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 have to operate in an ever-changing and dynamic environment. Anticipating the dynamics of traffic flows on the roads is critical for these services and, therefore, it is of paramount importance to forecast how they will evolve over time. By predicting future events (such as traffic jams) and demands, vehicular services can take proactive actions to minimize Service Level Agreement (SLA) violations and reduce the risk of accidents. In this paper, we compare several techniques, including both traditional time-series and recent Machine Learning (ML)-based approaches, to forecast the traffic flow at different road segments in the city of Torino (Italy). Using the most accurate forecasting technique, we propose n-max algorithm as a forecast-based scaling algorithm for vertical scaling of edge resources, comparing its benefits against state-of-the-art solutions for three distinct Vehicle-to-Network (V2N) services. Results show that the proposed scaling algorithm outperforms the state-of-the-art, reducing Service Level Objective (SLO) violations for remote driving and hazard warning services.
Highlights
The 5th generation (5G) of mobile communications revisits the traditional design of cellular systems that focused on connectivity, towards the support of a wide variety of network services supporting a disparate set of requirements and capabilities in a shared physical infrastructure
4) Long Short-Term Memory (LSTM) [5]: LSTM is a special form of Recurrent Neural Network (RNN) that can learn long-term dependencies based on the information remembered in previous steps of the learning process
This paper provides an extensive analysis of state-of-the-art techniques to forecast the road traffic for the city of Torino, either based on traditional time-series methods or on MLbased techniques
Summary
The 5th generation (5G) of mobile communications revisits the traditional design of cellular systems that focused on connectivity, towards the support of a wide variety of network services supporting a disparate set of requirements and capabilities in a shared physical infrastructure. Service elasticity is required, adapting the system to workload changes in order to avoid any degradation of the service performance and violation of Service Level Agreements (SLAs) To this end, traditional scaling approaches include static. Martín-Pérez et al.: Dimensioning of V2N Services in 5G Networks through Forecast-based Scaling or reactive (e.g., threshold-based) solutions They are incapable of facing unforeseen events, especially when multiple services must coexist over the same infrastructure.
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