Tato diplomová práce se zabývá návrhem a implementací rozšíření pro UnifiedPush Server, které serveru umožní přistupovat k nerelační databázi MongoDB a využívá potenciál horiznotální škálovatelnosti neralačních databází. Součástí práce je i návrh výkonnostních testů a porovnání výkonu při behu na jednom a vícero uzlích, návrh migračního scénáře z MySQL na MongoDB, identifikace úzkých míst. Aplikace je implementována v jazyce Java a využívá Java Persistence API pro přístup k databázím. Pro přístup k nerelačním databázím používá implementaci standardu JPA Hibernate OGM.

Non-relational database management systems address some of the limitations relational database management systems have when storing large volumes of unstructured, user generated text-based data in distributed environments. They follow different approaches through the data model they use, their ability to scale data storage over distributed servers and the programming interface they provide. An experimental approach was followed to measure the capabilities these alternative database management systems present in their approach to address the limitations of relational databases in terms of their capability to store unstructured text-based data, data warehousing capabilities, ability to scale data storage across distributed servers and the level of programming abstraction they provide. The results of the research highlighted the limitations of relational database management systems. The different database management systems do address certain limitations, but not all. Document-oriented databases provide the best results and successfully address the need to store large volumes of user generated text-based data in a distributed environment / School of Computing / M. Sc. (Computer Science)

Att designa mjukvaruapplikationer med en viss struktur kan ofta framhäva efterfrågade egenskaper. För att välja rätt arkitektur behövs ofta övervägningar, och ibland till och med kompromisser, göras om applikationens planerade karaktär. Det är ofta bra att i detta stadie ha en klar bild om vilka attribut en applikation önskas ha. Ett av de viktigare attributen på sikt är skalbarhet. Kunskapen om olika arkitekturers skalbarhet spelar en definitiv roll i designfasen, vilket avgör hur en applikation senare skalas. På senare år har mikrotjänst-arkitektur blivit ett populärt sätt att bygga mjukvara på där den höga skalbarheten sägs vara en bidragande faktor. Detta arbete har till syfte att undersöka skalbarheten hos mikrotjänst-arkitektur i förhållande till monolitisk arkitektur och visa hur detta kvalitetsattribut påverkas när en transformering från en monolit till en mikrotjänst-arkitektur görs. Arbetet har valt att utgå ifrån en existerande modul i en E-handelsplattform med öppen källkod. Modulen som transformerades till en mikrotjänst, skalades horisontellt för respektive arkitektur och applikations-version. Vid användandet av lämpliga verktyg, såsom Docker, visar resultatet att horisontell skalbarhet finns i högre grad hos mikrotjänst-arkitekturen och fortsätter därefter vara hög. Skalning av mikrotjänster kan göras med en högre precision av det som önskas förändras. Detta står i kontrast till den monolitiska strukturen, där skalning begränsas av prestandan av den miljö där mjukvaruapplikationen körs. Efter transformationen till en mikrotjänst-arkitektur ökade skalbarheten, då skalningsmetoden gjordes med mer finkornighet och isolering av den utvalda modulen. För att individuellt skala den monolitiska modulen horisontellt behövdes förändringen göras virtuellt med hjälp av bakgrundsprocesser. Denna lösning visar sig vara en indirekt skalning av hela den monolitiska strukturen. Utöver horisontell skalbarhet fokuserar utvärderingen av resultatet på kvalitativa attribut i form av simplicitet, autonomi och modularitet. / In designing software applications, a chosen structure can often accentuate desired properties. To choose the correct architecture, one must often do considerations and sometimes even compromises, about the intended characteristics of the application. In that stage it is often well motivated to have a clear picture about which attributes the application shall possess. Over time, one of the most important attributes is scalability. The knowledge about the scalability of different architectures could play a crucial part in the design phase, determining how an application is scaled in the future. In recent years Microservice Architecture has been a popular way of building software and its high scalability is said to be a contributing factor. This work has the purpose of examine the scalability of microservice architecture relative to the monolithic architecture and how this quality attribute is affected after a transformation is done from a monolith to a microservice system. This work is based on an existing module from an open source E-commerce platform. The module was first transformed into a working microservice, then both architectures was horizontally scaled. Using suitable tools such as Docker, the result of this work shows that horizontal scalability exists in a high degree within the microservice architecture and continues being high there after. Scaling of microservices can be done with higher precision of what are to be changed. This stands in relation to the monolithic approach where scaling is limited to the performance of the environment where the software application is running. The transformation to a microservice architecture resulted in an increase of scalability. The scaling method was more fined-grained and isolated to the selected module. In contrast, individual horizontal scaling of the monolithic module was required to be done virtually with background processes. This concluded in an indirect scaling of the whole structure of the monolith. Besides horizontal scalability, the evaluation is focused on the system quality attributes of simplicity, autonomy and modularity.

The machine learning (ML) industry has taken great strides forward and is today facing new challenges. Many more models are developed, used and served within the industry. Datasets that models are trained on, are constantly changing. This demands that modern machine learning processes can handle large number of models, extreme load and support recurring updates in a scalable manner. To handle these challenges, there is a concept called model serving. Model serving is a relatively new concept where more efforts are required to address both conceptual and technical challenges. Existing ML model serving solutions aim to be scalable for the purpose of serving one model at a time. The industry itself requires that the whole ML process, the number of served models and that recurring updates are scalable. That is why this thesis presents an open-source framework for large-scale ML model serving that aims to meet the requirements of today’s ML industry. The presented framework is proven to handle a large-scale ML model serving environment in a scalable way but with some limitations. Results show that the number of parallel requests the framework can handle can be optimized. This would make the solution more efficient in the sense of resource utilization. One avenue for future improvements could be to integrate the developed framework as an application into the open-source machine learning platform STACKn.