  Data-driven functions for operation and management often require measurements
collected through monitoring for model training and prediction. The number of
data sources can be very large, which requires a significant communication and
computing overhead to continuously extract and collect this data, as well as to
train and update the machine-learning models. We present an online algorithm,
called OSFS, that selects a small feature set from a large number of available
data sources, which allows for rapid, low-overhead, and effective learning and
prediction. OSFS is instantiated with a feature ranking algorithm and applies
the concept of a stable feature set, which we introduce in the paper. We
perform extensive, experimental evaluation of our method on data from an
in-house testbed. We find that OSFS requires several hundreds measurements to
reduce the number of data sources by two orders of magnitude, from which models
are trained with acceptable prediction accuracy. While our method is heuristic
and can be improved in many ways, the results clearly suggests that many
learning tasks do not require a lengthy monitoring phase and expensive offline
training.
