# Slice as an Evolutionary Service: Genetic Optimization for Inter-Slice Resource Management in 5G Networks

arXiv: https://arxiv.org/abs/1802.04491 · HTML: https://mlsanity.com/paper/1802.04491/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2018.

## Built on by (most cited first, 2 of 2)

- [The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms](https://mlsanity.com/paper/2203.12315/) (2022)
- [AWaRe-SAC: Proactive Slice Admission Control under Weather-Induced Capacity Uncertainty](https://mlsanity.com/paper/2601.05978/) (2026)

## Abstract (arXiv)

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.