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

TL;DR

This paper introduces a genetic algorithm-based online optimizer for inter-slice resource management in 5G networks, enhancing efficiency, robustness, and scalability without prior traffic knowledge.

Contribution

It presents a novel evolutionary algorithm approach for real-time inter-slice resource optimization in 5G, supporting heterogeneous slices and dynamic traffic conditions.

Findings

Achieves near-optimal slicing strategies with high utility.

Demonstrates robustness in non-stationary scenarios.

Supports scalable and heterogeneous network slices.

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.