Bachelorarbeit BCLR-2017-88

Seifermann, Valentin: Application performance monitoring in microservice-based systems.
Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik, Bachelorarbeit Nr. 88 (2017).
85 Seiten, englisch.
CR-Klassif.C.4 (Performance of Systems)
H.3.4 (Information Storage and Retrieval Systems and Software)

Nowadays, cloud computing including its functionality and service delivery model, turned into a common paradigm to provide on-demand IT resources and scalability. This also leads to a change of architecture from monolithic systems to a microservice-based architecture with high scalability and elasticity as well as independent developments and deployments. Due to this change, IT environments are getting more complex and highly distributed. Thus, the requirements of Application Performance Management (APM) for these types of systems are also changing. While monitoring monolithic applications and infrastructures focuses on a few single components with their callstacks and the surveillance of the general state of health, the monitoring of microservice-based systems requires other approaches. This work will elaborate the actual state-of-the-art of APM in microservice-based systems on the basis of an industrial case study. Furthermore, the challenges of monitoring microservice-based systems will be elaborated. As part of the industrial case study, an existing microservice monitoring tool will be evaluated on different environments and integrated into the state-of-the-art business-oriented monitoring strategy "System Management: Inform-Locate-Escalate" (SMILE), which consists of various monitoring and service management tools. The evaluation will be done by conducting use cases, defined in the context of the thesis to meet the challenges. In addition, the work proposes an experimental concept for APM in microservice-based systems. This concept consists of a selected monitoring stack with different open-source tools and an existing microservice monitoring solution. Furthermore, it contains different dashboards with decisive metrics and other monitoring-specific data.

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Abteilung(en)Universität Stuttgart, Institut für Softwaretechnologie, Sichere und Zuverlässige Softwaresysteme
Betreuervan Hoorn, Dr. André; Högl, Georg
Eingabedatum3. Dezember 2018
   Publ. Informatik