Hierarchical Soft Slicing to Meet Multi-Dimensional QoS Demand in Cache-Enabled Vehicular Networks

TL;DR

This paper introduces a hierarchical soft slicing framework for cache-enabled vehicular networks, enabling resource sharing among slices to meet diverse QoS needs, with analytical and simulation validation showing significant throughput improvements.

Contribution

It proposes a novel service-oriented soft slicing framework that isolates resources per service while allowing opportunistic reuse, improving network efficiency for vehicular content services.

Findings

IS throughput increased by 30%

Content freshness and delay guarantees maintained

Analytical and simulation results validated the framework

Abstract

Vehicular networks are expected to support diverse content applications with multi-dimensional quality of service (QoS) requirements, which cannot be realized by the conventional one-fit-all network management method. In this paper, a service-oriented hierarchical soft slicing framework is proposed for the cache-enabled vehicular networks, where each slice supports one service and the resources are logically isolated but opportunistically reused to exploit the multiplexing gain. The performance of the proposed framework is studied in an analytical way considering two typical on-road content services, i.e., the time-critical driving related context information service (CIS) and the bandwidth-consuming infotainment service (IS). Two network slices are constructed to support the CIS and IS, respectively, where the resource is opportunistic reused at both intra- and inter-slice levels. Specifically, the throughput of the IS slice, the content freshness (i.e., age of information) and delay performances of the CIS slice are analyzed theoretically, whereby the multiplexing gain of soft slicing is obtained. Extensive simulations are conducted on the OMNeT++ and MATLAB platforms to validate the analytical results. Numerical results show that the proposed soft slicing method can enhance the IS throughput by 30% while guaranteeing the same level of CIS content freshness and service delay.