networking Services

T

TCP Transmission Control Protocol

U

UE User Equipment

UL Uplink

URA UTRAN Registration Area

V

VOIP Voice Over IP

W

Wi-Fi Wireless Fidelity


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Copyright Huawei Technologies Co., Ltd. 2012. All rights reserved.

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that could cause actual results and developments to differ materially from those

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Trademark Notice

, HUAWEI, and are trademarks or registered trademarks of Huawei Technologies Co.,

Other trademarks, product, service and company names mentioned are the property of their respective owners.

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Version No.: M3-001034414-20120731-C-2.0

20120731 analyzing the network friendliness of mobile applications

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