Predicting Round-Trip Time Distributions in IoT Systems using Histogram Estimators

In this paper we describe and evaluate an approach for predicting conditional RTT probability distributions in an IoT system. From the distributions we derive conditional mean and quantiles, for example, which are essential for performance management and service assurance. The distribution is represented by a histogram, which requires a discretized target space, trained using supervised learning of a random forest classifier.We evaluate the approach using data traces obtained from experimentation in a realistic IoT testbed. The results show high model performance in prediction of quantiles and aggregated distributions, and the trends for conditional mean are captured.For the operator, the approach enables low-overhead and tractable IoT performance assessment, especially compared to traditional approaches using for example active measurements.