Literature Database Entry


Julius Ostermann, "Analyzing and predicting Topic Behaviour on public MQTT Servers," Bachelor Thesis, School of Electrical Engineering and Computer Science (EECS), TU Berlin (TUB), January 2021. (Advisor: Daniel Happ; Referees: Falko Dressler and Thomas Sikora)


Publish/subscribe systems such as MQTT have an increasing demand and require- ment to provide these services with sufficient capacity reliability and stability, which is a consequence of the growing demand created by the entry of IoT into everyone’s daily life. In order to provide this service, it is helpful to know how many messages will be published in the coming period to scale resources and distribute the load across multiple nodes. Therefore is why heuristics, such as averages, are often used. In this thesis it is discussed which machine learning algorithms are suitable for this task. We identify ARIMA, recurrent neural networks and artificial neural networks as promising candidates for the prediction. The performance of these is finally compared using the mean absolute error with the moving average and the total average heuristic, showing that after removing outliers from the data, the heuristics perform almost as well as the implemented algorithms. This does not apply to real conditions where outliers are not removed. In this case the recurrent neural networks outperform the heuristics by up to 60%. The thesis shows that by using more advanced technologies for load prediction, demand can be better predicted, which is a valuable information for increasing the quality and reliability of public MQTT servers while optimizing costs at the same time.

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Julius Ostermann

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    author = {Ostermann, Julius},
    title = {{Analyzing and predicting Topic Behaviour on public MQTT Servers}},
    advisor = {Happ, Daniel},
    institution = {School of Electrical Engineering and Computer Science (EECS)},
    location = {Berlin, Germany},
    month = {1},
    referee = {Dressler, Falko and Sikora, Thomas},
    school = {TU Berlin (TUB)},
    type = {Bachelor Thesis},
    year = {2021},

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