Skalabilna arhitektura mikrousluga zasnovana na platformi Docker s primjenom na probleme strojnog učenja

master's thesis

• Title (english): Scalable Microservice Architecture Based on Docker Platform Applied to Machine Learning Problems
• Author: Lucia Penić
• Mentor: Alan Jović (mentor)
• Committee member: Alan Jović (predsjednik povjerenstva)
• Committee member: Stjepan Groš (član povjerenstva)
• Committee member: Dejan Škvorc (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2020-07-13, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Mikrouslužna arhitektura je poznati i prihvaćeni način organizacije programske potpore. Korištenjem kontejnerizacije, mikrouslužna arhitektura je poboljšana ponajviše lakom prenosivosti usluge i osiguravanjem izoliranosti okoline izvođenja. U usporedbi s monolitnom arhitekturom, mikrouslužna arhitektura nudi brojne prednosti, ali i nedostatke. Jedan od većih nedostataka je upravo nadzor i orkestracija. Ovaj rad bavi se primjenom navedenih arhitekturnih koncepata na probleme strojnog učenja, konkretno na fazu isporuke modela korisniku. Na primjeru modela za analizu sentimenta izgrađen je sustav s kojim korisnik komunicira putem web usluge i šalje mu zahtjeve za klasifikacijom. Ostvareni sustav zapakiran je u kontejnere koristeći Docker, trenutno najpopularniju kontejnersku tehnologiju, i orkestriran je koristeći platformu Kubernetes.

Abstract (english)

In the world of software development, microservices are a well known and already accepted software organization method. Microservices may be improved using container technologies, which ensure portability and isolation of execution environment. Compared to monolithic architecture, microservice architecture offers numerous advantages, but there are also some disadvantages. Some of the major shortcomings are orchestration and monitoring. This paper deals with the application of the mentioned concepts to machine learning with practical application to the model deployment phase. The system consists of microservices used for sentiment analysis, as a machine learning problem example. The user can communicate with the model via a web service and can send classification requests. The system is then packaged in containers using Docker, currently the most popular container technology, and is orchestrated using the Kubernetes platform.

• Keywords: mikrouslužna arhitektura; Docker; Kubernetes; orkestracija; strojno učenje
• Keywords (english): microservices; Docker; Kubernetes; orchestration; machine learning
• Language: croatian
• URN:NBN: urn:nbn:hr:168:940619
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-13 10:34:52