  In the context of Fifth Generation (5G) mobile networks, the concept of
"Slice as a Service" (SlaaS) promotes mobile network operators to flexibly
share infrastructures with mobile service providers and stakeholders. However,
it also challenges with an emerging demand for efficient online algorithms to
optimize the request-and-decision-based inter-slice resource management
strategy. Based on genetic algorithms, this paper presents a novel online
optimizer that efficiently approaches towards the ideal slicing strategy with
maximized long-term network utility. The proposed method encodes slicing
strategies into binary sequences to cope with the request-and-decision
mechanism. It requires no a priori knowledge about the traffic/utility models,
and therefore supports heterogeneous slices, while providing solid
effectiveness, good robustness against non-stationary service scenarios, and
high scalability.
