Master Thesis MSTR-2018-20

BibliographyChaudhry, Muhammad Bilal: Enhancing data flow models with computing requirements for IoT environments.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 20 (2018).
57 pages, english.

In this thesis, an approach is developed to make data flow tools like FlexMash and YesWorkflow more fitting for the Internet of Things. In the current approach of these tools, all the data is sent to a remote runtime, oftentimes located in the cloud, for execution of the data flow models. Since the amount of data produced in an Internet of Things environment can be very large, this approach results in high network traffic and high latency, which is not suitable for the Internet of Things. In order to solve this problem, the approach developed in this thesis enhances the data flow models generated from the data flow tools by annotating nodes and edges of a data flow model with computing requirements. This allows the modeled data flow to be executed in a distributed manner i.e., near the data sources. Furthermore, this thesis also presents an approach for recommending the required computing resources for an operation before the modeled data flow is actually executed. This thesis serves as a proof of concept for executing data flow models in a distributed manner in an Internet of Things environment.

Department(s)University of Stuttgart, Institute of Parallel and Distributed Systems, Applications of Parallel and Distributed Systems
Superviser(s)Schwarz, PD Dr. Holger; Franco da Silva, Ana Cristina; Reimann, Dr. Peter
Entry dateMay 27, 2019
   Publ. Computer Science