  To facilitate the emerging applications in 5G networks, mobile network
operators will provide many network functions in terms of control and
prediction. Recently, they have recognized the power of machine learning (ML)
and started to explore its potential to facilitate those network functions.
Nevertheless, the current ML models for network functions are often derived in
an offline manner, which is inefficient due to the excessive overhead for
transmitting a huge volume of dataset to remote ML training clouds and failing
to provide the incremental learning capability for the continuous model
updating. As an alternative solution, we propose Cocktail, an incremental
learning framework within a reference 5G network architecture. To achieve cost
efficiency while increasing trained model accuracy, an efficient online data
scheduling policy is essential. To this end, we formulate an online data
scheduling problem to optimize the framework cost while alleviating the data
skew issue caused by the capacity heterogeneity of training workers from the
long-term perspective. We exploit the stochastic gradient descent to devise an
online asymptotically optimal algorithm, including two optimal policies based
on novel graph constructions for skew-aware data collection and data training.
Small-scale testbed and large-scale simulations validate the superior
performance of our proposed framework.
