Budapest, 26-28 October 2016

BIG DATA INTERPRETATION AND CHALLENGES IN MOBILE NETWORK TESTING Benjamin Teke and Zoltán Elzer© All rights reserved

Testing challengesMOBILE TELEPHONY NETWORK

2

• Network elements

• Separate software instances

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• Network

• Connection of network elements

• Communication according to

standards

I WANT TO

MAKE A

CALL

YOU HAVE

A CALL? „What is in the network?”

Testing challengesCOMPLEXITY GROWTH

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2G

3G

User Data Management domain2G / 3G network domain

MSC

RNCNodeB

BSCBTS

2G/3GUser

SGSN

GGSN

Internet

4G network domain

eNodeB

4GUser

MME PCRF

P-CSCF

I/S-CSCF

E-CSCF/EATF

MMTel AS

SCC AS

MGWMRFP

MGCF

BGCF

MRFC

TrGW

IBCF

BGF

Presence Server

AAA

ePDGInternet

Internet

4G (LTE)

Voice over 4G (VoLTE)

Voice over WiFi

SGW PGW

HLR/VLR

HSS/SLF

More complex

network

architecture

= MORE TESTING

IP Multimedia

Subsystem

(IMS) domain

Testing challengesTEST LEVELS IN MOBILE NETWORK DEVELOPMENT

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Solution

test

Domain test

Product test

Software module test

• Focus on end-to-end

network

• Communication between

network functions

• Check interworking and

end-to-end behaviors

Testing challengesSOLUTION TEST

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Mobile

networkSimulators

Traffic capturing

and data collectionUser equipment

Test & operation

framework

Signaling

messages

Load generators

Beyond testingAFTER TEST EXECUTION

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Test

executionMonitoring

the network

Troubleshoot /

Repair

Collecting dataReal time/offline

analysis

• Automation of steps is required!

Automated analysis, troubleshoot

and repair based on collected data

Focus

Beyond testingDATA COLLECTION

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Network element reported

session records

• MB to GB / day

Network element reported

performance data

• MB to GB / day

Network element internal logs

• MB to GB / day

Traffic capturing

• Multiple TBs /day

SUM data size: „Small” VoLTE network = ~1-2 TByte/day

Example: 300+ application level messages per VoLTE call

Beyond testingDATA COLLECTION – TRAFFIC CAPTURING

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• Monitoring

• Various points of the network

• Collect signaling messages

• The gathered data

• Linear list of messages

• Not certainly in the expected

order

User Data Management domain2G / 3G network domain

MSC

RNCNodeB

BSCBTS

2G/3GUser

HLR/VLR

SGSN

GGSN

Internet

4G network domain

eNodeB

4GUser

MME PCRF

SGW PGW

HSS/SLF

IP Multimedia Subsystem (IMS) domain

P-CSCF

I/S-CSCF

E-CSCF/EAT

F

MMTel ASSCC AS

MGWMRFP

MGCF

BGCF

MRFC

TrGW

IBCF

BGF

Presence Server

AAA

ePDGInternet

Internet

„How do we get the data?”

Beyond testingCORRELATION – THE TRADITIONAL METHOD

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• FIND is the keyword

• Incoming data

• No structure

• Flows can overlap

• Troubleshooting

• Time consuming

• Prone to errors

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Beyond testingCORRELATION – STRUCTURING THE DATA

• Correlated data

• Assembled flows

• With grouping and

filtering options

• Placed in the network

Beyond testingCORRELATION – SIMPLIFIED WORKFLOW

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Intra-protocol

correlation

Inter-protocol

correlation

Forming the

input

Connect flows

to end-user

User Data Management domain2G / 3G network domain

MSC

RNCNode

B

BSCBTS

2G/3G

User

HLR/VLR

SGSN

GGSN

Internet

4G network domain

eNodeB

4GUser

MMEPCR

F

SGW PGW

HSS/SLF

IP Multimedia Subsystem (IMS) domain

P-CSC

F

I/S-CSC

F

E-CSCF/EATF

MMTel AS

SCC AS

MGW

MRFP

MGCF

BGCF

MRFC

TrGW

IBCF

BGF

Presence

Server

AAA

ePDG

Internet

Internet

Beyond testingCORRELATION – BIG DATA PROCESSING

• The tasks can be a separate services

• Services can run parallel or pipelined parallel

• Multiple instances can be started

• Real-time processing is achievable

• Micro-batch processing

• Same algorithm for offline and online processing

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”Information must be correlated”

Intra-protocol

correlation

Inter-protocol

correlation

Forming the

input

Connect flows

to end-user

Beyond testingBIG DATA FRAMEWORKS – WHAT DO WE USE?

• Own platform

• Helps to develop, test, maintain applications using Big Data

technologies

• Gathers open source frameworks

• Used frameworks

• Processing: Hadoop MapReduce, Apache Storm, Apache Spark

• Messaging: ZeroMQ, Apache Kafka

• Misc.: Hadoop HDFS, Apache HBase, Apache Phoenix

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Example

Takeaway

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Focus on end-to-end

system behavior and drill

down to the details

Automation of data

collection, analysis and

troubleshooting

Common solution for

offline and online analysis

Multi-stage correlation to

parallelly process diverse

data

QUESTIONS & ANSWERThank you for your attention.

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