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Automatic Log Analysis System Integration: Message Bus Integration in a Machine Learning Environment
KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Radio Systems Laboratory (RS Lab). (CCS)
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Ericsson is one of the world's largest providers of communications technology and services. Reliable networks are important to deliver services that live up to customers' expectations. Tests are frequently run on Ericsson's systems in order to identify stability problems in their networks. These tests are not always completely reliable. The logs produced by these tests are gathered and analyzed to identify abnormal system behavior, especially abnormal behavior that the tests might not have caught. To automate this analysis process, a machine learning system, called the Awesome Automatic Log Analysis Application (AALAA), is used at Ericsson's Continuous Integration Infrastructure (CII)-department to identify problems within the large logs produced by automated Radio Base Station test loops and processes. AALAA is currently operable in two versions using different distributed cluster computing platforms: Apache Spark and Apache Hadoop. However, it needs improvements in its machine-to-machine communication to make this process more convenient to use. In this thesis, message communication has successfully been implemented in the AALAA system. The result is a message bus deployed in RabbitMQ that is able to successfully initiate model training and abnormal log identification through requests, and to handle a continuous flow of result updates from AALAA.

Abstract [sv]

Ericsson är en av världens största leverantörer av kommunikationsteknologi och tjänster. Tillförlitliga nätverk är viktigt att tillhandahålla för att kunna leverera tjänster som lever upp till kundernas förväntningar. Tester körs därför ofta i Ericssons system med syfte att identifiera stabilitetsproblem som kan uppstå i nätverken. Dessa tester är inte alltid helt tillförlitliga, producerade testloggar samlas därför in och analyseras för att kunna identifiera onormalt beteende som testerna inte lyckats hitta. För att automatisera denna analysprocess har ett maskininlärningssystem utvecklats, Awesome Automatic Log Analysis Application (AALAA). Detta system används i Ericssons Continuous Integration Infrastructure (CII)-avdelning för att identifiera problem i stora loggar som producerats av automatiserade Radio Base Station tester. AALAA är för närvarande funktionellt i två olika versioner av distribuerad klusterberäkning, Apache Spark och Apache Hadoop, men behöver förbättringar i sin maskin-till-maskin-kommunikation för att göra dem enklare och effektivare att använda. I denna avhandling har meddelandekommunikation implementerats som kan kommunicera med flera olika moduler i AALAA. Resultatet är en meddelandebuss implementerad i RabbitMQ som kan initiera träning av modeller och identifiering av onormala loggar på begäran, samt hantera ett kontinuerligt flöde av resultatuppdateringar från pågående beräkningar.

Place, publisher, year, edition, pages
2015. , xiii,28 p.
Series
TRITA-ICT-EX, 2015:55
Keyword [en]
Big Data, Machine learning, Message passing, Machine-to-machine communication
Keyword [sv]
Big Data, Maskininlärning, Meddelandesändning, Maskin-till-maskin kommunikation
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-168837OAI: oai:DiVA.org:kth-168837DiVA: diva2:818538
Presentation
2015-06-01, Seminar room Grimeton, Isafjordsgatan 22, Kista, 14:00 (English)
Supervisors
Examiners
Available from: 2015-06-09 Created: 2015-06-09 Last updated: 2015-06-09Bibliographically approved

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CiteExportLink to record
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