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
In REF , the authors take the SlaaS approach and develop an online genetic-based optimizer that approaches toward the ideal slicing strategy with the maximized long-term network utility.
Slice as an Evolutionary Service : Genetic Optimization for Inter-Slice Resource Management in 5 G Networks