Stochastic Optimization and Control Framework for 5G Network Slicing with Effective Isolation

TL;DR

This paper introduces a stochastic control framework for 5G network slicing that ensures slice isolation, minimizes power, and guarantees low latency and reliability for diverse wireless services in dynamic environments.

Contribution

It proposes a novel Lyapunov drift-plus-penalty based control framework for effective resource management and isolation in 5G network slicing with time-varying user demands.

Findings

Maintains system reliability during network changes.

Ensures effective slice isolation in dynamic conditions.

Reduces power consumption while meeting QoS requirements.

Abstract

Network slicing is an emerging technique for providing resources to diverse wireless services with heterogeneous quality-of-service needs. However, beyond satisfying end-to-end requirements of network users, network slicing needs to also provide isolation between slices so as to prevent one slice's faults and congestion from affecting other slices. In this paper, the problem of network slicing is studied in the context of a wireless system having a time-varying number of users that require two types of slices: reliable low latency (RLL) and self-managed (capacity limited) slices. To address this problem, a novel control framework for stochastic optimization is proposed based on the Lyapunov drift-plus-penalty method. This new framework enables the system to minimize power, maintain slice isolation, and provide reliable and low latency end-to-end communication for RLL slices. Simulation results show that the proposed approach can maintain the system's reliability while providing effective slice isolation in the event of sudden changes in the network environment.