"MAO-MAO aims to monitor the evolution of microservice repositories and exploit the data to analyse emerging development patterns and quality issues. The goal is to promote reproducibility and peer validation of our data, by running all experiments within a distributed system with a federated architecture."
Title: Data Distribution and Exploitation in a Global Microservice Artefact Observatory
Keywords: microservices, software artefacts, cloud computing, cloud native, serverless, artefact quality
Abstract: Microservices and the architectures they enable are becoming a prominent force in the field of cloud-native application development. Due to their highly modular and reusable nature, their distribution among the developer community is also on the rise, with marketplaces centred around microservice artefacts gaining popularity rapidly. With Docker and its public repository, Dockerhub, now in widespread use, and further examples such as Helm, Kubernetes Operators and AWS Serverless applications gaining ground, this thesis aims to investigate these marketplaces and the artefacts they distribute. We collect data on development trends, code and artefact quality metrics, performance and security issues intending to analyse and archive and ultimately, through feedback to developers, contribute to a higher quality standard. Inspired by global observatories in astronomy, we aim to contribute a software platform for collaborative research on the topic, designed to promote reproducibility and peer validation of our data, by running all experiments within a distributed system with a federated architecture.
Planned duration: 1.2.2020 - 31.01.2023
Panagiotis Gkikopoulos (2019), Data Distribution and Exploitation in a Global Microservice Artefact Observatory, SERVICES 2019: 319-322