Stream processing is an attractive paradigm for developing innovative fog computing applications: they may be used to design highly interactive applications, data analytics applications (for example in an Internet-of-Things context), etc. Usually, the sources of input data and the destination of processing results is imposed by the application. However, we can choose the placement of intermediate computing elements. The objective of this thesis is to design algorithms to decide on the placement of stream processing operators in a fog computing architecture in order to achieve specific performance/cost/energy tradeoffs.

Expected results

  • A detailed state of the art on task placement and distributed stream processing techniques.
  • Methods and software for analyzing the data flow patterns between stream processing operators in a distributed context.
  • Modeling framework capable of capturing the relevant measured metrics about the workflow graph between operators, data communication between them, processing times of each operator, and constraints due to the location where input data are produced and output data are consumed.
  • Stream processing operator placement algorithms making use of the aforementioned modeling framework.
  • Integrated software implementing automatic stream flow modeling and operator placement in the Apache Flink framework



Rennes is the capital city of Britanny, in the western part of France. It is easy to reach thanks to the high-speed train line to Paris. Rennes is a lively city and a major center for higher education and research. The job will take place within the INRIA/IRISA research center, which is internationally recognized for its research in the domain of information and communication sciences.

Planned secondments

14 months at Elastisys (Umeå, Sweden); 7 months at Las Naves (Valencia, Spain).


Please write your application in English, as it will be examined by multiple people in different European countries. Please do not apply to more than two positions within the FogGuru project.

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