Dimensioning of V2X Services in 5G Networks through Forecast-based Scaling

TL;DR

This paper evaluates forecasting techniques for traffic flow prediction in 5G V2X services, proposing a forecast-based scaling algorithm that improves resource efficiency while maintaining low latency violations.

Contribution

It introduces a novel forecast-based scaling method for vehicular services in 5G, leveraging ML techniques to optimize resource allocation based on traffic predictions.

Findings

Resource savings of up to 5% achieved

Latency violations increased by less than 0.4%

Forecasting accuracy impacts scaling effectiveness

Abstract

With the increasing adoption of intelligent transportation systems and the upcoming era of autonomous vehicles, vehicular services (such as, remote driving, cooperative awareness, and hazard warning) will face an ever changing and dynamic environment. Traffic flows on the roads is a critical condition for these services and, therefore, it is of paramount importance to forecast how they will evolve over time. By knowing future events (such as, traffic jams), vehicular services can be dimensioned in an on-demand fashion in order to minimize Service Level Agreements (SLAs) violations, thus reducing the chances of car accidents. This research departs from an evaluation of traditional time-series techniques with recent Machine Learning (ML)-based solutions to forecast traffic flows in the roads of Torino (Italy). Given the accuracy of the selected forecasting techniques, a forecast-based scaling algorithm is proposed and evaluated over a set of dimensioning experiments of three distinct vehicular services with strict latency requirements. Results show that the proposed scaling algorithm enables resource savings of up to a 5% at the cost of incurring in an increase of less than 0.4% of latency violations.