Master Thesis MSTR-2017-36

BibliographyGraser, Leon: Design and implementation of an evaluation testbed for fog computing infrastructure and applications.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 36 (2017).
87 pages, english.

Besides the popular Cloud Computing paradigm, a new approach to distributed computation, known as Fog Computing, has been emerging in the last few years. This approach suggests, that the intelligence should move from the data centers to the network level. In the past years, Fog Computing has been gaining more attention, which has led to the rise in projects and publications. Unfortunately, there is very little support to test and evaluate Fog Computing applications. Aside from expensive real world deployments, there are few tools to simulate the behavior. Since simulation does not execute the application to be tested, the results are less accurate than in an emulated environment. Emulation offers a trade-off between evaluation costs and accurate results. This work proposes a new approach to read in network topologies from different sources and uses them to evaluate user defined Fog Computing applications. To identify the edge of those networks an algorithm is presented. Also, a heuristic to place fog nodes cost optimal within a user defined proximity of the edge is suggested. The final outcome can be exported to a network emulator like MaxiNet in combination with Docker. This approach is implemented in EmuFog and published open source. It is easily extensible for future use, platform independent, and flexible for different applications to test. A user can specify the computing capabilities (i.e.RAM) of each node type and define the associated Docker image to run. Hierarchies can be built using dependencies between fog node types. Also, an evaluation is carried out to measure the algorithms presented. For the edge identification and the fog node placement, the evaluation shows reasonable running times even for bigger network sizes of up to 10,000 nodes. In the evaluated networks the heuristic shows an average deviation of 1.2, and in the worst-case scenario, a deviation of 5/3 of the cost optimal result.

Full text and
other links
Department(s)University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems
Superviser(s)Rothermel, Prof. Kurt; Mayer, Ruben
Entry dateMay 28, 2019
   Publ. Computer Science