Online Feature Selection for Low-overhead Learning in Networked Systems
Xiaoxuan Wang, Forough Shahab Samani, Andreas Johnsson, Rolf Stadler · 2021
Data-driven functions for operation and management require measurements and readings from distributed data sources for model training and prediction. While the number of candidate data sources can be very large, research has shown that it is often possible to reduce the number of data sources significantly while still allowing for accurate prediction. Consequently, there is potential to lower communication and computing resources needed to continuously extract, collect, and process this data. We demonstrate the operation of a novel online algorithm called OSFS, which sequentially processes the collected data and reduces the number of data sources for training prediction models. OSFS builds on two main ideas, namely (1) ranking the available data sources using (unsupervised) feature selection algorithms and (2) identifying stable feature sets that include only the top features. The demonstration shows the search space exploration, the iterative selection of feature sets, and the evaluation of the stability of these sets. The demonstration uses measurements collected from a KTH testbed, and the predictions relate to end-to-end KPIs for network services.