  Machine learning (ML) is a powerful tool to model the complexity of
communication networks. As networks evolve, we cannot only train once and
deploy. Retraining models, known as continual learning, is necessary. Yet, to
date, there is no established methodology to answer the key questions: With
which samples to retrain? When should we retrain?
  We address these questions with the sample selection system Memento, which
maintains a training set with the "most useful" samples to maximize sample
space coverage. Memento particularly benefits rare patterns -- the notoriously
long "tail" in networking -- and allows assessing rationally when retraining
may help, i.e., when the coverage changes.
  We deployed Memento on Puffer, the live-TV streaming project, and achieved a
14% reduction of stall time, 3.5x the improvement of random sample selection.
Finally, Memento does not depend on a specific model architecture; it is likely
to yield benefits in other ML-based networking applications.
