Slice as an Evolutionary Service: Genetic Optimization for Inter-Slice Resource Management in 5G Networks
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In the context of Fifth Generation (5G) mobile networks, the concept of "slice as a service" (SaaS) 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 requires no a priori knowledge about the traffic/utility models, while providing solid effectiveness, good robustness against non-stationary service scenarios, and high scalability.
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