ABSTRACT 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. INDEX TERMS 5G mobile communication, business model, communication system operations and management, genetic algorithms, network slicing, optimal scheduling, optimization, resource management
With the aim to maximize the long-term network utility, reference REF uses a genetic algorithm to serve slice requests.
Slice as an Evolutionary Service : Genetic Optimization for Inter-Slice Resource Management in 5 G Networks