Master Thesis MSTR-2021-53

BibliographyHagemann, Pascal: Evaluating dynamic load balancing of ECM workload pattern employed in cloud environments managed by a Kubernetes/Docker eco-system.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 53 (2021).
58 pages, english.

The transition to cloud-based ECM solutions requires adaptation and enhancements for dynamic cloud environment. Cloud computing and containerization offer key concepts to create solutions for this task. They also open new opportunities to benefit from cloud usage by leveraging the pay-as-you-go model. Cloud users and providers both benefit from the increased efficiency of dynamic applications. But legacy applications are not yet able to leverage the benefits provided by cloud orchestration. This thesis, therefore researches the feasibility of a dynamic load balancing approach applied to an ECM application deployed into a cloud environment. To evaluate the approach, a prototype using open-source software is created on a Kubernetes orchestrated cluster. Previous work included the port of the containerized ECM application into the Kubernetes environment. The present prototype builds up on this approach by enhancing the ECM application components with metrics export capabilities. A monitoring system based on Prometheus is introduced to gather these metrics from the ECM application and other system components. Information provided by these metrics are used to add elasticity to application components. The prototype proves that dynamic load balancing of the ECM application in the cloud is feasible. Two major challenges for an efficient deployment of the application were identified, (1) the generation of useful metrics and (2) removing dependencies from individual components. Further research into optimizations of stateful service components is required. This further ensures an efficient usage in cloud based elastic topologies, especially considering stateful database applications.

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Department(s)University of Stuttgart, Institute of Parallel and Distributed Systems, Applications of Parallel and Distributed Systems
Superviser(s)Mitschang, Prof. Bernhard; Mega, Cataldo
Entry dateDecember 22, 2021
   Publ. Computer Science