  Network slicing allows mobile network operators to virtualize infrastructures
and provide customized slices for supporting various use cases with
heterogeneous requirements. Online deep reinforcement learning (DRL) has shown
promising potential in solving network problems and eliminating the
simulation-to-reality discrepancy. Optimizing cross-domain resources with
online DRL is, however, challenging, as the random exploration of DRL violates
the service level agreement (SLA) of slices and resource constraints of
infrastructures. In this paper, we propose OnSlicing, an online end-to-end
network slicing system, to achieve minimal resource usage while satisfying
slices' SLA. OnSlicing allows individualized learning for each slice and
maintains its SLA by using a novel constraint-aware policy update method and
proactive baseline switching mechanism. OnSlicing complies with resource
constraints of infrastructures by using a unique design of action modification
in slices and parameter coordination in infrastructures. OnSlicing further
mitigates the poor performance of online learning during the early learning
stage by offline imitating a rule-based solution. Besides, we design four new
domain managers to enable dynamic resource configuration in radio access,
transport, core, and edge networks, respectively, at a timescale of subseconds.
We implement OnSlicing on an end-to-end slicing testbed designed based on
OpenAirInterface with both 4G LTE and 5G NR, OpenDayLight SDN platform, and
OpenAir-CN core network. The experimental results show that OnSlicing achieves
61.3% usage reduction as compared to the rule-based solution and maintains
nearly zero violation (0.06%) throughout the online learning phase. As online
learning is converged, OnSlicing reduces 12.5% usage without any violations as
compared to the state-of-the-art online DRL solution.
