Artikel in Tagungsband INPROC-2020-21

Zimmermann, Michael; Breitenbücher, Uwe; Képes, Kálmán; Leymann, Frank; Weder, Benjamin: Data Flow Dependent Component Placement of Data Processing Cloud Applications.
In: Proceedings of the 2020 IEEE International Conference on Cloud Engineering (IC2E).
Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik.
S. 83-94, englisch.
IEEE Computer Society, April 2020.
ISBN: 978-1-7281-1099-8; DOI: 10.1109/IC2E48712.2020.00016.
Artikel in Tagungsband (Konferenz-Beitrag).
CR-Klassif.C.0 (Computer Systems Organization, General)
D.2 (Software Engineering)

With the ongoing advances in the area of cloud computing, Internet of Things, Industry 4.0, and the increasing prevalence of cyber-physical systems and devices equipped with sensors, the amount of data generated every second is rising steadily. Thereby, the gathering of data and the creation of added value from this data is getting easier and easier. However, the increasing volume of data stored in the cloud leads to new challenges. Analytics software and scalable platforms are required to evaluate the data distributed all over the internet. But with distributed applications and large data sets to be handled, the network becomes a bottleneck. Therefore, in this work, we present an approach to automatically improve the deployment of such applications regarding the placement of data processing components dependent on the data flow of the application. To show the practical feasibility of our approach, we implemented a prototype based on the open-source ecosystem OpenTOSCA. Moreover, we evaluated our prototype using various scenarios.

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Abteilung(en)Universität Stuttgart, Institut für Architektur von Anwendungssystemen
Eingabedatum18. Mai 2020
   Publ. Informatik