An Experiment-Driven Performance Model of Stream Processing Operators in Fog Computing Environments

Data stream processing (DSP) is an interesting computation paradigm in geo-distributed infrastructures such as Fog computing because it allows one to decentralize the processing operations and move them close to the sources of data. However, any decomposition of DSP operators onto a geo-distributed environment with large and heterogeneous network latencies among its nodes can have significant impact on DSP performance. In this paper, we present a mathematical performance model for geo-distributed stream processing applications derived and validated by extensive experimental measurements. Using this model, we systematically investigate how different topological changes affect the performance of DSP applications running in a geo-distributed environment. In our experiments, the performance predictions derived from this model are correct within ±2% even in complex scenarios with heterogeneous network delays between every pair of nodes.