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Author:Mäkinen, Elina Suvi
Title:Improving performance and scalability of a network data analyzer
Publication type:Master's thesis
Publication year:2010
Pages:[8] + 62      Language:   eng
Department/School:Tietotekniikan laitos
Main subject:Tietokoneverkot   (T-110)
Supervisor:Aura, Tuomas
Instructor:Patinen, Juha-Pekka
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
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In the closed network of Learning Centre you can read digital and digitized theses not available in the open network.

The Learning Centre contact details and opening hours: https://learningcentre.aalto.fi/en/harald-herlin-learning-centre/

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Location:P1 Ark Aalto  1225   | Archive
Keywords:mobile internet
deep packet inspection
clustering
performance testing
scalability
mobiili internet
klusterointi
suorituskykytestaus
skaalautuvuus
Abstract (eng): Mobile Internet access has become increasingly popular in the past few years.
This is partly due to the rapid development of mobile devices, but also to, the growing popularity of fiat-rate pricing plans offered by operators.
However, fiat-rate pricing prevents the operators from gaining full profit out of the mobile Internet boom.

Traffic Analyzer (TA) is a product that offers the operators a possibility to implement differentiated charging based on the accessed services and used protocols.
It analyzes the traffic passing through it and performs charging based on predefined rules.
The operators who have deployed TA have indicated that the amounts of data traffic in their networks are growing and that more TA capacity is needed.
The aim of this thesis is to study how clustering can improve the performance of an existing TA product.

This thesis reports experiments in which the performance of TA is measured with different cluster configurations.
The cluster size varies from one to six nodes, and the tests are performed with both HTTP and WAP protocols.
The performance is measured in transactions per second.
Throughput in kilobytes per second is estimated based on the measured values.
Additionally, CPU usage is monitored during the tests.

Based on the experiments, the recommended maximum size of a TA cluster in the current architecture is four nodes.
Adding more nodes to a cluster may even decrease the performance.
If more capacity is needed, alternative solutions, such as separate geographically distributed TA clusters should be considered.
Abstract (fin): Internetin käyttö matkapuhelimilla on kasvattanut suuresti suosiotaan viime aikoina.
Tähän on syynä paitsi mobiililaitteiden huima kehitys, myös operaattorien yleistynyt tapa laskuttaa datasiirrosta kiinteä hinta riippumatta siirretyn datan määrästä.
Kiinteä laskutus kuitenkin estää operaattoreita saamasta täyttä hyötyä mobiilin Internet-yhteyden suuresta suosiosta.

Traffic Analyzer (TA) on laite, joka tarjoaa operaattoreille mahdollisuuden differentioituun laskutukseen haettujen palveluiden ja käytetyn protokollan perusteella.
Se analysoi lävitsensä kulkevaa dataliikennettä ja laskuttaa käyttäjiä etukäteen määriteltyjen sääntöjen mukaan.
Operaattorit, joilla Traffic Analyzer on käytössä, ovat ilmaisseet liikennemäärien olevan kasvussa ja tarpeen suurempaan kapasiteettiin.
Diplomityön tarkoitus on tutkia, kuinka klusteroinnilla voidaan parantaa kyseisen TA-tuotteen suorituskykyä.

Diplomityössä testataan TA:n suorituskykyä erilaisilla klusterikonfiguraatioilla.
Klusterin koko vaihtelee yhdestä kuuteen, ja testit suoritetaan sekä HTTP- että WAP-protokollalla.
Testeissä mitataan kuinka monta transaktiota sekunnissa TA kykenee käsittelemään.
Mitattujen arvojen perustella arvioidaan myös klusterin välityskyky kilobitteinä sekunnissa.
Lisäksi testien aikana tarkkaillaan CPU:n käyttöastetta.

Testitulosten perusteella klusterin suositeltava maksimikoko nykyisellä arkkitehtuurilla on neljä noodia.
Jos klusteria kasvatetaan tätä isommaksi, suorituskyky saattaa jopa laskea.
Jos tarvetta on vieläkin suuremmalle kapasiteetille, tulisi harkita vaihtoehtoisia ratkaisuja, kuten erillisten, maantieteellisesti hajautettujen TA-klusterien käyttöä.
ED:2010-09-15
INSSI record number: 40803
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