Slice as an Evolutionary Service: Genetic Optimization for Inter-Slice Resource Management in 5G Networks
It addresses the challenge of efficient online resource management for mobile network operators and service providers in 5G networks, offering a novel method for a known bottleneck.
This paper tackles the problem of optimizing inter-slice resource management in 5G networks to maximize long-term network utility, presenting a genetic algorithm-based online optimizer that achieves solid effectiveness, good robustness, and high scalability without requiring prior knowledge of traffic or utility models.
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.